Tag: CISO

AI Is Entering the Physical World: Cybersecurity Must Change Now

AI Is Entering the Physical World: Cybersecurity Must Change Now
Image generated by ChatGPT, 2026

Part 2 of “AI Is entering the physical world”

Considering everything covered in part 1 of this article, it’s time to explore the importance and relevance of the adversarial mindset.

The Adversarial Mindset Must Extend to Perceived Reality

Security teams cannot limit questions to something like:

Does the AI accurately understand the environment?

Instead, they should ask:

How could I make the AI misunderstand the environment while leaving it confident that its understanding remains accurate?

That shift produces very different security exercises.

For example, the following questions start to gain power:

  • Can conflicting sensor states exist?
  • Can temporal relationships between events be manipulated?
  • Can a legitimate device report technically valid but physically impossible values?
  • Can one AI agent be made to trust information supplied by another compromised agent?
  • Can some AI technology be fooled into selecting a dangerous action that still appears rational?
  • Can several individually low-risk inputs be influenced such that their combined effect changes some system’s interpretation of reality?

This goes far beyond vulnerability scanning.

It requires an understanding of the target environment and the making of educated (adversary informed) attacking assumptions.

Identity Becomes Even More Important in the Physical World

Interestingly, physical AI also amplifies the importance of identity. This is important given that many of the older OT protocols have no notion of a user or identity. Think about that, in many cases older ecosystems would allow network traffic carrying commands that could create physical impact. Yet, no authenticated user was part of the equation. We must do better now.

Every participant in a physical environment needs an identity or an attributable source of authority. That includes people, sensors, robots, cameras, controllers, applications, models, workloads, autonomous agents, and external systems.

The AI must know more than what information arrived.

It needs context about where that information came from, which identity produced it, and whether the organization should trust that source.

Likewise, when some AI technology decides to act, the receiving system needs to understand the authority behind that action.

Organizations should preserve a hierarchical chain such as:

  • Human owner
  • AI system
  • Model decision
  • Agent identity
  • Delegated authority
  • Physical command
  • Machine action

When anything within that chain breaks, accountability breaks with it.

More importantly, security loses the ability to determine whether something with legitimate authority produced some physical action.

In IT, a compromised identity can expose information or disrupt systems.

In physical AI, a compromised identity may eventually move something.

Cybersecurity Must Now Protect State, Not Just Systems

As physical AI develops, cybersecurity architecture will need to focus increasingly on state. Some of the types of questions that need answers as they relate to state are:

  • What is true right now?
  • Which entities exist at the moment?
  • What are these entities doing?
  • Which relationships connect them?
  • What actions have already occurred?
  • Which future states remain plausible?
  • What authority exists to change the current state?

And critically:

How confident are we that the data describing current state is trustworthy?

Traditional alerts often examine individual events.

Physical AI security will need to understand sequences, relationships, causality, and physical context.

Some examples are:

  • A temperature reading of 190 degrees may be safe in one operating state and extremely dangerous in another.
  • A valve opening may be normal after one event and malicious after another.
  • A robot entering an area may present little risk until a person enters the same physical space.

In the physical domain, context determines risk.

Therefore, security platforms will need stronger temporal models, dynamic graphs, event streams, behavioral baselines, identity relationships, process awareness, and state prediction.

The goal cannot remain simply detecting what has already happened.

We need to understand what is happening, why it is happening, and what is likely to happen next.

This represents another important shift for cybersecurity.

Historically, security operations have been overwhelmingly reactive. An event occurs, a signal appears, an alert fires, and analysts investigate. The entire incident response industry exists because of this reactive model.

Physical AI will demand more predictive security.

If some AI technology controlling or influencing an environment can reason about what happens next, defenders must develop comparable capabilities to identify dangerous future states before systems can reach them.

The objective becomes more than detecting malicious activity.

It becomes preventing the environment from reaching an unsafe state.

Six Security Principles for Physical AI

This is not television, and it will be a bit before humanoid robots begin to appear throughout the enterprise. Cybersecurity leaders do not need to wait for that day before they start preparing. That preparation can begin now and here are six relevant suggestions:

1. Protect the Data That Defines Reality

Identify the data that physical AI systems use to understand their environment.

Establish provenance, integrity controls, behavioral baselines, cross-source validation, and clear ownership. This data needs to be protected as it will be the basis of important truths.

Furthermore, treat manipulation of physical-state data as a high-consequence security event.

We have spent decades protecting sensitive data from exposure. Physical AI requires equal attention to protecting data from malicious alteration.

2. Understand Semantics, Not Just Traffic

Do not stop at network visibility as that is simply not enough.

Understand what commands and values actually mean to consuming physical processes.

This was central to the data-centric approach I advocated in OT security years ago, and it becomes even more important when AI consumes that information to understand some environment.

Allowed communication and safe action are not synonymous.

3. Bind Identity to Physical Authority

Every human and non-human actor capable of influencing the physical environment needs an attributable identity, constrained authority, and accountable owner. We have to do better than the OT protocols of the past where no identity was bound to commands and changes flowing via network communications.

Organizations must know who or what caused every consequential action.

They also need to continuously evaluate whether that identity remains trustworthy.

4. Model the Blast Radius Before Granting Autonomy

Before giving an AI system authority to act, determine what happens if it makes the wrong decision. This requires proper testing, consideration of edge cases, and careful attention to the design and enforcement of boundaries.

Ask how far one incorrect action can propagate through interconnected machines, systems, and physical processes.

Then constrain autonomy accordingly.

The greater the physical consequence, the smaller the acceptable gap between authority and accountability.

5. Use Simulation as a Security Tool

Digital twins and simulated environments should do more than optimize operations or train models.

Security teams can use them to test adversarial scenarios, evaluate “what-if” conditions, attempt to predict attack paths, and observe potential physical consequences without endangering production environments.

However, teams must also secure the simulation itself.

If the digital twin becomes an input into training, planning, or decision-making, poisoned simulation data can eventually influence downstream real-world behavior.

6. Design for Safe Failure

Every physical AI system needs an independently enforceable path to a safe state.

Security teams should be able to dynamically revoke authority, isolate compromised components, reject untrusted data, switch to manual control, and stop physical action.

Most importantly, do not assume that the AI responsible for normal operation should also control its own emergency containment.

Leadership needs to Understand the Physical AI Transition

Boards and executive teams do not need to become experts in AI technologies or the designing of world-class architectures.

However, they do need to understand what happens when AI crosses the boundary between recommendation and action as that can have a direct impact on business operations.

Here are a few questions leadership should start asking:

  • Where can AI already influence physical processes in our organization?
  • Which AI systems can issue commands that trigger physical action?
  • Which data sources clearly define their understanding of physical state?
  • Can we establish the integrity and provenance of that data?
  • Can data manipulation create unsafe environments?
  • Which human and machine identities possess authority over systems that have physical capabilities?
  • Have we tested how the systems respond to intentionally manipulated environments?
  • What physical consequences could follow an incorrect decision?
  • Can we quickly force the environment into a safe state when trust disappears?

These are not robotics questions.

They are enterprise-risk and governance questions.

We Have Seen Part of This Future Before

World models and physical AI introduce powerful new technology.

Yet one of their central security problems brings me directly back to the OT environments we worked to protect at Bayshore Networks.

In 2020, I argued that protecting industrial environments required us to understand more than who communicated with whom.

We had to understand the actual values moving through industrial protocols.

Then we had to understand what would happen in the physical world when a PLC, controller, drive, or other system acted upon those values.

That progression was:

Data → Context → Command → Physical Consequence

World models extend that to:

Data → Perceived Reality → Predicted Future → Decision → Physical Consequence

That additional intelligence does not eliminate the old security problem.

It magnifies it.

World models will increasingly use huge volumes of data to construct representations of reality, predict future states, and select actions.

Therefore, cybersecurity must protect much more than the model.

We must protect the integrity of the world the model believes it inhabits.

That requires trusted data, attributable identities, semantic understanding, adversarial testing, state awareness, predictive security, and tight control over physical authority.

The GenAI era taught organizations that machines can create.

The agentic AI era is teaching us that machines can act.

World models and physical AI will force us to confront the next question:

What happens when machines can understand enough of the physical world to predict it, and possess enough authority to change it?

Cybersecurity leaders should start considering that question now.

Because AI is entering the physical world.

And once cyber risk becomes physical risk, we no longer get to treat a corrupted view of reality as merely a bad AI output.

AI Is Entering the Physical World: Cybersecurity Must Change Now

AI Is Entering the Physical World: Cybersecurity Must Change Now
Image generated by ChatGPT, 2026

Part 1 of “AI Is entering the physical world”

For the last several years, most organizations have experienced Artificial Intelligence through a screen. Come to think of it, so have many of the recently self-appointed AI experts. I consider most of these people users, not experts. Things are changing on levels these folks are not prepared for. AI Is entering the physical world. Why cybersecurity must change now.

Typical “AI” usage at the moment equates to Generative AI (GenAI). This means someone types a prompt and an engine generates content. The engine can write code, analyze a document, create an image, summarize data, or recommend an action.

That model of AI is already changing.

The next major evolution will push AI beyond understanding language and digital information. AI systems will increasingly model environments, predict how those environments may change, reason about physical objects, and take actions in the real world.

World models, embodied AI, robotics, autonomous systems, digital twins, and increasingly capable agentic ecosystems are moving us in that direction.

Consequently, cybersecurity leaders need to understand that this transition changes the security problem dramatically.

Potential Physical Impact

When AI exists primarily inside a digital environment, a bad decision may generate incorrect information, expose data, execute malicious code, or compromise a business process.

When AI can perceive and act upon the physical world, a bad decision can move a machine.

It can alter a manufacturing process or the behavior of a robot. It can influence an autonomous vehicle or manipulate an industrial control process.

Ultimately, it can create physical consequences.

That is why cybersecurity must change. Now.

Traditional cybersecurity primarily protects systems, identities, networks, applications, and information. Physical AI adds something fundamentally different. In most cases, foreign. Cybersecurity must now protect an AI system’s perception of physical reality, the data used to construct that reality, the authority to act upon it, and the resulting physical state.

An attacker may no longer need to compromise the AI model itself.

Manipulating the world the model sees may be enough.

That shifts cybersecurity beyond protecting systems and information. We must increasingly protect state, perception, prediction, authority, and physical consequence.

Physical AI changes cybersecurity because we must protect not only the AI, but the integrity of the world the AI believes it inhabits.

World Models Change What AI Understands

Large Language Models (LLMs) became powerful by learning relationships across enormous amounts of data.

World models pursue a different capability.

At a high level, a world model develops a representation of an environment and uses that representation to reason about how the environment may change over time.

Instead of merely asking, “What should come next in this sequence?” as LLMs do, world model based systems begins answering questions such as:

  • What exists in this environment?
  • How are these objects related?
  • What state are they currently in?
  • What happens if something moves or changes?
  • What will the environment probably look like next?
  • How will my actions affect this environment?
  • What action is necessary to reach a desired state?

This capability matters enormously for robotics and autonomous systems.

For example, a robot operating in a warehouse cannot simply identify a forklift. It needs to understand where the forklift is, is it currently being operated, how quickly it is moving, where it will probably go next, and what obstacles surround it.

Likewise, an industrial AI system cannot simply recognize that a valve exists. It may need to understand the valve’s current state, its relationship to pressure elsewhere in the process, what normally happens after the valve changes state, and which physical consequences could follow.

In other words, the AI must build and continuously update a representation of reality.

That representation becomes extraordinarily valuable.

It also becomes an extraordinarily attractive target.

Why Cybersecurity Must Change When AI Becomes Physical

Cybersecurity traditionally focuses on protecting identities, systems, applications, networks, APIs, and data.

Physical AI forces us to extend that thinking.

We now have to protect the system’s understanding of reality.

If an attacker manipulates the information an AI system uses to construct that reality, the attacker may never need to compromise the model itself. That is a dynamic the security industry has yet to contend with.

Consider an autonomous system that continuously processes sensor readings, environmental conditions, machine states, visual information, historical behavior, operator commands, and other telemetry.

The AI uses those inputs to determine what exists, what is happening, what will probably happen next, and what action it should take.

Now change one of those inputs.

Then change several.

Make the changes subtle enough that no individual result looks catastrophic.

An attacker can gradually create a false version of reality inside that target system. If the approach is slow and low the end result can be rather complex.

Along that journey, AI systems could make completely rational decisions based on completely corrupted context.

The model did not necessarily fail.

Its understanding of the world failed.

That distinction will become one of the defining problems in physical AI security.

I Wrote About This Problem Before World Models Entered the Conversation

This problem feels new to many because technology has changed and those people have likely not dealt with these types of environments.

But, to some of us the underlying security principle is not new at all.

In January 2020, while I was one of the original members and CTO at Bayshore Networks, I published an article in Network Security titled “Operational Technology Security – A Data Perspective.” (https://www.sciencedirect.com/science/article/abs/pii/S1353485820300088)

The central argument was straightforward: OT cybersecurity was concentrating too heavily on network-level visibility while overlooking something far more consequential – the actual values inside the data.

Knowing the following mattered:

  • That one IP address communicated with another.
  • Which network protocol was used.
  • That a particular workstation communicated with a Programmable Logic Controller (PLC).

However, none of those facts necessarily told us what happened to the physical process.

For that, we had to understand the data itself. We needed to understand the command, the register, the setpoint value.

Most importantly, we needed to understand what changing certain values would mean in the physical domain.

That was the data-centric security problem in OT. To an extent that is still a problem today.

An attacker did not necessarily need to break the network connection. The connection could remain completely legitimate.

An authenticated engineering workstation could communicate with an approved controller over an expected industrial protocol.

Yet if the attacker changed the right value inside that legitimate communication, the physical result could become dangerous.

In OT, the packet can be legitimate while the value inside it is hostile.

That concept drove much of the thinking behind the technology we built at Bayshore Networks.

We pushed inspection beyond basic network metadata and deeper into industrial protocols, transactions, commands, and values. We wanted security controls to understand what the industrial communication meant, not simply observe that the communication occurred.

Why?

Because data was not simply information.

Data could become physical action.

World Models Extend the Data-Centric OT Problem

This is where my earlier OT work and today’s world-model discussion converge.

The problem I described in 2020 focused on protecting data values because industrial systems could act upon those values with potential physical impact.

World models take that concept significantly further.

A physical AI system does not simply receive a single value and execute a command. Increasingly, it will consume enormous amounts of data to construct an internal representation of its environment.

It will correlate inputs, infer relationships, estimate current state, and predict future state.

Then it may select an action based on that representation.

Therefore, take the old OT question: what does this data value mean to the physical process? This now becomes an even more consequential AI security question: what reality is this data causing the AI to believe?

That is the intellectual bridge between data-centric OT security and physical AI security.

In the OT environments we protected years ago, manipulating a register or setpoint could change a physical process.

In a world-model-driven environment, manipulating enough trusted data could change the AI’s model of the entire process.

At that point AI itself may determine which action should follow.

This gives the adversary an entirely new level of leverage.

Data Becomes Part of the Physical Control Surface

Security leaders need to stop thinking about data solely as something they must prevent attackers from stealing.

In physical AI environments, adversaries may gain more value by changing data than by stealing it.

Consider the information that could feed a physical AI system:

  • Camera and video streams.
  • Temperature and pressure measurements.
  • Location and proximity data.
  • Motor position and velocity.
  • Machine state.
  • Human presence and movement.
  • Production telemetry.
  • Historical operating patterns.
  • Digital-twin state.
  • Maintenance information.
  • Identity and authorization context.
  • Commands from machines or other AI agents.

If some AI technology uses those inputs to determine current state, predict future states, and choose actions, the integrity of those data points becomes part of the physical control surface.

As a result, security teams must ask something much more sophisticated than:

Can this system access the sensor?

They must ask:

Should the system trust what that sensor is telling it right now?

That requires context. As such, questions such as these become very relevant:

  • Is this the expected device?
  • Has the configuration been modified?
  • Does value X make sense within the current operating state?
  • Did an authorized identity make some change?
  • Does the sequence of events match expected process behavior?
  • Could the data be syntactically valid but operationally impossible?

This is precisely where the lessons from my OT past become invaluable.

Seeing the communication is not enough.

Understanding the data is not enough.

Security must understand the data in the context of the physical process, and now in the context of some AI’s evolving model of that process.

A Hostile World Does Not Require a Compromised Model

Much of the current AI security conversation concentrates on attacking models.

We discuss prompt injection, jailbreaks, model theft, adversarial inputs, training-data poisoning, and manipulated outputs.

Obviously, those threats remain important.

However, physical AI creates another powerful adversarial strategy:

Do not attack the intelligence. Attack the world that the intelligence sees.

An adversary could target:

  • Perception – change what sensors, cameras, or other inputs report.
  • State – alter the data describing the current condition of a machine or environment.
  • History – corrupt the historical context the system uses to recognize normal behavior.
  • Identity – impersonate a trusted operator, sensor, machine, or workload.
  • Relationships – manipulate the system’s understanding of how physical entities depend upon one another.
  • Prediction – distort enough contextual information to influence the system’s expected future state.
  • Action – abuse the mechanism that translates AI decisions into physical commands.

This attack model should concern cybersecurity leaders because the attacker can work around the intelligence rather than directly against it.

Imagine an AI system correctly concluding:

Given everything I currently know about this environment, action X represents the safest response.

Now imagine that an adversary manipulated what the system knows.

The reasoning may remain sound, but the action can still become dangerous.

See part 2 of this write-up here.

AI Is Undeniably Weaponized Now. The Human Is the Adversary.

AI Is Undeniably Weaponized Now. The Human Is the Adversary.

Artificial Intelligence (AI) is undeniably weaponized now. But the human is still the adversary. AI changes the speed, scale, sophistication, and autonomy of cyberattacks, while in most AI-enabled attacks a human still defines the objective, determines the desired outcome, directs or delegates activity to the technology, and benefits from success.

AI has changed cybersecurity at extraordinary speed. Attackers now use AI as both a force multiplier and a capability multiplier. They can accelerate reconnaissance, generate and refine malware, build highly targeted phishing campaigns, impersonate executives, analyze enormous volumes of stolen data, discover relationships between data points, identify exploitable weaknesses, and increasingly execute sequences of actions through autonomous agents.

Yet those capabilities do not eliminate the human element. AI may execute the action. An agent may navigate the application. A model may create the campaign material. But behind most malicious AI activity, a human still defines the objective, decides what outcome matters, and benefits when the operation succeeds. Last I checked there wasn’t some AI technology cashing out some Bitcoin from a ransom and partying on a yacht.

Consequently, understanding The Adversarial Mindset matters more today than in the past.

Does AI Eliminate Human Intent From Cyberattacks?

No, AI does not eliminate human intent from cyberattacks. It can dramatically change how an attack is executed while a human adversary still defines the objective the technology is pursuing.

Too often, it feels like we talk about AI-powered attacks as though AI itself has suddenly become the adversary.

That framing can be misleading.

Consider the difference between traditional Generative AI (GenAI) and Agentic AI.

With traditional GenAI, the relationship remains relatively obvious. A human asks a model to do things such as identifying vulnerabilities, improving code, analyzing data, translating messages, performing research, or solving some other element of an operation.

The system provides the power. The human provides the objective.

Agentic AI creates more distance between those two elements.

Instead of asking AI to perform one task, a human can increasingly define an objective and allow an agent to determine how to accomplish it. The agent can browse websites, invoke tools, query data, make decisions, evaluate responses, select subsequent actions, and continue working toward a defined goal.

In other words, the human moves farther away from each individual action.

However, distance from execution does not automatically remove intent.

That distinction matters enormously for cybersecurity.

An attacker does not need to personally enumerate every endpoint, craft every request, write every line of malicious code, or send every social-engineering message to remain the adversary behind an operation.

AI gives that nefarious actor both abstraction and leverage.

Agentic AI gives that same human a certain level of delegation.

Neither automatically removes the human from the equation.

Who Is Acting When an AI Agent Accesses a Computer?

When an AI agent accesses a computer on a user’s behalf, the human user can remain the party performing the access. In the Ninth Circuit’s August 2026 Perplexity decision, the court treated the AI assistant as a tool and the human user as the party accessing Amazon’s systems for purposes of the federal Computer Fraud and Abuse Act (CFAA).

The dispute involved Perplexity’s Comet browser and its AI Assistant. Users could direct the Assistant to perform tasks on Amazon.com. Amazon argued that Perplexity’s technology accessed Amazon’s systems without authorization and sought relief under the CFAA, and its California counterpart (the Comprehensive Computer Data Access and Fraud Act – CDAFA).

The Ninth Circuit rejected Amazon’s theory at the preliminary-injunction stage.

More importantly, the court focused on a remarkably significant question:

Who actually accesses the computer?

On the record before it, the court concluded that the AI Assistant functioned as a tool. The court described the Assistant as a “tool, not a person for statutory purposes.” It then concluded that the user accessed Amazon’s computers while using the Assistant to carry out specific actions.

The decision marks the first federal appellate ruling addressing whether AI agents acting on behalf of users can legally access online platforms.

That distinction carries enormous significance beyond this particular dispute.

The court did not treat the AI agent as an independent legal actor simply because it could perform actions on behalf of a user. Instead, it looked through the technology to determine who actually performed the access for purposes of the statute.

At the same time, something important surfaced by way of a limitation.

The Ninth Circuit DID NOT create a sweeping legal doctrine that makes humans universally responsible for everything an AI system does. In fact, the opinion expressly states that it does not establish a new legal regime for agentic AI. The court limited its holding to the CFAA and CDAFA “access” issue, the technology at issue, and the factual record before it. Different facts, different levels of control, different laws, or different AI architectures could produce different outcomes.

Nevertheless, from a cybersecurity perspective, a much broader lesson remains powerful: technology can sit between a human and some action without rendering the human irrelevant (or innocent by default).

Should Security Programs Defend Against AI or the Adversary?

Security programs should defend against the adversary, not AI in isolation. AI mechanisms such as prompt injection, model poisoning, tool abuse, and MCP attacks matter, but they do not explain who wants to attack you, why they are targeting you, or how they will adapt.

The industry has become obsessed with AI security. Both RSAC and BlackHat this year showcased that obsession with great fanfare.

To answer the questions of who, why, and how, you need to understand the adversary, not just the technology at hand.

For example, imagine two attackers with access to exactly the same AI model and exactly the same agentic capabilities.

One is a teenager experimenting, testing boundaries.

The other operates inside an organized cybercriminal enterprise with millions of stolen identities, infostealer logs, credential collections, years of operational experience, and a clear understanding of how to monetize access.

The AI may be identical.

The threat is not.

The adversary behind the technology creates that difference.

Should Analysts and Frameworks Define a Security Program?

No, analysts and frameworks should not define a security program or its security strategy. They can inform both, but market intelligence about technologies, vendors, categories, and industry trends is not the same as understanding the adversary targeting your organization.

An industry analyst publishes some analysis. Vendors push categories. A maturity model emerges. Boards ask where the company sits relative to peers. CISOs then purchase technologies to fill perceived gaps. Eventually, the organization builds an architecture that looks remarkably similar to the architectures of dozens of other companies that consumed the same analyst research. And along the way end up with tons of tools whose true capabilities are not fully utilized.

To be clear, industry analysts provide value.

They can deliver market intelligence, technology comparisons, vendor analysis, spending benchmarks, maturity models, and useful observations about where the industry is heading.

However, organizations make a serious mistake when they use analyst research as the foundation of a security program.

Market intelligence is not adversary intelligence.

An analyst may understand the cybersecurity industry exceptionally well while possessing little firsthand understanding of the people trying to defeat your security program.

They may understand industry sectors, products, categories, vendors, differentiators and even what other CISOs are spending on.

Yet none of those things necessarily means they understand how a real adversary thinks.

More importantly, an adversary does not care whether your program aligns with an analyst’s reference architecture.

The adversary cares whether your defenses prevent the desired outcome.

Therefore, security leaders should never stop at this question:

What does the industry say a modern security program should contain?

They must also ask:

If I were a competent, cunning, determined attacker targeting this organization, how would I defeat what we have built?

What Blind Spot Do Many CISOs Have?

The blind spot many CISOs have is a limited understanding of the real adversaries their security programs are supposed to defeat. Managing risk, compliance, architecture, technology, and incident response is not the same as understanding how a determined adversary thinks, adapts, combines weaknesses, and pursues an objective.

I am not referring to understanding ethical hackers, penetration testers, or red-teamers. These professionals absolutely add value, but they ultimately operate within constraints established by rules of engagement.

I am talking about a real adversary with ill intent, whose motivations may be financial, ideological, geopolitical, personal, or simply opportunistic; and who feels no obligation to respect rules, scope, policy, business hours, budgets, architecture diagrams, or organizational boundaries.

That distinction matters.

A penetration tester typically asks whether something can be compromised within an agreed scope.

An adversary asks a very different question:

How do I achieve my objective despite everything this organization has done to stop me?

That question requires a fundamentally different way of thinking.

Why Should Defenders Start With the Human Behind the Machine?

Defenders should start with the human behind the machine because AI amplifies adversarial capability without automatically replacing adversarial intent. Less sophisticated attackers can now access capabilities that once required specialists, while sophisticated adversaries can operate faster, analyze more data, uncover hidden relationships, and adapt more efficiently.

As an example, consider that AI technologies create conditions in which enormous quantities of stolen identity data can be ingested and analyzed, revealing relationships humans would otherwise miss.

It can perform actions such as:

  • transforming OSINT into targeted and strategic intelligence
  • generating individualized social-engineering content based on attackable profiles across thousands of targets
  • creating strategic campaigns rapidly
  • refining malicious code
  • analyzing defensive responses and adaptively creating alternatives

But it does so at the request of some human element. Consequently, we should stop thinking only in terms of “AI attacks.” What we increasingly face are human adversaries with machine-scale leverage.

That represents a much more consequential problem.

How Does The Adversarial Mindset Change Security Strategy?

The Adversarial Mindset changes security strategy by making the adversary, not the framework, product, analyst, or compliance requirement, the starting point. Security leaders first ask what an adversary wants, what that adversary already knows, which assumptions and relationships can be exploited, and how the attacker will adapt when defenses interfere.

Ask questions like:

  • Who would want what we possess?
  • What exactly would they want?
  • What information about our people, systems, suppliers, executives, and customers do they already possibly have?
  • Which assumptions are we making that they would immediately challenge?
  • Where do identities, relationships, privileges, and trust create nefarious opportunities?
  • How could they combine several individually minor weaknesses into one viable attack path?
  • How would they adapt after encountering resistance to their techniques?
  • How could AI make each of those steps of adaptability cheaper, faster, or more precise?

At that point, you begin designing security from the adversary backward.

That is the essence of The Adversarial Mindset.

Moreover, this approach does not require organizations to abandon frameworks, compliance obligations, analyst research, or established security architectures. Those tools still serve important purposes.

However, they should support your security strategy rather than define it.

The adversary should help define it.

Why Does AI Make The Adversarial Mindset More Important?

AI makes The Adversarial Mindset more important because it gives human adversaries greater speed, scale, precision, leverage, and increasingly autonomous execution. Security teams therefore need to understand not only what AI can do, but what a motivated adversary can now accomplish because those capabilities exist.

The cybersecurity industry will inevitably spend enormous amounts of time debating how autonomous AI will become.

That discussion absolutely matters.

Eventually, increasingly autonomous systems may force us to confront genuinely difficult questions about intent, accountability, responsibility, control, and attribution.

However, that conversation leaves gaps. Organizations cannot afford to wait for those philosophical and legal questions to reach resolution. This is especially so for larger organizations that are not exactly agile.

Today, humans are discovering what AI can do for them.

Some of those humans are defenders, others are researchers, and still others are innovators.

Realistically, some are adversaries.

The last group does not care whether your AI strategy appears in an analyst report. They do not care which security technologies occupy a leader quadrant, or how mature your program looks against some industry benchmark.

They care whether they can accomplish their objective.

The Ninth Circuit’s Perplexity decision gives us an important legal manifestation of a broader technological reality: an intelligent tool can become increasingly capable while still operating in service of human direction.

Therefore, defenders should resist the temptation to focus exclusively on the technology.

That distinction changes the questions security leaders should be asking.

“What can AI do?”

This has to start migrating towards something like:

“What can an adversary now do because AI exists?”

Those are very different questions.

Ultimately, the second question is the one our security programs need to answer. AI is undeniably weaponized now. The human is the adversary.

Note: This article discusses the cybersecurity implications of Amazon.com Services, LLC v. Perplexity AI, Inc. and does not provide legal advice. The Ninth Circuit’s August 4, 2026 decision concerned a preliminary injunction and a specific interpretation of “access” under the CFAA and CDAFA based on the record before the court.

Reasons AI Governance Fails Without The Adversarial Mindset

AI Governance Fails Without The Adversarial Mindset
Image generated by Jetpack AI, 2026, via WordPress

I genuinely believe that most AI governance programs begin with good intentions. But that doesn’t instantly equate to a successful program. Often, AI governance fails without the adversarial mindset.

These types of governance programs typically define acceptable use, establish review committees, classify risk, document models, assign owners, and publish principles around fairness, privacy, transparency, and human oversight.

Undoubtedly, those activities matter.

Those programs also tend to assume that people, systems, data, and models will operate within the boundaries the organization designed.

An adversary makes no such assumption. Moreover, the boundaries organizations have designed mean nothing to an adversary.

Attackers will generally search for paths of least resistance that lead to success. This includes ways to manipulate inputs, compromise identities, poison data, exploit integrations, misuse legitimate capabilities, and confuse and manipulate humans.

Employees, contractors, customers, partners, activists, fraudsters, competitors, and nation-state actors may all test the distance between what an AI system was intended to do and what it can be manipulated to do.

Governance that considers only intended behavior is policy.

Governance that anticipates intentional manipulation becomes resilience.

AI governance without an adversarial mindset documents how a system should behave. It does not prepare the organization for how the system can be made to behave.

The Adversary Has a Vote

Executives often discuss AI risk as though the organization controls all relevant variables.

Leaders choose the model. Engineers establish the architecture. Data teams manage information. Security implements controls. Legal writes policy. Users receive training.

Then the system enters the real world.

Customers provide unexpected inputs. Employees find shortcuts. Vendors go out of business. Models drift. Credentials become exposed. Attackers study architectures and systems. Data sources become contaminated.

Ultimately, the adversary gets a vote in how the system operates.

This principle has shaped cybersecurity for decades. A secure architecture cannot assume that users will follow instructions, data will remain trustworthy, or that controls will continuously operate exactly as designed.

AI governance must adopt a similar reality.

MITRE ATLAS (https://atlas.mitre.org/) documents tactics and techniques used against predictive, generative, and agentic AI systems. NIST has developed a taxonomy for adversarial machine learning (https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-2e2025.pdf). The NCSC’s secure AI guidance (https://www.ncsc.gov.uk/collection/guidelines-secure-ai-system-development/guidelines/secure-design) emphasizes threat modeling across design, development, deployment, and operation.

Those movements create a message that is consistent: AI risk cannot be governed solely through compliance reviews and intended-use documentation.

AI Governance Must Cover More Than Model Failure

Executives commonly focus on whether an AI system will produce an incorrect answer.

Yet that is only one failure mode.

Interestingly, an AI system may produce an accurate answer for the wrong person. It may also follow a valid command issued through a compromised identity or expose sensitive information while correctly completing a task. Sound recommendations may be generated based on poisoned data.

The model may operate exactly as designed while the larger system fails.

This distinction matters because AI is not merely a model.

It is an architecture of identities, data, software, infrastructure, integrations, humans, workflows, and delegated authority.

An adversary does not need to attack the most sophisticated component. The adversary will attack the component that produces the greatest advantage or outcome for the least effort.

That may very well be the model itself.

But, it may also be an exposed API key, overprivileged service account, manipulated document, compromised developer, insecure plugin, careless employee, or trusted third-party data source.

An Adversarial Mindset Is Not Merely Pessimism

Some leaders resist adversarial thinking because it can sound negative or obstructive. Others claim that doing so empowers and/or validates the adversary.

Those notions misunderstand the purpose of the adversarial mindset.

An adversarial mindset does not assume that every person is malicious or that every AI initiative will fail. It assumes that valuable systems will attract manipulation and that unintended behavior becomes more likely as complexity, scope, and authority increase.

It asks the organization to examine its assumptions before an attacker does.

This mindset challenges statements and/or beliefs such as:

  • Only employees can access the system.
  • The model does not have access to sensitive data.
  • A human reviews every important decision.
  • The agent can only use approved tools.
  • The training data comes from trusted sources.
  • We have a kill switch, and it works.

Each statement may be technically accurate while concealing dangerous assumptions. As such, further probing may look like:

  • Which employees?
  • Through which identities?
  • Does the human reviewer understand enough to challenge the model or system?
  • Can approved tools be combined to create an unapproved outcome?
  • Who determines whether a source remains trustworthy?
  • How long does shutdown actually take?

The adversarial mindset turns reassuring claims into testable questions.

Seven Questions Leaders Should Ask

1. How Could Someone Intentionally Misuse This System?

Governance reviews often begin with the approved business use case.

Adversarial governance begins with the abuse case.

Leaders should scrutinize the angles. Ask how an employee, customer, contractor, criminal, or competitor could use some legitimate capability for a different objective.

A customer-service assistant might help employees retrieve account information. Could it also help a malicious insider assemble customer profiles?

A fraud model might identify suspicious payments. Could someone probe its thresholds and learn how to avoid detection?

A security agent might isolate compromised systems. Could an attacker manipulate it into disrupting legitimate operations?

Security teams should create abuse stories alongside legitimate user stories. Every material use case should identify who may benefit from subverting it and which capabilities they would seek out in pursuit of that benefit.

2. What Happens When the Data Becomes Hostile?

Organizations tend to view data as an input.

Adversaries may use it as code, instructions, a weapon, or a persistence mechanism.

Manipulated training data can alter future model behavior. Poisoned reference material can corrupt retrieval-augmented systems. Malicious instructions embedded in emails, websites, documents, or images can influence agents that consume external content.

The source may appear trusted while the content is not.

Leaders should ask:

  • Which data sources can influence the system?
  • Who can change those sources?
  • How does the organization establish provenance?
  • Can the system separate data from instructions?
  • What happens when sources conflict?
  • How quickly can poisoned information be detected and removed?

Data governance must assess not only quality and privacy but also hostility.

3. Which Identity Creates the Greatest Blast Radius?

Eventually, many attacks turn out to have some intersection with identity.

A compromised developer can change code. A stolen service credential can invoke functionality. An overprivileged agent can call dangerous tools. An administrator can modify thresholds or do things like suppress logs.

Leaders should identify the human and non-human identities capable of influencing high-consequence AI systems.

They should understand:

  • Who can alter data, especially data used for training models.
  • Who can change policies or guardrails.
  • Who can deploy or replace models.
  • Which agents can invoke production tools.
  • Who can disable monitoring.
  • Which identities can approve their own changes.

The most dangerous identity may not possess the most obvious administrative title. It may be an automation account that quietly connects elements such as models, data, and production systems.

4. Can the Human in the Loop Be Manipulated?

Organizations frequently rely on “human in the loop” as their final safeguard.

Adversarial thinking asks whether the human loop actually works.

People may over-trust AI recommendations, especially when a system appears confident or technically sophisticated. Reviewers may approve outputs automatically because of enormous volumes of data to review, time pressure, weak interfaces, inadequate context, or fear of challenging a system the organization has heavily promoted.

An attacker may also target the reviewer, a human, directly. After all, whether purposely or not, humans are the source of many unfortunate cyber events.

Manipulated systems could present targeted evidence, conceal uncertainty, overwhelm the reviewer with volume, or frame the decision in a way that encourages the desired behavior (of the nefarious actor).

Meaningful human oversight requires authority, context, time, training, and psychological permission to disagree.

A person who can technically click “reject” but is organizationally discouraged from doing so is not an effective control.

5. What Happens When Trusted Components Become Untrustworthy?

AI systems depend on external data, models, open-source libraries, cloud platforms, plugins, APIs, and vendors.

Each dependency extends the trust boundary.

A provider may change a model. An integration may gain new capabilities, or lose existing ones. A library may become compromised. A data source may introduce manipulated content. A vendor may retain more information than expected.

Third-party risk assessments often occur before deployment and then fade into GRC oblivion.

Adversarial governance treats trust as temporary.

Leaders should know which external components can influence decisions or actions, what changes providers are making, how the organization detects those changes, and whether it can continue operating safely when the dependency becomes unavailable or untrustworthy. These are elements that make up a resilient ecosystem.

6. How Would We Detect a Quiet Failure?

Not every AI incident will produce blinking lights, loud alarms, an obvious outage or some catastrophic result.

Some of the most damaging failures will appear gradually.

A model may become slightly less accurate for a specific population. An agent may begin retrieving more data than it needs. A recommendation system may slowly favor manipulated content. A fraud model may become less sensitive to a criminal technique.

The system continues to operate, and traditional availability metrics remain healthy.

Leaders need indicators that reveal changes in context, behavior, authority, data access, confidence, exception rates, and human impact.

They should also monitor near misses. An action stopped by a human or compensating control still reveals a weakness in the system.

Simply put, governance cannot measure only uptime and adoption. It must measure whether the system remains within its intended behavioral boundaries.

7. Can We Contain the System Before We Understand the Incident?

Executives often assume that teams can shut down an AI system when something goes wrong.

That assumption should be tested.

A system may be embedded in business workflows. Multiple applications may depend on this integration. Agents may retain active sessions or credentials. Third-party components may continue processing information. Teams may hesitate because shutting some system down could create operational consequences.

Incident response requires the ability to reduce authority quickly, even before investigators understand the complete failure.

Organizations should be able to:

  • Revoke agent and service-account access.
  • Disable specific tools or integrations.
  • Quarantine suspicious data sources.
  • Roll back models and configurations.
  • Move automated decisions into manual review.
  • Preserve evidence for investigation.
  • Continue critical operations in a degraded mode.

The ability to stop an AI system safely needs to be a design requirement, not an emergency improvisation.

Turn Governance Into an Adversarial Operating Model

An adversarial mindset must produce more than provocative questions. When pursued properly this mindset should guide and mold entire security programs.

Organizations should embed this mindset into their operating model.

That includes:

  • Threat modeling – examine the complete AI architecture, not only the model.
  • Abuse-case development – document how legitimate capabilities could support illegitimate objectives.
  • Red teaming – without boundaries (attackers have none) test models, identities, integrations, users, and workflows.
  • Control validation – under realistic conditions, prove that guardrails work rather than accepting that they exist.
  • Behavioral monitoring – actively detect changes in data access, authority, tool use, and outcomes.
  • Incident exercises – regularly rehearse containment, rollback, investigation, communication, and recovery. This can follow the standard tabletop model even though many of those exercises introduce boundaries and constraints that take them out of the realm of realistic conditions.
  • Continuous reassessment – review risk whenever the system’s data, model, tools, users, or authority change.

Governance should operate as a feedback loop:

Assume → Challenge → Test → Observe → Adapt

Typically, a policy changes only when someone updates the document. An adversarial governance program needs to change when evidence reveals that an assumption no longer holds.

Policy Describes the Organization You Hope Exists

AI governance policies describe approved behavior. They define responsibilities, expectations, controls, and boundaries.

These definitions are necessary.

Sadly, they are not the same as actual readiness.

The real organization includes shortcuts, legacy access, human bias, compromised credentials, conflicting incentives, third-party dependencies, weak integrations, and determined adversaries.

An adversarial mindset closes the distance between the organization described by policy and the one that actually operates under pressure.

Leaders must ask more than whether AI is accurate, compliant, or useful.

They must ask how someone could manipulate it, misuse it, impersonate a trusted identity, corrupt its data, exploit its authority, or influence the humans responsible for oversight.

AI governance without an adversarial mindset is just policy.

Policy defines the rules.

The adversarial mindset determines whether those rules survive contact with reality.

Fuzed Identities and the Collapse of the Corporate-Personal Security Boundary

Fuzed Identities and the Collapse of the Corporate-Personal Security Boundary
Image generated by Jetpack AI, 2026, via WordPress

The collapse of the corporate-personal security boundary is upon us. Cybersecurity has spent decades protecting accounts while adversaries have concentrated on compromising people.

That distinction is becoming increasingly consequential.

Enterprise identity systems divide a person into manageable objects: an employee record, directory account, email address, mobile number, device registration, cloud identity, privileged account, and application entitlement. Each object is assigned to a platform, protected by controls, and evaluated within a defined organizational boundary.

The individual behind those objects does not live within that boundary.

The same person maintains multiple personal email accounts, mobile devices, usernames, social profiles, messaging accounts, browser sessions, payment identifiers, cloud applications, and years of accumulated digital exposure. Those identities intersect with the enterprise whenever the person opens a work document on a personal device, reuses a password, accesses work from a personal device, stores a business session in a browser, uses a personal telephone number for recovery, or becomes the target of an infostealer.

For the adversary, there is no meaningful distinction between an employee’s corporate identity and personal identities. There is simply a collection of possible routes to the same human target.

This reality requires a new security construct: the fuzed identity.

From record matching to attributable identity

A fuzed identity is a holistic identity view created when attribution links seemingly disparate identifiers and establishes, to an appropriate confidence threshold, that they belong to the same individual.

This is more sophisticated than mere data aggregation or de-duplication.

Two records should not be linked merely because they contain similar names. Names are shared, addresses change, telephone numbers are reassigned, and identity data can be deliberately manipulated.

Reliable identity fuzing combines multiple forms of evidence. These can include historical email address relationships, telephone numbers, device characteristics, usernames, infrastructure, behavioral patterns, credentials, temporal consistency, location signals, payment artifacts, social accounts, breach records, and other attributable data.

Each relationship must retain its source, age, relevance, and confidence. Historical context can play a very powerful role when performing attribution.

The result is not simply a larger identity profile. It is an evidentiary model that explains why multiple digital artifacts are believed to represent the same person.

This distinction becomes especially important when identity intelligence influences access, fraud prevention, investigations, or workforce security. Attribution must be explainable, contestable, and proportionate to the action being considered.

The account-centric blind spot

Identity and Access Management (IAM) systems perform a necessary but limited function. They determine whether an account can authenticate and whether it is authorized to perform a requested action.

They do not necessarily determine who is truly behind a given account.

Even strong authentication can prove only that an actor possesses an accepted authenticator. It does not always prove that the actor is the person the organization intended to provide access to.

A stolen session token, coerced authentication, compromised recovery channel, fraudulent enrollment, or synthetic persona can satisfy technical controls while defeating the business purpose of those controls.

NIST’s 2025 revision of its Digital Identity Guidelines (https://pages.nist.gov/800-63-4/) recognizes this changing environment. SP 800-63-4 expands fraud-related identity-proofing guidance, recommends continuous evaluation metrics, and introduces controls addressing forged media and injection attacks. These additions reflect a fundamental change: identity assurance cannot be reduced to checking documents during enrollment and validating credentials at login.

Identity must be evaluated as an evolving risk condition.

A user who was legitimate at enrollment may later have credentials stolen. A secure corporate endpoint may coexist with an infected home computer. A trusted contractor may accumulate new external exposure. A valid employee account may be operated by an entirely different person.

An account-centric model can miss these changes because the account itself remains valid.

When the personal device becomes enterprise infrastructure

Hybrid work erased many of the physical distinctions between personal and enterprise technology. Infostealers are now exploiting the remaining logical distinctions.

Microsoft Threat Intelligence reported in June 2026 that infostealer infections often occur outside managed enterprise networks, including on employees’ home computers where corporate monitoring is absent (https://www.microsoft.com/en-us/security/blog/2026/06/24/stealc-and-amadey-breaking-down-infostealers-and-the-cybercrime-services-that-deliver-them/). These infections may collect legitimate credentials and active session cookies, allowing attackers to authenticate as the victim and potentially bypass Multi-Factor Authentication (MFA).

StealC, one of the malware families analyzed by Microsoft, collects credentials and cookies from browsers while also targeting email clients, messaging applications, cryptocurrency wallets, gaming platforms, screenshots, and other data.

From the malware’s perspective, all accessible identities are part of one collection event.

An employee may think of a browser as personal because it runs on a home computer. The browser may nevertheless contain:

  • A corporate Microsoft 365 session.
  • A customer relationship management login.
  • A personal email account used for recovery.
  • Social media credentials.
  • Personal financial information.
  • Messaging sessions.
  • Autofill data containing addresses and telephone numbers.

An infection of this type creates a package representing the person across multiple contexts.

The criminal buying or processing that package can search for the most valuable route. The initial compromise might be personal, but the monetization can be corporate.

Verizon’s 2025 credential research (https://www.verizon.com/business/resources/articles/credential-stuffing-attacks-2025-dbir-research/) found that compromised credentials were involved as an initial access vector in 22 percent of the breaches reviewed. Verizon also found that, for the median user appearing in its infostealer dataset, only 49 percent of passwords across services were distinct.

Password reuse is only one linkage. Shared recovery addresses, telephone numbers, browser cookies, device fingerprints, usernames, and behavioral patterns can create others.

The enterprise therefore cannot evaluate identity exposure solely by searching for its corporate domain. It must understand identities connected to the person operating under that domain.

Re-framing employee identity risk

Fuzed identity intelligence does not mean a default classification of certain employees as inherently risky.

It means identifying security exposure associated with the employee’s connected, holistic digital identity.

This distinction is important. A person may be an entirely trustworthy employee while still presenting elevated technical risk because their credentials, sessions, devices, or personal identifiers have been compromised.

A fuzed identity model could allow an enterprise to determine that:

  • A breached personal email account belongs to a privileged administrator.
  • An infected personal device contained corporate authentication material.
  • An executive’s mobile number is being used in impersonation campaigns.
  • Several employee accounts share an exposed recovery mechanism.
  • A contractor’s identity is connected to multiple suspicious applications.
  • A departed employee retains active identities under alternate addresses.
  • An apparent login anomaly matches a broader pattern of identity compromise.

This additional context improves prioritization.

A ten-year-old password exposure involving a deactivated consumer account should not receive the same response as a fresh malware log containing an administrator’s corporate session cookie. But that ten-year-old password may be a key attribute against modern data based on the behavior of some humans.

The value of fuzed identity is not that it produces more alerts. Its value is that it distinguishes meaningful identity risk from noise.

Synthetic identity as an enterprise threat

Synthetic identity is frequently discussed as a financial fraud problem, but the underlying method extends directly into enterprise security.

The Federal Reserve describes a synthetic identity as one constructed by combining real and fabricated information (https://fedpaymentsimprovement.org/wp-content/uploads/frs-synthetic-identity-payments-fraud-white-paper-july-2019.pdf). A legitimate Social Security Number (SSN) might be paired with a fictional name, address, or date of birth. Because some components are authentic, the resulting identity can evade conventional verification and credit-screening processes.

Cybercriminals can apply the same architecture outside financial services.

A synthetic employment identity might combine:

  • A stolen person’s name or identity document.
  • An AI-generated profile photograph.
  • A fabricated employment history.
  • A legitimate residential address supplied by an accomplice.
  • A US-based telephone number.
  • A social profile created months earlier.
  • A domestic bank or payment account.
  • A remotely accessible corporate laptop.
  • Technical work performed by someone in another country.

Every component contributes to the appearance of legitimacy.

An identity-proofing process that validates each component independently may approve the applicant. A fuzed identity process examines whether the components form a coherent person.

It looks for evidence that several applicants use the same devices, infrastructure, telephone numbers, payment destinations, writing patterns, facilitators, or historical identities. It also identifies evidence that the supposed individual has a contradictory or impossible digital history.

The synthetic insider

The North Korean remote IT worker schemes demonstrate what happens when synthetic and stolen identities move beyond account fraud and become enterprise infiltration mechanisms.

In April 2026, the US Department of Justice announced the sentencing of two US facilitators involved in a scheme that placed North Korean IT workers at more than 100 US companies (https://www.justice.gov/opa/pr/two-us-nationals-sentenced-facilitating-fraudulent-remote-information-technology-worker-0). The operation compromised the identities of more than 80 US persons, generated over $5 million in revenue, and caused victim organizations at least $3 million in legal, remediation, and related costs.

The workers were not merely creating fraudulent accounts. They were becoming employees.

The operation used stolen identities, alias email addresses, social media profiles, job-site accounts, cross-border payment systems, shell companies, proxy computers, US-based facilitators, and laptop farms. Some workers obtained access to sensitive employer data, source code, and export-controlled information.

This is more accurately understood as a synthetic insider operation.

Traditional insider-risk programs generally begin after a person has joined the organization. They monitor the behavior of someone the enterprise believes it has already identified.

A synthetic insider defeats that assumption.

The organization monitors the account and device behavior of an employee who does not actually exist in the form presented during hiring. The person communicating with management may differ from the person operating the computer. Several supposed employees may even be controlled by the same network of actors.

Fuzed identity intelligence shifts part of insider-risk analysis to exist earlier in the lifecycle. It helps evaluate whether the applicant, contractor, employee, account operator, device user, and payment recipient appear to be the same attributable person.

Collapsing criminal aliases

The same capability can support cybercrime investigations.

Threat actors compartmentalize their operations. One person may use separate identities for malware development, forum participation, initial-access sales, cryptocurrency payments, infrastructure registration, social engineering, and communication with victims.

The objective is to prevent any one persona from revealing the whole operation.

Identity attribution undermines that compartmentalization.

A fuzed identity graph may establish that:

  • Two forum aliases use email addresses exposed with the same password.
  • Several messaging accounts were accessed from the same device.
  • A cryptocurrency wallet connects previously unrelated personas.
  • An infrastructure registration uses a historical personal email address.
  • A threat actor’s writing patterns persist across aliases.
  • Multiple synthetic employees share a facilitator or payment endpoint.
  • A supposedly new actor reuses identifiers from an older campaign.

A single relationship rarely proves common ownership. Multiple independent relationships may.

The analytical objective is to move from possibility to corroboration. Every link should contribute to a cumulative confidence assessment, and analysts should distinguish confirmed attribution from assessed attribution.

When that discipline is applied, an adversary’s collection of disposable identities becomes a map back to the underlying actor or actor cluster.

Moving from identity management to identity intelligence

Fuzed identities are by no means replacements for IAM, identity governance, Privileged Access Management (PAM), Security Information and Event Management (SIEM), endpoint detection, fraud prevention, or threat intelligence.

They should improve the decisions those systems make.

An identity intelligence layer can provide context at several points:

Enrollment and hiring

Detect identity inconsistencies, duplicate applicants, synthetic personas, reused infrastructure, and relationships to known facilitators.

Authentication

Adjust authentication requirements based on current external exposure, device risk, session compromise, and identity confidence.

Privileged access

Prioritize monitoring and verification when a highly privileged individual’s connected identity shows evidence of recent compromise.

Security operations

Connect apparently unrelated alerts involving different emails, usernames, devices, or accounts to one affected person.

Incident response

Determine the full scope of identity compromise rather than resetting only the credential named in the original alert.

Threat intelligence

Link aliases, infrastructure, wallets, accounts, behavioral signals, and historical data into attributable actor clusters.

Third-party risk

Understand whether contractors and supplier identities create exposure that conventional vendor-level assessments do not reveal.

The operating principle is straightforward:

Evaluate identity risk at the level at which the adversary exploits it, the person, not merely an account.

Confidence, provenance, and time

Identity fuzing can create serious consequences if poorly implemented.

Three elements are essential to operate with legitimacy.

  • Every identity relationship needs a confidence level – a shared name may be a weak signal. A shared device, unique telephone number, credential history, and consistent behavioral pattern may provide much stronger corroboration.
  • Every relationship needs provenance – analysts and decision-makers must know where the data came from, how it was collected, when it was observed, and whether it has been independently verified.
  • Identity intelligence must account for time and history – addresses change. Telephone numbers are reassigned. Credentials become obsolete. Devices are transferred. Criminals deliberately seed misleading data. Data, or close derivatives, used 15 years ago can pop up today,

A connection that was accurate five years ago may not describe the current identity. On the flip side a human can slip up and use a username or moniker today that they used 10 years ago. They just thought the world forgot about it.

A fuzed identity is therefore not a permanent static profile. It is a versioned assessment that evolves as evidence evolves.

The necessary privacy boundary

The disappearance of the technical boundary between personal and corporate identity does not eliminate ethical, legal, or privacy boundaries.

This point must be explicit.

A fuzed identity program should focus on security-relevant exposure, not personal curiosity. Organizations should establish a legitimate purpose, use validated sources of data, restrict internal access, define retention periods, maintain auditability, and provide human review before consequential actions.

Organizations should not infer risk from lawful personal beliefs, associations, medical information, family relationships, political activity, or other protected or irrelevant characteristics.

Nor should an external identity signal automatically produce disciplinary or employment action.

The appropriate response to a compromised personal device may be to revoke enterprise sessions and help the employee remediate the exposure. Instantly treating the employee as an adversary would be both unfair and counterproductive.

The objective is to protect the person and the enterprise from a shared threat surface.

A useful governance principle is: the threat boundary has disappeared, but the privacy boundary must remain.

The future identity control plane

The next generation of identity security will need to answer more than whether an account successfully authenticated.

It will need to determine:

  • Who is actually behind the account?
  • What other identities are connected to that person?
  • Has any connected identity recently been compromised?
  • Are several accounts actually operated by one actor?
  • Does current evidence justify changing access or verification requirements?
  • Can the organization explain and defend its attribution?

These questions turn identity into an intelligence discipline.

The enterprise directory will remain important, but it will no longer provide a sufficient representation of the workforce. Authentication will remain essential, but it will not always establish the real operator. Behavioral monitoring will remain valuable, but it must be interpreted in the context of the person’s broader identity exposure.

Adversaries already construct these holistic views. They correlate personal and professional accounts, search breach data, acquire infostealer logs, map relationships, and identify the shortest path from a human being to an enterprise asset.

Fuzed identity gives defenders the opportunity to operate with equivalent context, subject to stronger evidentiary, privacy, and governance standards.

Identity fragmentation has protected cybercriminals and obscured enterprise risk for too long.

The strategic opportunity is to turn those fragments into attributable intelligence by leveraging the collapse of the corporate-personal security boundary.

How to Lead With Confidence When Certainty Disappears

How to Lead With Confidence When Certainty Disappears
Image generated by Jetpack AI, 2026, via WordPress

For much of my career, I lived in worlds where precision mattered. The hardest shift from a technology or security leader to CEO is trading certainty for judgment. In this executive leadership world a key ability is being able to lead with confidence when certainty disappears.

From Precision to Ambiguity

As a technologist, architect, CTO, and CISO, I was trained to look for edge cases, root causes, system behavior, technical truths, and to have defensible answers. When something failed, the goal was to understand why as soon as the disaster was dealt with. When risk surfaced, the goal was to measure it, contain it, and communicate it. When a system needed to scale, the goal was to design something resilient enough to survive negative impact.

That background is incredibly valuable as it sharpens how you think. It also teaches you to respect complexity while separating signal from noise. That operating system also gives you a deep appreciation for how fragile things can become when assumptions go untested.

The Real Shift: Certainty to Judgment

Becoming a CEO requires a different operating system.

The hardest shift is not going from technology to business. It is going from certainty to judgment.

In technical leadership, you often have the luxury of eventually getting to a correct answer. The system works or it does not. There is a reality to a functional state. A control is effective or it isn’t. An architecture scales or it breaks. Vulnerabilities are exploitable or they aren’t. Even when there is debate, there is usually a path toward some solution.

As CEO, the path is rarely that clean.

Leading When the Answer Is Not Obvious

Unfortunately, CEOs have to make decisions with incomplete information. That is simply part of the job’s reality. You balance financial realities, customer needs, market timing, employee morale, board expectations, competitive pressure, and operational constraints. There is a mental state where you are constantly choosing between options that all carry risk. Sometimes the decision is not between right and wrong. It is between imperfect and necessary.

That is a very different kind of pressure.

Risk Is Only One Part of the Equation

A CISO is often rewarded for identifying what could go wrong. A CEO is responsible for deciding what must go forward irrespective of risk.

That does not mean ignoring risk. It means understanding that risk is only one part of the enterprise equation. Growth has risk. Inaction has risk. Delay has risk. Over-analysis has risk. Moving too slowly can be just as damaging as moving too fast.

This was one of the most important mindset changes for me.

As a security leader, I spent years helping organizations avoid bad outcomes. I analyzed as many angles as I could and prepared in the most realistic way possible. As a CEO, I still care deeply about avoiding bad outcomes, but I also have to create the conditions for positive outcomes. After all, I have a company to run and grow. That means building momentum, making tradeoffs, allocating capital, setting priorities, developing leaders, and helping the company move with conviction even when the data is not perfect. Sometimes it means deciding between a gamble that could improve ARR or mitigating risk.

Technical Depth Can Become a Constraint

As expected, technical leaders often bring a powerful bias toward depth. We want to understand details and inspect machinery. Often, knowing why something is happening is essential before action takes place.

That instinct is useful. But it can also become a constraint.

As an example, imagine a scenario where sales leadership does not know intimate details about a potential customer. You ask questions such as who the economic buyer really is, what the internal deadlines are, or what their budget is. These are details that dictate how real a deal is and whether you put that data in front of the board. But realistically, those details are likely not made known to a sales person by the potential customer. My bias for depth just became both a constraint and source of frustration.

The CEO’s Job Is to Build Decision Capacity

Realistically, a CEO cannot personally inspect every system, approve every decision, or resolve every ambiguity. The job is not to become the ultimate escalation point for every hard problem. Staying focused, as a CEO you want to build an organization that can make better decisions without waiting for you.

That requires trust.

Trust in people, in operating rhythms, in the quality of the strategy, in the mechanisms that surface truth early. It also requires a leadership tier that understands the business, mission, constraints, and relevant standards.

As a CEO you must accept that no amount of technical brilliance eliminates uncertainty.

Judgment Is the CEO’s Most Important Tool

Given the reality of uncertainty, judgment becomes the CEO’s most important tool.

Judgment is not instinct alone. Nor is it guessing. Judgment is the ability to combine facts, experience, pattern recognition, timing, and foreseen consequences into a decision that moves the organization forward.

Good judgment asks:

  • What do we know?
  • What do we not know?
  • What assumptions are we making?
  • What happens if we are wrong?
  • What must be true for this decision to work?
  • What is the cost of a given decision?
  • What is the cost of not deciding now and waiting?
  • Who needs clarity now?

A Practical Framework for Leading When Certainty Disappears

Confidence under uncertainty does not come from pretending to have all the answers. It comes from using a disciplined process to turn incomplete information into responsible action. When the path forward is unclear, I use the following eight steps:

Accept That Certainty May Not Arrive

Recognize that consequential leadership decisions often must be made before every fact becomes available. Waiting for complete certainty can become its own decision, carrying costs and risks that may exceed those of taking action.

Separate What You Know From What You Do Not Know

Identify the reliable facts, the missing information, and the areas where uncertainty remains. This type of compartmentalization prevents assumptions, opinions, and incomplete signals from being treated as established truth.

Expose Your Assumptions

Make the assumptions behind the decision explicit and determine which ones carry the greatest risk. An assumption left unspoken can quietly become a serious risk and even a single point of failure.

Evaluate the Consequences

Consider what happens if the decision is wrong and how the effects could spread across the organization. Look beyond the immediate outcome to the potential larger impact on customers, employees, cash, execution, and credibility.

Compare Action With Inaction

Assess the cost of acting, the cost of waiting, and the risks created by delay or excessive analysis. Inaction can be very expensive. Leaders often examine the downside of moving forward without giving equal attention to the downside of standing still.

Define what must be

Determine the conditions required for the decision to succeed. Then assess whether those conditions already exist, can be created, or depend on factors outside the organization’s control.

Make and communicate the decision

Establish a clear direction, explain what matters most, and give the people who must act the clarity they need. Communicate what you know, what you believe, what you have decided, and what the organization must do next.

Adapt as reality provides new information

Lead as transparently as possible, with honest conviction rather than false certainty. Monitor the results, test the assumptions behind the decision, and adjust the course as new facts emerge.

This process does not eliminate uncertainty. It creates the decision capacity required to move through it responsibly. The objective is not to predict every outcome. It is to make the strongest available decision, communicate it clearly, and remain ready to adapt.

The Company Is Now the System

Some of those questions are familiar to technical leaders. They sound a lot like risk analysis, incident response, architecture review, and threat modeling. The difference is that, as CEO, they now apply to areas (e.g., Sales, Marketing, HR) technical leaders seldom manage. In fact, they now apply to the whole company.

Strategy becomes an architecture problem. Culture becomes a scaling problem. Communication becomes a signal integrity problem. Talent becomes a resilience problem. Cash becomes an operating constraint. Execution becomes the ultimate proof point.

The CEO role forces you to widen the aperture.

You can no longer look only at whether something is functional or technically sound. You have to ask whether it is commercially viable, operationally executable, strategically aligned, and fiscally responsible. You have to think about how decisions cascade across customers, employees, investors, partners, and the broader market.

Credibility Changes at the CEO Level

That broader blast radius for each decision made is where the CEO transition can feel uncomfortable for deeply technical leaders.

We are used to being credible because of what we know. As CEO, credibility increasingly comes from how we decide, how we communicate, and how we create clarity for others, even when conditions are hazy.

The organization does not need the CEO to have every answer.

It needs the CEO to establish clear direction.

It needs the CEO to make the hard calls.

It needs the CEO to define what matters most.

It needs the CEO to be calm when the data is incomplete and when the pressure is on.

It needs the CEO to turn ambiguity into action.

Conviction Without False Certainty

To be clear, none of this means pretending to be certain. In fact, false certainty is dangerous. People can feel when a leader is manufacturing confidence. The better posture is honest conviction: here is what we know, here is what we believe, here is what we are going to do, and here is how we will adapt as reality teaches us more.

That is a different kind of leadership maturity.

The transition from CISO or CTO to CEO is not a rejection of technical depth. It is an expansion of it. The same disciplines still matter: systems thinking, adversarial understanding, resilience, risk management, architecture, and operational rigor.

The difference is that they must be applied at a broader level.

The company is now the system.

The market is now the threat model.

The competition is now an adversary.

The strategy is now the architecture.

The people are now the execution layer.

And the CEO is responsible for whether all of it works together under pressure.

Your Expertise Got You Here. Judgment Determines What Happens Next.

For technical leaders aspiring to broader executive roles, this is the real lesson: your expertise got you to the table, but judgment determines your impact once you are there.

Depth still matters. Precision still matters. Technical fluency still matters.

But the role changes.

You are no longer only protecting the business.

You are leading it.

You are growing it.

And leadership, at the CEO level, is the discipline of making consequential decisions before certainty arrives.

“The Artificial Adversary” – a New Operating Model for Cybercrime

The Artificial Adversary - a New Operating Model for Cybercrime

“Artificial adversaries don’t have egos, suffer burnout, or deal with corporate drama. Your defenses do.” – Andres Andreu

In the spring of 2026, a handful of engineers with little security background ran an experiment. They pointed an Artificial Intelligence (AI) model at thousands of software codebases and asked it to identify issues. Over the course of one night it did more than find decades-old flaws hiding in plain sight. It created working exploits for them. The model was Claude Mythos Preview. In fact, its creator judged it so capable at weaponizing vulnerabilities that it chose not to release the model at that time.

For most of our field’s history, the adversary was human. Clever and motivated but bounded by sleep, attention, money, and skill. Now, however, that adversary is being augmented, and sometimes replaced. The replacement does not tire or hesitate. Moreover, it ignores the operational rhythms our defenses quietly assume. I call it “The Artificial Adversary.” Essentially, it takes one of two forms:

  • A human operator empowered by an AI stack.
  • An autonomous AI system acting toward malicious ends.

At this stage these have stopped being thought experiments and are now turning up in incident reports.

An Inflection Point, Not a Trend Line

Three things are happening at once. Together, they mark an inflection point rather than an incremental shift:

  • AI has lowered the barrier for entry to sophisticated crime.
  • Synthetic media is collapsing our ability to trust digital signals. A familiar face or a known voice, after all, no longer proves what it once did.
  • The volume and speed of AI-enabled activity now outpaces the manual, static defenses built for a slower era.

The numbers are no longer speculative

SoSafe’s 2025 research found that roughly 87% of organizations worldwide faced an AI-powered cyberattack in the prior year. Direct attacks aside, model evaluations are just as concerning. For instance, the UK’s AI Security Institute (AISI) tested Claude Mythos Preview. It solved expert-level CTF challenges about 73% of the time. Notably, no model could complete those challenges at all before April 2025. Mythos went further still. In fact, it became the first model to solve the AISI’s 32-step simulated network takeover, from reconnaissance to full compromise. Anthropic’s red team reported even broader findings. Working alongside the AISI, it watched the model surface thousands of zero-day flaws. These included a dormant 27-year-old vulnerability in OpenBSD and a 16-year-old bug in FFmpeg. In Firefox alone, Mythos found 271 vulnerabilities and wrote exploits for 181 of them.

A signal, not the threat itself

Anthropic withheld Mythos from public release. Instead, it granted limited access to a small set of organizations that build and maintain critical software and infrastructure. The program is called Project Glasswing. Launch partners reportedly include Amazon Web Services, Apple, Cisco, CrowdStrike, Google, JPMorgan Chase, the Linux Foundation, Microsoft, NVIDIA, and Palo Alto Networks. Officially, the intent was to give defenders a head start. Yet Mythos isn’t the only game in town. For example, things such as OpenAI’s GPT-5.4-Cyber, OWASP CVE Lite CLI, and Google’s Big Sleep already show great promise and in some cases comparable capability. When competition rises the cost of entry keeps falling. Regulators noticed quickly. Within weeks, the Bank of England intensified its AI risk testing, and German banks consulted regulators and cyber experts. The lesson, therefore, is the one Bain and others drew immediately. In short, assume your adversaries are building equivalent capabilities, nation-states, criminal enterprises, and rogue actors alike. Mythos is a signal, not the threat itself.

Defining the Artificial Adversary

It helps to name the archetype precisely, because precision changes how we defend. So picture an AI-enhanced human actor. Here, the human sets the strategic objectives. The machine, in turn, executes the great majority of the tactical workload. The consequence is direct. As a result, offensive cycles compress, and defenders can no longer assume a human-speed response on the other side of the keyboard.

Human adversaries operate within cognitive, temporal, and logistical limits. An autonomous AI-based adversary does not. Needing no sleep, it carries no emotional baggage and runs continuously across global digital environments. Moreover, it can analyze vast data stores and reason probabilistically in real time. Such a system can also coordinate through decentralized, agentic architectures that resist any single point of shutdown. Its capacity for deception, mimicry, and adaptation, therefore, creates a new category of risk. Consequently, detection, attribution, and deterrence all become far harder. The asymmetry, however, is not only technological. It is also cognitive. In the end, defenders must prepare for opponents that do not tire, hesitate, or follow any rules.

The Artificial Adversary Taxonomy

A practical taxonomy has five levels.

  • AI-assisted human operator – a human attacker uses AI for discrete tasks such as phishing, translation, research, script generation, or stolen-data summarization.
  • AI-augmented threat crew – a criminal or nation-state team embeds AI into reconnaissance, exploit research, identity profiling, malware development, infrastructure staging, data exfiltration, and victim communications.
  • AI-orchestrated campaign – agentic systems coordinate personas, assign tasks, monitor responses, tune timing, and manage parallel workflows while humans supervise outcomes.
  • Semi-autonomous adversarial agent – the system conducts meaningful parts of the intrusion chain itself, including asset discovery, service testing, response analysis, and attack path modification.
  • Autonomous malicious AI system – an AI system pursues malicious objectives with limited or delayed human direction, raising harder questions around attribution, containment, predictability, and control.

This taxonomy matters because an AI-assisted phishing actor requires different defenses than an autonomous agent probing applications, manipulating identities, and adapting to telemetry in real time.

Facilitation – Lowering the Barrier

The first way AI empowers adversaries is the least glamorous and the most pervasive. Simply put, it removes friction. For a few years now, the underground has marketed “Dark LLMs.” The roster includes WormGPT, FraudGPT, KawaiiGPT, and imitators such as MalwareGPT, SpamGPT, and Xanthorox. Each promises jailbreaks, malware help, and ready-made scam playbooks. Some are functional. Many, however, are simply scams that prey on aspiring criminals. Either way, the real significance is not any single tool. Rather, it is the normalization of the idea. A capable, on-demand junior developer is now available to anyone with a few GPUs, a wallet of API keys, and some patience.

Malware that writes itself

Proof-of-concept work made the threat concrete. Researchers, for instance, demonstrated BlackMamba, a keylogger that built its malicious code at runtime by calling a Large Language Model (LLM). That approach neatly sidesteps the static signatures defenders rely on. By late 2025, the threat had moved from the lab to the wild. Google’s threat intelligence team documented two malware families: PROMPTFLUX and PROMPTSTEAL. Both query LLMs during execution. One rewrites itself, while the other generates fresh commands mid-attack. This is “Just-In-Time” (JIT) malicious code. In other words, the software does not carry its full payload. Instead, it assembles the payload on demand, from a model that does not know it is being conscripted.

When the face on the call is fake

Facilitation also reaches the human layer through synthetic media. Convincing face and voice clones, for example, can now be mass-produced. So can cross-lingual conversion and studio-quality content. Better yet for the attacker, agent teams run these operations around the clock, iterating on failures without fatigue. As a result, the multi-party deepfake video call is no longer hypothetical. Picture a finance employee walked through an “urgent” wire transfer by a “CFO” and “general counsel” who are both synthetic. Clearly, the attack surface is no longer just endpoints and identities. It now also includes the emotional tone around those identities. And does so across collaboration tools, social media, and internal communications.

Vibe Hacking – Psychological Warfare at Machine Speed

This last point deserves its own name. After all, it is where AI-enabled social engineering becomes something new. Vibe hacking is social engineering supercharged with a full AI stack. Here, the adversary does not send a single phishing email or place one deepfake call. Instead, models shape the emotional context around a target over time. The goal, therefore, is not to trick a victim once. Rather, it is to tune the “vibe” of their human state along with their digital environment, so that risky actions feel natural, familiar, and self-initiated.

Sensing, profiling, persistence

A campaign begins with sensing and profiling. To start, adversaries point AI at everything they can scrape. These sources include OSINT, LinkedIn activity, public Slack and Discord communities, conference talks, support tickets, and marketing emails. Sentiment analysis is important here and models infer mood, personality, stress levels, decision style, and trust anchors. That attackable profile, in turn, feeds a working model of the target’s context. Things like a looming quarter, a key project, the likely sources of anxiety or excitement all become real and exploitable. Generative models subsequently produce content tuned to the target’s state. The real weaponization, however, comes from scale and persistence. One artificial adversary can run dozens of long conversations at once. Each hides behind a distinct persona, the sympathetic colleague, the urgent executive, the overworked vendor. Meanwhile, it A/B tests tone, timing, and channel to learn what lowers resistance and/or skepticism. By the time the critical ask arrives, therefore, the victim feels they are accommodating a relationship, not responding to an attack.

This is the reframing that matters:

Vibe hacking isn’t better phishing. It’s your own people, profiled and played at machine scale – we hardened the edges and left the nervous system exposed.

Andres Andreu

From theory to a real victim list

None of this is a forecast. In August 2025, in fact, Anthropic’s Threat Intelligence team disclosed a case it tracked as GTG-2002. A single actor used an agentic coding tool to run a data-extortion operation. In total, the targets numbered at least 17 organizations, spanning healthcare, emergency services, government, and religious institutions. A defense contractor was among the victims, too. Remarkably, the whole campaign ran in roughly a month. To pull it off, the attacker embedded an operational playbook in a configuration file, so the AI could make tactical decisions during live intrusions. From there, the model automated reconnaissance and credential harvesting. It even generated ransom notes tailored to each victim, with demands reported between roughly $75,000 and more than $500,000. Ultimately, one person, with an AI operator alongside, did the work of a coordinated crew.

Scale – From Assistant to Operator

Facilitation lowers the barrier to entry; scale changes the magnitude. For example, the same agentic models that help an enterprise automate work can be organized into adversarial swarms. A planner agent sets the goals. Meanwhile, sub-agents run in parallel performing actions such as OSINT scraping, phishing and deepfake generation, code generation, and dropper construction. Because they share memory and data from feedback loops, the whole system improves with each iteration.

The criminal supply chain, in turn, has matured around this model. Telegram, for instance, serves as a resilient “dark social layer”, encrypted, anti-censorship, easy to churn and burn, and slow to take down. There, automated bots stream stolen credit card data and run validation checks at a pace no human team could sustain. Increasingly, the same architecture is aimed at availability, too. Agentic orchestrators break a Layer-7 denial-of-service goal into reconnaissance, traffic generation, and adaptive evasion, while coordinated worker nodes handle individual parts of the overall campaign.

The first autonomous espionage campaign

A defining incident arrived in November 2025. Anthropic reported disrupting a campaign it attributed, with high confidence, to a Chinese state-sponsored group tracked as GTG-1002. Notably, it was the first publicly documented, largely autonomous AI-orchestrated cyber-espionage campaign. It was detected in mid-September. In all, the operation targeted roughly thirty high-value organizations across technology, finance, chemical manufacturing, and government.

To pursue their objectives, the attackers manipulated an agentic coding tool into acting as a fleet of autonomous penetration-testing orchestrators and agents. First, they jailbroke its safeguards by role-playing a defensive security firm. Then they broke malicious objectives into benign-looking subtasks. From that point, the AI handled reconnaissance, vulnerability discovery, exploitation, credential harvesting, lateral movement, and exfiltration. In total, that came to an estimated 80 to 90% of tactical operations, issued at thousands of requests per second. Human operators, by contrast, stepped in only at a few strategic chokepoints. This wasn’t as clean as a Hollywood movie scene as the model’s hallucinations sometimes invented credentials or overstated findings. Those errors were among the few things keeping the operation from full autonomy.

A Real Incident, End to End – The NPD Sextortion Wave

To see these capabilities combine into one industrialized pipeline, consider the extortion spam that followed the National Public Data (NPD) breach. The underlying breach was staggering. Systems were first compromised in December 2023. By April 2024, the data had surfaced on the dark web. The company, however, acknowledged the incident only in August 2024. All told, it affected up to 170 million people and exposed as many as three billion records. The follow-on campaign was instructive less for its novelty than for its assembly. Specifically, attackers used GPT-based code generation to operationalize the stolen data end to end. The result was personalized extortion content. Each message addressed the victim by name, referenced a real home address, and embedded street-view imagery of the respective house. Then it demanded payment in Bitcoin, usually between $1,900 and $2,000, for the sake of tranquility or peace of mind.

None of the individual techniques were sophisticated. The sophistication, instead, lay in the orchestration. Consider the parts, a breach corpus, a code-generating model, a templating layer that fused public records with mapping imagery, and a delivery pipeline. Stitched together, these produced a campaign with a scale and personalization no manual operation could match. That, in essence, is the pattern security leaders should internalize. The artificial adversary rarely wins with one brilliant exploit. Instead, it wins by removing friction from every step, and running the whole chain faster than defenders can detect and respond.

Turning the Tables – Disrupting Malicious Automation

The very properties that make AI dangerous on offense also make it invaluable on defense. Better still, they open a counter-strategy that purely human teams never had. If attackers automate, then defenders can engineer the environment to exploit that automation. In practice, deception engineering and adversarial intelligence combine well.

The single goal is to convert the attacker’s automation into your early-warning system. Synthetic credentials, decoy services, and AI-generated traffic, for instance, all look irresistible to an autonomous agent. As such, they become tripwires. Because the agent probes tirelessly and indiscriminately, it hits the decoys long before a careful human would. Consequently, it can surface a campaign while it is still in an early stage.

Red teaming with autonomous agents

AI-augmented red teaming has a strong place here. In a 2024 experiment reported by WIRED, for example, a journalist let autonomous AI agents from the startup RunSybil attack a custom web app. The agents collaborated in real time. Specifically, they used SQL injection, brute-force authentication, form-field manipulation, and path traversal. Most importantly, they iterated on their failures. Without human direction, they re-planned and adjusted strategies, surfacing logic flaws that traditional scanners had missed. The agents were not malicious; their behavior, however, was. It was adversarial, coordinated, and effective. The takeaway, then, is fairly straightforward. First, adopt autonomous red-teaming agents to pressure-test your defenses against continuous, iterative, logic-driven attacks. Then pair them with high-fidelity telemetry and behavioral anomaly detection. Together, they can flag AI-like probing even when individual requests looks benign.

Governing the Machine and the People Around It

Speed without governance introduces its own risk. As defenders deploy autonomous and semi-autonomous capabilities, they take on an obligation. Those capabilities must be fast where they must be, careful where they should be, and always controllable by competent humans. Fortunately, a workable program can borrow from frameworks now maturing across the industry. For a foundation, anchor on NIST’s AI Risk Management Framework or ISO/IEC 42001. To turn principles into adversarial test cases, layer in MITRE ATLAS and the OWASP Top 10 for LLM applications. To harden the model lifecycle, draw on ISO/IEC 23894 and Google’s Secure AI Framework. Finally, add a staged maturity model to move from reactive to adaptive.

High-impact automated actions, meanwhile, need extra care. By default, mass credential revocation, large-scale connection throttling or tarpitting, and account lockouts should sit behind human-in-the-loop gates. In addition, back them with immutable audit logs, explainability proportional to impact, and fast paths to appeal and rollback.

Two cautions

Two cautions deserve emphasis.

First, treat AI models and their supply chains as critical software assets. In practice, that means validating provenance, verifying integrity, and monitoring runtime behavior. After all, data and model poisoning are now first-class threat vectors.

Second, resist the urge to fight fire with fire across legal lines. Attacker AIs, remember, routinely route through innocent third parties. As a result, heavy-handed countermeasures invite escalation and cross-border legal exposure, among them hack-back, automated counter-intrusion, and poisoning someone else’s ecosystem. Privacy by design, data minimization, auditability, and human oversight should not be compliance theater. On the contrary, they should be focused on what keeps a fast defense lawful and trusted.

What Security Leaders Must Do Now

The artificial adversary does not need to be sentient to change the game. Instead, it only needs to make capable attackers faster, more iterative, and less dependent on rare human skill. Accordingly, defenders should architect for that reality:

  • Treat AI as both adversary and ally – regularly run hybrid threat scenarios, machine-augmented attackers against machine-augmented defenders, so that you find your blind spots first.
  • Shift from signatures to behavior – static, content-based controls cannot anticipate self-modifying code or agentic chaining. Instead, invest in behavioral analytics, high-fidelity logging, and context-aware security that reads relationships, not keywords.
  • Stand up real AI governance – name a single accountable owner and convene a cross-functional oversight board. Then keep a model and agent registry, and define rules of engagement and rollback paths before you enable automation.
  • Secure the model supply chain – audit data lineage and model integrity, and assume third-party datasets, weights, and components can be poisoned upstream.
  • Deploy deception as early warning – use AI honeypots and synthetic assets to turn the adversary’s tireless automation into your early detection advantage.
  • Compress your defensive cycle – above all, adopt AI-augmented red teaming and threat hunting so that you out-learn the adversary. Then measure what matters – detection accuracy, false-positive and false-negative rates, model drift, autonomy and override rates, and time to contain.

The Pivotal Question

The pivotal question about any adversary has changed. No longer is it simply who they are or what they want. Instead, it is “what can they assemble and operationalize with AI faster than we can detect and respond?” Once, the human attacker was the central concern. Now, by contrast, security leaders face intelligent, scalable opponents that run as close to machine speed as the hardware allows. Confronting them takes more than static controls and periodic red teaming. Rather, it takes continuous learning, dynamic simulation, and AI-augmented defense. Above all, it takes one hard admission, the next major breach may not be human at all.

Awareness is the beginning; action defines resilience. The Artificial Adversary is here. The only question is whether we will be ready when it decides to strike.

From 4X CISO to CEO: What Leadership Looks Like Now

Andres Andreu reflecting on leadership lessons from moving from CISO to CEO

For years, I led from the seat of a Chief Information Security Officer (CISO). From 4X CISO to CEO.

As a CISO I learned “healthy paranoia”. I learned to see around corners. I learned to prepare for failure without becoming ruled by it. I learned that resilience is not a slogan, trust is not soft, and pressure reveals what an organization really is.

Then I became a CEO.

The title changed, but that was not the real transition. The real transition was this: the scoreboard changed.

Success means something very different now.

As a CISO, much of the job revolves around reducing downside. You protect value. You harden systems. You reduce exposure. You prepare for impact. Success often shows up as the absence of disaster.

As a CEO, that is no where near enough.

A CEO still has to manage downside. But the real job is broader and frankly, harder. You have to create upside even the upside is not obvious. You have to allocate capital, focus people, accelerate execution, build trust, and make the company stronger under pressure. You are no longer measured only by what you prevent. You are measured by what you build, what you compound, and whether the organization can win.

That shift has changed how I think about leadership.

It has not made me less disciplined. It has made me more complete.

Here are the lessons that came into focus for me in the move from CISO to CEO.

Protecting value and creating value are not the same job

Security leaders are trained to think in terms of exposure, controls, failure paths, and resilience. That training is valuable. In fact, in a volatile world, it is a serious leadership advantage. But …

The CEO role forces a wider lens.

You cannot lead a company by focusing only on what might break. You have to decide what deserves energy, capital, and conviction. You have to place bets. You have to define where the company will lead, where it will differentiate, and where it will refuse distraction. You also have to make the hard choices between protecting something or paving a path to new revenue.

That is a major shift.

A CISO protects value.

A CEO creates, compounds, and defends value.

The distinction matters because it changes the posture of leadership. It moves you from preservation alone to purposeful construction.

Risk is only part of the story

For a long time, one of the most important questions in my world was: What could go wrong?

That question still matters. It always will.

But CEOs have to ask a broader set of questions:

What are we building?
What are we solving?
What are we choosing not to do?
Where are we underinvesting?
What will matter six quarters from now, not just six weeks from now?

This is where many leaders get trapped. They confuse awareness of risk with clarity of direction.

These are not the same.

A company can become highly fluent in threat, friction, and constraints and still fail to move. It can become excellent at discussing complexity and poor at converting that complexity into action.

The CEO’s job is not to eliminate uncertainty. The CEO’s job is to move the organization through uncertainty with judgment.

That is a different discipline.

Capital allocation spells truth

One of the clearest lessons of becoming CEO is that strategy sounds impressive in slides but reveals itself in budgets.

Capital allocation exposes the truth.

You can say innovation matters. But if you do not invest in data quality, operating discipline, and workflow redesign, then innovation does not really matter.

You can say trust matters. But if you underfund execution, transparency, and customer experience, then trust does not really matter either.

You can say growth matters. But if priorities are bloated, ownership is vague, and friction is tolerated, then growth is a cheap talking point.

This is one of the hardest truths in leadership: strategy is not what you announce. Strategy is what you consistently fund, reinforce, and protect.

The CEO sees that more directly than anyone else.

Money is not just a resource. It is a declaration of belief.

Clarity scales better than intensity

Earlier in my career, I thought strong leadership often meant pushing harder, doing more, leading in very visible form.

I no longer believe that.

Strong leadership now means clarifying faster.

Companies do not scale on intensity alone. They scale on clarity. They scale when people know what matters, who you are selling to, who owns what, how decisions get made, what good looks like, and what deserves to be ignored.

Intensity without clarity creates motion, not momentum.

This becomes even more important at the CEO level because ambiguity compounds as it moves through the organization. A vague executive statement becomes a confused team priority. A confused priority becomes wasted time. Wasted time becomes operating drag. Operating drag becomes missed expectations.

That is why clarity is not just a communication skill. It is an operating advantage.

The larger the company, the more expensive vague leadership becomes. But, it also takes longer to unearth that type of situation. In smaller companies vague leadership exposes itself way quicker as there are fewer buffers.

Trust is not soft. Trust is throughput.

Too many leaders still talk about trust as if it belongs in the category of culture alone.

It does not.

Trust affects speed. Trust affects execution. Trust affects retention. Trust affects customer confidence. Trust affects whether people escalate intelligently or defensively. Trust affects whether hard truths surface early or get buried until they become an expensive burden.

In low-trust environments, everything takes longer. People protect themselves. Decisions loop slowly. Teams revisit the same conversations. Energy leaks everywhere. Indecision reigns.

In high-trust environments, accountability gets stronger, not weaker. Standards become easier to uphold because intent is clearer and friction is lower.

This is one of the biggest mindset expansions I have had as a CEO.

Trust is not theater.

Trust is infrastructure.

And in many organizations, it is the hidden variable behind execution quality.

Resilience matters more than compliance

Compliance matters. It builds baseline discipline. It creates structure. It can improve consistency.

But compliance is not the same as resilience.

A compliant company can still be fragile.

A resilient company absorbs pressure without losing direction. It adapts when conditions change. It makes decisions based on imperfect and/or incomplete information. It keeps operating even when the environment turns hostile.

That distinction matters now more than ever.

The modern business environment does not reward organizations simply for looking prepared. It rewards organizations that can keep moving when things break.

This is where my years in security still shape me deeply. I know what fragility looks like. I know how fast confidence erodes when stress exposes weak assumptions. I know the difference between a control that looks good and a capability that holds.

As CEO, that lesson only became more important.

Build for the test, not just the audit.

The CEO’s steadiness becomes part of the operating model

This may be the most personal lesson of all.

The CEO carries more than accountability. The CEO carries signal.

The ecosystem around you (employees, the board, investors, peer CEOs, partners, customers) watch how you process. How you process pressure. How you process the sea of bad news with the sprinkle of good here and there. They watch how you handle incomplete information, mixed results, difficult tradeoffs, and external noise. They watch your tone when momentum slows. They watch your posture when the answer is not obvious.

This does not mean a CEO needs to project false certainty.

It does mean the CEO has to project steadiness.

And do so irrespective of what is at hand. That steadiness matters because organizations borrow emotional direction from leadership. When the environment is noisy and/or unsteady, the CEO helps determine whether the company becomes reactive, distracted, disciplined, or resolved.

That is not abstract leadership philosophy. It’s not a textbook principle taught in business school. That is operational reality.

Steadiness preserves focus. Focus preserves execution. Execution preserves trust.

In the end, leadership is not about being right. It is about steering the organization towards an outcome, making sure employees stay oriented while the organization works through challenges and hurdles.

Security-Centric Steadiness

Security teams alone cannot secure a company from threats. The company’s organizational culture, risk tolerance, and investments are defined collectively by leadership, in many organization this means a roll up to the CEO. It is ultimately the CEOs responsibility to:

  1. Set the tone (organizational culture): if the CEO treats security as a priority, it permeates the entire organization. If neglected, it breeds a relaxed, vulnerable afterthought culture.
  2. Define risk tolerance: the CEO must decide what level of risk is acceptable and where to invest in defense, rather than assuming the CISO can stop 100% of attacks alone. This is a challenge as a lot of CEOs, for the sake of self-preservation, shy away from explicitly taking a stance on risk. Even if a CEO isn’t signing an acceptance/rejection of risk they can define tolerance levels.
  3. Create cross functional alignment: silos break down due to force from the top, IT, Legal, HR, and Operations work together to protect the company when that is a mandate coming from the CEO.

What I carried with me from the CISO seat

I did not leave my CISO instincts behind when I became CEO.

I feel I brought the best of them with me.

I still believe in disciplined thinking.
I still believe in resilience under pressure.
I still believe in asking hard questions early.
I still believe that trust takes years to build and minutes to lose.
I still believe leaders should prepare for failure without becoming defined by fear.

But the CEO role forced me to widen the aperture.

The mission is no longer only to defend the enterprise.

The mission is to build an enterprise that can win.

That means creating trust, not just protecting it. It means creating momentum, not just preventing disruption. It means turning discipline into direction, and direction into execution.

That is the real shift.

Final thought

Going from a 4X CISO to CEO did not make me think less about security. It made me think more completely about leadership. I still believe the best leaders see the angles and see around corners. Now, I also believe they have to be able to build through those angles and corners.

Industrialized Identity – The New Factory Model for Fraud

Industrialized Identity - The New Factory Model for Fraud

Industrialized Identity – adversaries now run identity like a factory. Most organizations still talk about identity breaches like they talk about storms: unfortunate, occasional, and mostly out of their control. But attackers don’t forecast storms, they manufacture them.

The adversary does not see it that way. Instead, they treat identity as raw material. They harvest it, refine it, enrich it, and operationalize it, over and over, until they can monetize it by running fraud, impersonation, and Account Takeover (ATO) campaigns like a production line.

This dynamic doesn’t just change adversarial TTPs per say. And this cascades as it changes the adversary’s economics. It also changes defender timelines. And it changes what “good” looks like for a CISO who needs to protect revenue, customers, and business operations.

In the 2026 Identity Breach Report from Constella Intelligence we see the signal clearly – identity exposure now moves at machine speed and scale, with industrial processes behind it, not opportunistic one-offs.

Identity risk didn’t just get “worse.” It got productized.

And once it’s productized, attackers don’t need to break in to create impact. They can often log in, have data changed/reset, or impersonate. Traction becomes real when they assemble “attackable profiles”. In practice, that means they can:

  • pass help desk or account recovery checks
  • bypass “knowledge-based” verification
  • look legitimate across channels
  • scale automation without spiking obvious alarms

For these attackable profiles to become real, adversaries have built an identity supply chain:

Ingest → Clean → Correlate → Enrich → Package → Operationalize

Quarterly controls and reactive incident response will not stand up to this type of pattern. Worse off this can become industrialized at scale. Defense models need to runs at that same tempo.

The Identity Density Gap – the story behind +135% record growth vs. +11% unique identifiers

Let’s quantify the shift. Here’s a 2025 statistic that should force a mindset change: breach record volume grew by 135% while unique identifiers only grew 11%.

That says something simple and brutal: adversaries don’t need more identity data. So the problem isn’t more identities. It’s more context per identity (more data per person). This is the Identity Density Gap.

Put differently, density is leverage:

  • A thin identity (email + password) supports commodity credential stuffing.
  • A dense identity (email + phone + address + DOB + linked accounts + recovery hints + active session objects) supports high-confidence impersonation and repeatable fraud.

Density gives attackers options. Options create resilience. Resilience creates pathways that can also be leveraged at scale.

The outdated way that so many security teams pursued was to fixed authentication. Yet they constantly lost to ATO and fraud. The adversary no longer cares about the login prompt, they are seeing the surface across the entire identity lifecycle:

  • onboarding and enrollment
  • authentication
  • session handling and token reuse
  • account recovery and help desk flows
  • high-risk transactions and workflow approvals

Defending only one link in that chain is a mere inconvenience now, attackers route around fragmented strategies. And they do it fast.

Industrialized data correlation – how attackers turn billions of attributes into attackable profiles

Attackers don’t win because they possess data. Attackers win because they correlate data. When an operation runs at the scale of 400 billion+ attributes, correlation stops being a research activity and becomes a manufacturing step. Couple this with the vast amount of OSINT in existence and a picture starts to form.

Here’s how the factory works:

First – Normalization

Adversaries normalize raw material – they standardize fields, clean formatting, remove duplicates, and fix missing pieces. They don’t need perfection. They need enough consistency to automate.

Next – Linking

Data gets linked across disparate datasets – the adversary matches email addresses to phone numbers. Phone numbers to addresses. Addresses to dates of birth, and so on. One dataset fills the gaps in another.

Then – Scoring

Adversaries score attackable profiles to measure ROI. They don’t ask, “Can I compromise this account?” They ask, “Can I monetize this identity fast?”

They prioritize identities that connect to:

  • financial access
  • enterprise privileges
  • payroll and HR workflows
  • customer support recovery paths
  • vendor payment processes

Finally – Packaging

Profiles get packaged for operations. This is where identity becomes attackable. The profile supports repeatable playbooks: ATO, recovery bypass, SIM swap targeting, impersonation, and payment diversion.

That’s why identity risk now behaves like a business function for adversaries. They build a pipeline. That pipeline gets refined. Then it gets scaled.

And then exposure events feed that pipeline.

The Top Exposure Events – why mega breaches punch above their weight

When massive exposure events hit, many leaders respond with the familiar: “We’ll monitor. We’ll see if we’re affected.”

That script fails at machine speed. Large exposure events don’t just increase volume, they increase operational certainty for attackers:

  • consistent record structure
  • high overlap of data points with prior leaks
  • fast enrichment potential
  • easy automation with AI powered technologies

There are many examples of large data breaches. At this point they need to be treated as more than just headlines. Treat them as inventory injections, the raw materials needed for the modern day identity supply chain.

Once that inventory enters circulation, attackers don’t “use it once.” They:

  • monetize it
  • repackage it
  • enrich it with other datasets
  • resell it
  • and operationalize it in waves

That’s why identity exposure rarely behaves like a single incident. It behaves like a persistent condition.

And that’s why “wait for confirmed compromise” becomes the wrong approach.

Machine-speed defense – stop chasing events, interdict the pipeline

If attackers run identity like a factory, defenders must reciprocate. Defenders need to treat identity like a control plane.

This isn’t about perfect security as there is no such thing. Defenders do however need faster cycles:

  • faster detection-to-decision
  • faster decision-to-enforcement
  • tighter governance around automation
  • metrics that prove reduced operational risk

Here are some practical steps to improve an ecosystem:

Convert exposure into action

Alerts don’t help if they don’t trigger changes in systems and/or behavior. If it doesn’t change enforcement, it’s just telemetry. Build an identity exposure-to-action playbook that answers:

  • Which identities matter most? (executives, finance, privileged admins, support)
  • Which workflows create the largest blast radius? (recovery, vendor payments, payroll, customer support)
  • What control do we trigger first? (session resets, account recovery restrictions, throughput throttling)

Next, attack their economics.

Render stolen credentials less valuable

Kill the advantages that adversaries love by:

  • deploying phish-resistant MFA, especially for privileged roles
  • binding sessions to devices where possible
  • tightening token lifetimes and reuse policies

Then, close the side doors.

Harden the bypass routes

Adversaries don’t always brute force their way in. They tke less resistant paths, such as socially engineering account resets via a help desk. Treat recovery like a privileged operation by:

  • restricting recovery pathways for users, especially privileged ones
  • requiring stronger proof for recovery than just login creds
  • adding friction (synchronous checks via phone call, etc) to high-impact changes (bank info, payout routing, email changes)
  • training support teams on identity manipulation patterns and escalation guardrails

Finally, scale your response.

Automate enforcement

Automation wins at machine speed when done right, but beware as it can also break business operations. Start slow with low-risk actions and require human approval for high-impact actions (account lockouts, financial workflow freezes, privileged access resets).

And if you want to win long-term, measure what matters.

Measure the right outcomes

Generally speaking, if something gets measured, it can be improved. Consider the following so as to improve a security posture:

  • time-to-detect exposure (requires analysis to unearth original exposure)
  • time-to-enforce controls
  • % of privileged users on phish-resistant MFA
  • reduction in successful recovery abuse
  • reduction in ATO attempts that reach “valid session” state

Some of these metrics are not trivial and require analysis. But they translate cleanly to business outcomes: less fraud, fewer outages, fewer customer escalations.

The bottom line

Identity risk didn’t just automagically grow. It got industrialized.

Interestingly, attackers now build identity products. They run correlation pipelines. They operationalize exposure at machine speed. And they scale fraud the way mature businesses scale customer acquisition: with automation, testing, and iteration.

Here’s the modern posture. Instead of relying on outdated perimeter strategies, consider:

  • treating exposure as a leading indicator
  • hardening the identity lifecycle, not just the login
  • interdicting the pipeline wherever possible

Defending identity in the industrial era requires a new mindset.

AI Powered Cybercrime – How AI Supercharged a Sextortion Wave

AI Powered Cybercrime - How AI Supercharged a Sextortion Wave

Part 3 of AI Powered Cybercrime

Sextortion isn’t new. Velocity has increased, personalization has sharpened, and attackers can now run campaigns at industrial scale. This wave is a collision event between the first two posts in this series: facilitation (credible intimidation) and scale (high-volume delivery). AI Powered Cybercrime – How AI Supercharged a Sextortion Wave.

Many security programs have an important blind spot: they treat coercion as a personal problem. In reality, coercion quickly becomes an enterprise problem when it pressures employees into silence, errors, or unsafe, unethical, illegal behavior.

What happened (high level)

Following the large-scale exposure of personal data from a data broker, threat actors began sending extortion emails that included real names, real email addresses, and real home addresses. The goal was not technical proof; it was psychological terrorism. When a recipient of these types of emails and/or files sees real personal details, the scam feels “more real,” even if the core claim is false.

Some variants escalated intimidation by including a photo of the victim’s home sourced from publicly available mapping imagery. That addition is a masterclass in facilitation: it takes something the attacker can generate cheaply and turns it into a credibility anchor that increases stress and compresses decision time.

Why it works: plausibility beats truth under pressure

Most victims don’t evaluate these messages like analysts. They evaluate them like humans under threat, with emotion. The campaign design is built around that reality: shock, shame, urgency, and a narrow window to “fix” the situation. The scam doesn’t need to be technically accurate to be operationally effective; it only needs to feel plausible long enough to trigger payment.

This is the same vibe hacking dynamic we see in enterprise fraud: urgency is used as a control bypass. When the attacker can manufacture plausibility quickly, policy and verification become the only reliable defenses.

Where AI fits (without fluff)

AI does not need to run the entire scheme to increase harm. It only needs to improve the leverage points. First, it enables endless text variation while maintaining a consistent tone and similar messaging. Second, it makes personalization easy by merging templates with leaked data and also seamlessly integrating with Open Source Intelligence (OSINT) sources. Third, it reduces the human effort required to run a campaign, which increases throughput.

The result isn’t “smarter extortion.” It’s cheaper extortion at higher volume paired with sharper intimidation artifacts. That combination is what makes waves like this so disruptive.

Why CISOs should care: coercion becomes a business threat

Even when a victim is targeted “personally,” the downstream effects can land inside a CISOs organization. An employee under threat may avoid reporting, reuse credentials poorly, or comply with nefarious demands. Panic and stress can lead to unsafe behaviors, and this noise can weave its way into an organization distracting security teams from secondary attacks that aim to exploit some of that chaos.

If a resilience program covers ransomware but not coercion-driven fraud and extortion, there could be an operational gap. Sextortion waves are a reminder that the adversary’s true target is often decision-making under pressure.

What organizations should do during a wave

The objective in a wave is speed, clarity, and support. Issue a same-day bulletin that states what is happening, what employees should do, and how to report. Keep it stigma-free. The most important message is this: employees can report safely, and they won’t get in trouble.

Next, harden the identity bridge. Ensure MFA is enforced for email and sensitive applications, watch for anomalous sign-ins, and monitor for new device enrollments. Then improve detection quality by treating this as a campaign: pattern match across inboxes and route messaging to a single owner to reduce confusion and duplicate work.

The resilience lesson

Waves like this are not just security events; they’re leadership events. The organizations that respond well reduce harm by moving fast, communicating clearly, and providing support. They also learn: which workflows were stress-tested, where employees hesitated, and what verification gates were missing.

If you treat coercion as out-of-scope, you will eventually treat it as an incident, under pressure. Build the playbook now.

Key takeaways

  • Normalize coercion reporting; activate employee assistance programs immediately to protect people and organizational reputation.
  • Instrument wave messaging detection to tune signals and reduce both data fatigue and operational distraction.
  • Harden identity ecosystems fast; enforce MFA immediately to prevent panic-driven account takeover actions.
  • Operationalize extortion playbooks and drill them regularly to reduce chaos and decision latency.