Your Biggest Cybersecurity Blind Spot Is How You Make Money

Your Biggest Cybersecurity Blind Spot Is How You Make Money.
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A huge blind spot may sit inside the decisions that generate your revenue. Growth targets, customer commitments, and operating pressures shape how your people grant access, approve exceptions, and respond to unusual requests. Attackers can exploit those choices. Your biggest cybersecurity blind spot is how you make money.

Look at what your company cannot afford to interrupt, and that is an area where an adversary is likely to apply pressure.

Your revenue engine tells a story. It reveals which customers command exceptions and/or expedient actions, which systems must run as a matter of urgency, and which deadlines override caution. An adversary who understands that story can identify opportunities that a vulnerability scan will never reveal.

Having held engineering, CISO, and CEO roles, I approach this problem from several directions. Growth matters. Customer commitments matter. Cash matters. So does understanding how those priorities influence behavior under pressure.

A security strategy that ignores how the company makes money leaves part of the adversary’s opportunity unexplored.

1. Your Growth Targets Can Reward Dangerous Shortcuts

Growth targets create exposure when employees gain more from completing a transaction than from questioning related risks and/or its legitimacy.

Consider a hypothetical sales team approaching quarter-end. A major prospect requests an urgent integration. The deal requires broad access, while the security review threatens the closing date.

Leadership approves an exception. The integration launches. Everyone celebrates the booking. Meanwhile, nobody owns the related risk, the accountability for security problems, or the deadline for addressing the risk that has been introduced.

That commercial decision has created an opportunity.

An attacker does not need to understand the business opportunity or revenue recognition. They only need to discover that urgency and revenue potential make your organization more accommodating.

Leaders should examine what happens after someone raises a concern. If that person loses recognition while the exception earns praise, the incentive speaks louder than the responsibility to protect the organization.

One interesting question to ask: which business targets and/or processes encourage employees to skip verifications, open up access, or leave exceptions unresolved?

2. Your Largest Customers Can Become Exceptions to Your Rules

Customer concentration creates security pressure when employees fear that enforcing a boundary could damage a critical relationship. And if a business depends heavily on one or a select few customers then this problem grows exponentially. A simple check of Annual Recurring Revenue (ARR) percentages can paint a very clear picture.

A valuable customer, that represents 51% of your ARR, requests a sensitive data export or an unusual support action. The timing coincides closely with a renewal deadline so there is real pressure. Your team recognizes the organization and the timing at hand, they in turn accelerate the fulfillment of the requests.

Familiarity with an account like this does not establish the legitimacy of every request.

An adversary could impersonate a customer contact or compromise that customer contact’s account. Knowledge of the relationship makes the request more convincing. Commercial pressure makes hesitation more expensive.

This is where identity intelligence and business context intersect. Your team needs to understand who requests the action, what authority they hold, and whether the request fits the relationship.

Organizations need to give employees a fast, independent verification route. Make sure executives on both sides support its use when a customer pushes back. After all, this is for the sake of mutual protection and this cannot be ignored considering the level of fraud that exists today.

One interesting question to ask: which customer names cause our people to stop challenging unusual requests?

3. Your Availability Promise Gives Attackers a Pressure Point

A business that depends on continuous service gives adversaries an opportunity to exploit the cost of interruption.

That pressure extends beyond a complete outage. An attacker could target a narrow function that blocks revenue or delivery. Think about something like order releases, customer authentication, production scheduling, or access to operational data.

The technical footprint may feel small. The business consequence may prove enormous.

Consequently, an asset inventory cannot tell the whole story. Security teams need to understand how disruption travels through the business. This is a systemic approach rather than one focused on a specific node.

Focusing on resilience, teams should aim to map the processes that must continue, their dependencies, and the alternatives that can actually keep a business operational. Then, those alternatives need to be pressure tested under realistic constraints. They may be the saving factor in the face of a negatively impacting event.

One interesting question to ask: which single interruption would place leadership under the greatest pressure to make a bad decision?

4. Your Efficiency Strategy Can Concentrate Failure

Efficiency creates concentrated exposure when several business functions depend on the same provider, integration, or privileged identity. This is similar to many disparate software elements all depending on one library.

Consolidation can simplify operations and improve security management. However, it also deserves a clear examination of shared dependencies.

Consider several departments that rely on one platform. Each department documents its own business continuity plan. Yet every plan assumes the same platform will remain available.

The organization has several plans and one single point of failure.

Shared service accounts and administrative integrations can create a similar problem. A compromise in one location may give an adversary influence across multiple workflows.

Teams need to evaluate the scope of access, the ability to isolate affected functions, and the practical cost of operating without some dependency. Treat those findings as inputs to future efficiency decisions.

One interesting question to ask: where have we reduced operating costs by concentrating authority or eliminating a workable alternative?

5. Your Customer Experience Can Weaken Identity Checks

Customer experience goals create exposure when teams remove verification steps without preserving reliable ways to establish identity and authority.

Account recovery makes this tension obvious. A legitimate customer wants immediate access (quite often with a loud voice). A support employee wants to resolve the problem quickly. An adversary wants the same outcome as the customer: control of the account.

If your workflow rewards speed above all else, the attacker can vibe hack (use frustration, create a sense of urgency, utilize personal details, etc) their way to pushing the interaction forward.

Accurate information about someone does not prove that the requester is that person. Likewise, successful authentication does not justify every subsequent action.

These days teams should aim to design additional verification around consequential actions, including recovery, privilege changes, and sensitive exports. Leadership needs to give support teams a clear escalation path that preserves service quality.

One interesting question to ask: can someone use our commitment to customer excellence to obtain access they could not otherwise gain?

6. Your Automation Can Simply Execute the Wrong Business Decision Faster

Automation amplifies exposure when systems act on manipulated inputs with more authority than the task requires.

Imagine a workflow that accepts a supplier change, updates a record, and initiates downstream actions. An attacker who influences the input may redirect the process without exploiting a software vulnerability.

An AI agent with delegated access adds another decision point. Leaders must understand what evidence the agent trusts and which actions it can initiate.

The business objective may sound harmless: resolve requests faster. The implementation may grant authority to change records, release information, or commit resources.

There needs to be a separation of low-impact assistance from consequential execution. Aim to limit authority, verify sensitive changes through independent channels, and preserve a practical way to stop the workflow.

One interesting question to ask: what could an adversary accomplish if our automation accepted a convincing but malicious request?

7. Your Culture Can Hide the Warnings Leadership Needs

A culture that penalizes delay or unwelcome news makes it harder for leadership to recognize exploitable conditions.

Employees learn which concerns leaders welcome. They also learn which concerns threaten a launch, embarrass an executive, or complicate a forecast. Moreover, they learn which actions threaten their livelihood.

Over time, people may soften their language, defer escalation, or handle exceptions quietly. Leadership then makes decisions with an incomplete picture.

An adversary benefits from that gap.

CEOs should examine how their own behavior shapes escalation. When someone challenges a commercially attractive decision, do you investigate the concern? Or do you demand a workaround before understanding the exposure?

Make someone accountable for each exception, including its scope, expiration, and corrective action. Reward employees who surface a problem early enough to address it. Also, reward employees who scrutinize situations to ensure nothing fraudulent is at hand.

One interesting question to ask: what do our people already know that our leadership team makes difficult to say?

Put the Business Model Inside Your Threat Model

Leaders can start addressing some of these risks by testing how an adversary could exploit the company’s most important commercial workflows.

Start with one process that generates revenue or delivers a critical service. Bring its business owner together with security and engineering.

Trace:

  • Who requests an action?
  • Who authorizes said action?
  • What evidence does the authorizer trust?
  • Which systems execute the action?

Based on those answers, identify where urgency changes the rules. Then test a plausible abuse scenario. Consider these questions:

  • Could a convincing customer request trigger an unauthorized data export or action?
  • Could a supplier impersonator redirect a workflow?
  • Could one compromised identity interrupt multiple services?

Use the findings to change processes accordingly. The “if it ain’t broke don’t fix it” approach is not applicable here. Assign an owner to be held accountable for scrutinization and adjustments, set a deadline, and test whether the change blocks the abuse. The “test” part is very relevant here as this should be done by a competent entity and with the same rules of engagement that nefarious actors follow … none.

This is how the adversarial mindset becomes a business discipline. It forces leadership to examine how someone could turn the organization’s priorities against it.

This does require that a CISO understand the revenue engine at hand. A CEO needs to understand the exposure that engine creates. Both need the authority to act on what they discover. The CEO usually has this but the CISO may not.

Your business model explains how you intend to win. Study it closely enough to understand how an adversary could use it against you because your biggest cybersecurity blind spot is how you make money.

Why AI Governance Is Now a Critical Leadership Responsibility

Why AI Governance Is Now a Critical Leadership Responsibility.
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Unfortunately, many organizations are on the path to repeat one of the most consequential mistakes that we have made in this industry. AI Governance is now a critical leadership responsibility.

For years, executive leaders treated cybersecurity as a technical issue. It was convenient to tuck it away under Information Technology (IT) and it became someone else’s problem. They delegated it to specialists, discussed it only when budgets or incidents demanded attention, and assumed that technical teams could contain the risk.

Then the breaches became business disruptions. Regulatory consequences reached the CFO as well as the boardroom. Trust degraded, especially from customers. Operations stopped. Executives discovered that although they could delegate security work, they could not delegate accountability for the outcome. Tucking it conveniently inside of IT was no longer an option.

Artificial Intelligence (AI) is now following an eerily similar path, only much faster.

Many organizations still treat AI governance as a collection of technical controls, acceptable-use policies, legal reviews, and model assessments. They assign it to IT, data science, security, privacy, or compliance and assume those functions can govern the technology on behalf of the enterprise.

Simply put, they cannot.

Those teams can implement controls, evaluate models, monitor systems, and advise the business. They cannot independently decide which risks the organization should accept, which decisions should be influenced by AI or automation, where humans must retain authority, or who remains accountable when an AI-enabled processes have a negative impact.

Those are leadership decisions.

AI governance is not a technical specialization that executives can delegate. It is a leadership capability that executives must develop.

Leadership Cannot Outsource Accountability

I have spent much of my career moving between deeply technical responsibilities and executive leadership. I have worked in federal law enforcement technology, application architecture, offensive security, cybersecurity, the CISO function, the CTO function, and the CEO role.

Those experiences repeatedly reinforced the same lesson: technology may create the mechanism, but leadership creates the consequence.

For me, it took a while but that had to sink in as I lived my professional journey.

An algorithm does not determine whether an organization should use AI to evaluate employees, prioritize customers, detect fraud, approve transactions, recommend medical actions, or automate security responses. Leaders make those decisions.

The system may generate a recommendation, classification, or action. It does not absorb responsibility for the result. It simply generates an output.

A model cannot accept enterprise risk.

A chatbot is likely to not be able to explain a decision to an auditor.

An autonomous agent cannot appear before the board and defend the authority it was granted.

The human signature may become less visible as the footprints of AI and automation increase, but it does not disappear. It moves upward through the organization until it reaches the leaders who authorized these systems, established their boundaries (hopefully), funded their deployments, and accepted the risks at hand (again, hopefully).

AI Means More Than Generative AI

One reason organizations misunderstand AI governance is that most current conversations concentrate so heavily on Generative AI (GenAI). And the notion of “AI” in those conversations is incorrectly used to mean “GenAI”.

Large Language Models (LLMs), copilots, image generators, and conversational interfaces have made a subset of AI (GenAI) visible to almost everyone. They have also narrowed the discussion.

AI as a field extends far beyond generated text and images. Some organizations already use AI to:

  • Detect financial fraud and account takeover.
  • Score credit and insurance risk.
  • Identify cyber threats and automate containment.
  • Rank candidates and evaluate employee performance.
  • Recognize faces, objects, behaviors, and anomalies.
  • Predict equipment failures and optimize industrial processes.
  • Recommend products, services, prices, and content.
  • Route vehicles, shipments, and supply-chain resources.
  • Support medical diagnosis and clinical decisions.
  • Operate robots, sensors, and autonomous systems.

These systems may never generate a paragraph, but they can still shape someone’s employment, financial access, safety, privacy, or treatment.

Leaders who define AI governance as a policy for using ChatGPT will govern only the most visible layer of a much larger technology landscape.

Every system that predicts, accepts, rejects, classifies, recommends, prioritizes, optimizes, or acts should fall within the governance conversation. Yet, the limited understanding of where AI actually exists within organizations does not make that proper conversation possible.

AI Governance Begins With Ownership

Every material AI system needs an accountable owner. It doesn’t need a committee or some vague reference to “the business.”

A named leader must own the business purpose, risk, performance, and consequences of the system.

Technical ownership also matters, but it is not the same as business accountability. A data science team may build a model. A cloud team may host it. Security may monitor it. Legal may review it. None of those activities answers this central question:

Who has the authority to decide that this system should operate?

Ownership must extend across the AI lifecycle:

  • Who approved the use case?
  • Who authorized the data?
  • Who selected or developed the model?
  • Who defined acceptable performance?
  • Who approved production deployment?
  • Who monitors changes in behavior?
  • Who can suspend the system?
  • Who is accountable for the outcome?

When organizations cannot answer those questions, they do not have governance. They have the illusion of governance via distributed activity and no focused accountability.

Leaders Must Establish AI Risk Appetite

Many organizations speak about AI principles. Fewer define their AI risk appetite.

Principles describe what an organization values. Risk appetite determines what it will permit.

Effective leadership demands decisions around where AI may operate autonomously, where it may only recommend, and where it should not participate at all.

That requires decisions about:

  • Which data AI systems may access.
  • Which decisions may be automated.
  • Which decisions require human approval.
  • How much uncertainty the organization will tolerate.
  • What level of explainability a use case requires.
  • How much authority and/or autonomy an AI agent may receive.
  • Which failures require immediate shutdown.
  • When efficiency cannot outweigh safety, fairness, privacy, or trust.

For example, a fraud-detection model and an autonomous industrial controller should not operate under identical tolerance levels. Neither should a marketing assistant and a system that affects employment or access to employee resources.

Optimally, governance reflects potential consequence.

That judgment cannot come exclusively from a technical scoring system. It requires leaders who understand the organization’s culture, strategy, customers, obligations, operations, and values.

Human Oversight Must Be Real

“Human in the loop” has become a very overused phrase in AI governance.

Organizations often point to human review as evidence that a system remains under control. But placing a person near an automated decision does not guarantee meaningful oversight. Nor does it even reflect reality in some cases. The sheer volume of what AI powered systems can generate make human intervention questionable.

The human may lack sufficient time and/or information to challenge the system. The interface may encourage automatic approval. Time pressure may make careful review impossible. Employees may assume that the model is more accurate than they are. Responsibility may become so distributed that nobody feels empowered to intervene.

Realistically, human oversight requires more than a final approval button.

The reviewer must have:

  • Enough context to understand the decision.
  • Enough authority to reject or override it.
  • Enough time to exercise independent judgment.
  • Enough technical literacy to recognize uncertainty.
  • Enough organizational protection to challenge the system.

Effectively, leaders must also consider automation bias. This is the natural tendency for people to trust the output of a system that appears objective, complex, or authoritative.

Ultimately, the human factor does not disappear when AI enters a workflow. It becomes more complicated.

Identity and Authority Form the AI Control Plane

Oddly, many AI governance discussions often focus on models and data while overlooking identity.

That is a serious mistake.

People build AI systems. Service accounts train them. Pipelines deploy them. Applications invoke them. Administrators change them. Agents increasingly act through them.

Every step involves an identity exercising authority.

An organization must know:

  • Who or what is acting.
  • Which identity the actor represents.
  • What authority that identity possesses.
  • What constraints exist on that authority.
  • Who granted that authority.
  • Whether the authority remains appropriate.
  • Whether the identity remains trustworthy.

This becomes especially important with autonomous agents. An agent may retrieve information, call APIs, create accounts, modify configurations, communicate with customers, or initiate actions.

An agent should not receive unrestricted access simply because an authenticated employee launched it.

It needs its own identity, constrained privileges, defined purpose, limited duration, attributable owner, and immediate revocation path.

The organization should preserve the full chain of authority:

  1. Human initiator
  2. Agent identity
  3. Delegated permission
  4. Tool invocation
  5. Impacted resource

Without that chain, the organization cannot distinguish legitimate automation from compromised autonomy.

The Adversary Gets a Vote

AI governance cannot operate only under the assumption that people and systems will behave as intended.

Adversaries couldn’t care less about the rules. They will manipulate models, compromise identities, poison data, steal credentials, exploit integrations, and misuse legitimate functionality.

They will search for the gap between what leaders think the system does and how it actually behaves under pressure.

This is where an adversarial mindset becomes essential.

Leaders should not ask only, “Does the system work?” In a headspace where there are no limits, they should also ask:

  • How could someone intentionally misuse it?
  • What happens if its data becomes untrustworthy?
  • Could a compromised identity change its behavior?
  • Can an attacker manipulate the human reviewer?
  • What authority could the system silently accumulate over time?
  • How would we detect subtle rather than catastrophic failure?
  • Can we stop it before we fully understand the incident?

Governance that assumes normal behavior is policy. And look at how effective policies are at stopping nefarious actors.

Governance that anticipates manipulation is a healthy step towards resilience.

AI Governance Must Become an Operating Rhythm

Organizations will not govern AI effectively through a policy, its annual review, or a one-time model assessment.

AI systems change. Their data changes. As do their users. Their integrations expand while authority grows. Their behavior may also shift as the environment around them changes. All of this is also happening at a rate of speed many organizations are not prepared for.

Governance must therefore become part of the organization’s operating rhythm.

Executive teams should receive recurring visibility into:

  • The inventory of blindly discovered (approved and unapproved) AI systems.
  • High-consequence use cases.
  • Detected changes in model behavior or authority.
  • Exceptions to established guardrails.
  • Third-party and supply-chain dependencies.
  • Identity exposure affecting AI environments.
  • Evidence that human oversight remains effective.

The objective is not to force leaders to review algorithms. One is to ensure that leadership understands where the organization has transferred decision-making power to machines and what could happen if that transfer fails. Another is to build a cadence of readiness preparation so that negative surprises are minimized.

Five Questions Executive Leaders Should Ask Now

Every executive team should be able to answer five questions:

  1. Where is AI already influencing decisions or actions across the organization?
  2. Who owns each material AI system and remains accountable for its outcomes?
  3. Which decisions may AI make autonomously, recommend to a human, or never influence?
  4. Can we trace every important AI action to a human or non-human identity and its delegated authority?
  5. Can we suspend the system quickly when its behavior, data, identity, or operating environment becomes untrustworthy?

If leadership cannot answer those questions, the organization is not ready to deploy and/or scale AI responsibly.

Leadership Is the Ultimate AI Control

Technical teams will remain essential to AI governance. Organizations need skilled architects, data scientists, security professionals, privacy experts, engineers, and legal counsel.

But expertise does not replace executive accountability.

AI will compress the distance between a leadership decision and its technological consequences. A policy choice can become an automated workflow. A risk tolerance can become a model threshold. A poorly governed identity can become an autonomous actor.

The organizations that succeed will not necessarily be those that adopt AI fastest.

They will be the organizations whose leaders understand where AI should have authority, establish clear boundaries around that authority, demand attributable ownership, anticipate adversarial behavior, and retain the ability to intervene.

We eventually learned that cybersecurity was not merely a technology problem.

We should not need another decade of incidents to learn the same lesson about AI.

AI Governance is now a critical leadership responsibility, it is also a leadership test.

The outcomes will reveal who studied and prepared for that test.