Part 1 of AI Powered Cybercrime
Cybercrime has historically had a skills bottleneck. Someone had to do the research, craft a believable story, write the lure, build the tooling, and then keep the victim engaged long enough for an outcome. Even for seasoned operators, that work takes time, and time is money. AI Powered Cybercrime – Facilitation.
Generative AI has changed the economics of that effort. It acts like a quality assistant that can draft, rephrase, personalize, and refine at machine speed. The net effect is not simply “smarter attackers.” It’s more adversaries that historically could not operate in this space. It is also a set of adversaries that can now perform at a higher baseline, scale larger, with fewer mistakes and more believable artifacts.
In this series, I use “facilitation” to describe the first-order impact of AI on cybercrime: removing friction across the attack lifecycle so that an individual attack becomes easier to execute, easier to adapt, and more likely to succeed.
Facilitation is where AI makes individual attacks better by lowering the skill barrier and improving content and/or persuasion quality.
The Facilitation Lens
A useful way to think about AI-enabled crime is as a pipeline. Attackers rarely win because they have one magic tool; they win because they can move smoothly from one stage to the next, recon, pretext, access, execution, and monetization. AI can assist at every stage, and it doesn’t need to be perfect. It only needs to be good enough to keep the process flowing through its journey.
For defenders, this creates a trap: many programs still focus on blocking discrete artifacts (one phishing email, one payload hash, one suspicious domain). Facilitation shifts advantage to the attacker because artifacts can be generated rapidly and with great volume; but the human processes and identity controls on the defensive side often remain static.
AI-powered malware: from coding to assembling outcomes
“AI malware” may inspire unrealistic notions of a fully autonomous super-virus. The more realistic, and more dangerous, reality is simpler: AI compresses development and iteration cycles. Instead of writing everything from scratch, adversaries can draft components, refactor quickly, generate variants, and troubleshoot faster. That matters because it reduces the time between idea and execution. It also empowers people that would not be operating in cybercrime without AI capabilities.
For defenders, the implication is that static signatures and one-off IOCs degrade faster. The same intent can show up as many slightly different implementations, and the “shape” of attacks changes just enough to evade brittle detection logic.
What can be done about this? Shift emphasis toward behavior and context. Instead of some static defense model we need to become more adaptable. If some payload dynamically changes, attackers will likely still need access to credentials, session tokens, the creation of persistence, or the exfiltration of data. Those are the slivers of opportunity where defenders have a chance of stable detection and containment. Given todays dynamic, the best place to shrink an attacker’s options is identity: the stronger and more tightly governed the identity boundary, the fewer places malicious tooling can successfully land.
Deepfakes: visual presence is no longer identity
Deepfakes move social engineering from “message deception” to “presence deception.” It’s one thing to spoof a sender name; it’s another to appear on a call as someone your team recognizes. That’s why deepfake-enabled fraud is so consequential: it attacks the human verification shortcuts we’ve relied on for decades, voice, face, and confidence.
The operational lesson is straightforward: “I saw them on video” is no longer a control. Nor is it a point of trust. A convincing presence can be manufactured, and group dynamics can be exploited to create social proof. The only reliable protection is to place high-risk actions behind verification steps that synthetic media cannot satisfy, out-of-band callbacks to known numbers, dual control for sensitive payments, and defined escalation rituals when urgency appears.
Social engineering: AI adds memory, consistency, and coordination
The biggest upgrade AI brings to social engineering is not grammar, it’s continuity. AI can maintain context over time, keep a persona consistent across messages and disparate systems, and pivot smoothly when a target makes adjustments. That capability turns many “one-and-done” lures into persistent conversations that wear down defenses.
This is why awareness training that focuses on typos and awkward phrasing is losing relevance. The tell is increasingly a process violation: a new payment path, a new channel, a sudden bypass of normal approvals, or an exception request that tries to compress decision time. If your employees know how to spot workflow bypass, they can defeat even polished, highly personalized lures.
Vibe hacking: weaponizing emotion to bypass analysis
Vibe hacking is the weaponization of emotion as a control bypass. Attackers don’t need you to believe every detail; they need you to act before you verify. Shame, urgency, fear, status, and belonging are some of the levers that move decisions faster than policy comes into play.
The countermeasure is not “tell people to be calm.” The countermeasure is building organizational escape hatches: clear permission to slow down, explicit escalation paths, and operational friction for irreversible actions. If urgency is treated as a trigger for verification, not a reason to move faster, we can turn the attacker’s primary advantage into a liability.
Short term reset
If you want one practical takeaway from facilitation, it’s this: identity and workflow integrity are choke points. AI can generate unlimited persuasion and/or manipulation artifacts, but we have to force it to cross an authorization boundary somewhere.
Start by identifying the three most irreversible workflows in your organization, for example pick from a pool like this one: payments, vendor banking changes, payroll updates, privileged access grants, or large data exports. Then ensure those workflows have step-up verification that cannot be satisfied by sense of urgency, polished messaging, or synthetic media. Finally, run a short blind red-team exercise on a deepfake or coercion scenario and measure how long it takes the organization to verify and contain. Blind = this must mimic reality.
Key takeaways
- Assume high-quality lures and retrain owners monthly to reduce fraud loss and downtime.
- Gate privileged actions and enforce out-of-band checks always; the goal is to prevent unauthorized transactions.
- Detect behavior shifts and tune telemetry regularly to cut dwell time and response costs.
- Standardize escalation and drill managers quarterly; the goal is to reduce coercion-driven errors.
- Institutionalize dissent and review exceptions monthly to avoid governance blind spots and audit fallout.
Part 2 of AI Powered Cybercrime