Think Like an Attacker: An AI-Driven CISO Playbook

Most security programs don’t fail because they lack tools. They fail because they defend what they meant to build, not what an attacker can actually exploit.

That’s the core idea behind Etay Maor’s advice in Dark Reading’s “Heard It From a CISO” series: train yourself (and your org) to think like an attacker. Not in a Hollywood way. In a practical way—how criminals gather clues, string small mistakes together, and pick the lowest-friction path to impact.

Now add one more ingredient: AI in cybersecurity. Attackers are already using AI to scale phishing, research targets, write malware variants, and automate reconnaissance. Defenders can’t respond with “more checklists.” They need faster learning loops: simulate attacks, detect weak signals, and reduce response time. That’s where an attacker mindset and AI-driven security operations fit together.

Thinking like an attacker beats checklist security

Answer first: Checklists harden systems against known issues; attacker thinking exposes how multiple “minor” gaps combine into a breach.

Checklists are comforting. Patch on schedule. Run a vulnerability scan. Enforce MFA. Rotate keys. It’s not that these are bad—they’re table stakes. The problem is what Maor calls out in practice: when teams rely on checklists, they often miss the human and operational side of intrusion.

Attackers don’t care if you’re “80% compliant.” They care that:

• A contractor’s credentials still work
• A public cloud bucket is misconfigured
• A helpdesk workflow lets them reset MFA
• A finance admin posts just enough on social media to enable social engineering

The reality? Breaches are rarely one exploit. They’re a chain. This is why Maor teaches non-technical students “Introduction to Hacking” (his course title is more polite). He’s not trying to create attackers—he’s trying to build defenders who can see chains, not isolated tasks.

Where AI changes the attacker mindset—for defenders

Answer first: AI compresses the time from “suspicion” to “scenario,” letting defenders test attacker paths quickly.

In practical terms, AI helps defenders do what strong red teams do—only more often:

• Generate plausible intrusion paths (misconfig → credential abuse → lateral movement)
• Summarize logs into narratives (what happened, in what order)
• Identify abnormal patterns across identity, endpoint, and cloud telemetry
• Suggest investigation pivots (related hosts, users, IPs, processes)

Used well, AI doesn’t replace expertise. It keeps you from missing the obvious because you’re drowning in noise.

AI-driven threat simulation: how to practice “attacker thinking” weekly

Answer first: To build attacker intuition, run repeatable, AI-assisted simulations that test identity, process, and human workflows—not just technical controls.

Maor’s best line is also the most uncomfortable: one of the best hacking tools is a hard hat and a yellow vest. Translation: humans and routines are exploitable.

So if you want a security program that’s resilient in 2026, don’t make “thinking like an attacker” a once-a-year tabletop. Make it a weekly muscle.

A practical cadence that works in real organizations

Here’s a rhythm I’ve found sustainable for teams that are already busy:

1. Weekly 30-minute “attack path review”

   • Pick one critical system (payroll, CRM, CI/CD, cloud admin)
   • Ask: “If I had one stolen password, what’s the fastest route to impact?”

2. Monthly micro purple-team exercise (2–4 hours)

   • Simulate one technique: OAuth abuse, helpdesk social engineering, token theft, SaaS misconfig
   • Validate detection and response, not just prevention

3. Quarterly executive scenario (60 minutes)

   • Focus on business decisions: shutdowns, comms, legal exposure, customer impact

AI tools can speed up preparation for all three. For example:

• Summarize your last 30 days of identity anomalies and propose top “investigation stories”
• Generate realistic phishing pretexts based on public company information (for training only)
• Build a “likely attacker route” map based on observed permissions and access paths

The win you’re aiming for

Don’t measure these exercises by how scary the scenario is. Measure them by:

• Mean time to detect (MTTD) in the simulation
• Mean time to respond (MTTR) to contain blast radius
• Number of handoffs required before action is taken (handoffs kill speed)
• Controls that failed silently (the most expensive failures)

AI in threat detection matters most when it reduces friction between “signal” and “decision.”

OSINT, oversharing, and the AI amplifier effect

Answer first: OSINT has always been powerful; AI makes it faster, cheaper, and more scalable—so defenders must treat public data exposure as a security control.

One of Maor’s teaching examples lands because it’s relatable: students discover how much you can infer from open source intelligence (OSINT)—social media posts, public profiles, and even payment app social graphs.

This isn’t theoretical. OSINT fuels:

• Spear phishing that references real projects and coworkers
• Business email compromise that mimics actual vendor relationships
• Password guessing using personal data patterns
• Physical intrusion aided by schedules, badges, photos, and office layouts

What AI adds to OSINT attacks

Attackers used to spend hours correlating scraps across platforms. Now they can:

• Summarize a target’s online footprint in minutes
• Generate tailored pretexts in the target’s language and tone
• Automate “relationship mapping” across names, handles, and organizations

If you’re running an AI in cybersecurity initiative, OSINT risk should sit next to vulnerability management. It’s part of the attack surface.

Actionable controls that don’t require a massive budget

• Lock down executive and finance team oversharing (speaking calendars, travel, org charts)
• Standardize helpdesk identity verification (no exceptions, no “I’m in a hurry” resets)
• Make payment/social apps private by default on corporate devices
• Run quarterly OSINT reviews on your organization (domains, exposed docs, leaked creds)

A memorable rule that holds up: If it’s public, assume it will be indexed, summarized, and weaponized.

Security teams need more than engineers (and AI makes that truer)

Answer first: Modern cybersecurity is interdisciplinary; AI expands both the attack surface and the defensive toolkit, so legal, comms, and operations must be part of the security system.

Maor’s perspective is blunt and correct: cybersecurity isn’t just IT anymore. It’s law, policy, marketing, fraud, HR, and executive decision-making.

AI accelerates this shift because it introduces:

• New data handling risks (training data leakage, sensitive prompts, shadow AI tools)
• New compliance questions (privacy, retention, auditability)
• New incident response demands (is the output trustworthy, reproducible, explainable?)

What this looks like inside an organization

If your AI security strategy lives only in the SOC, you’ll get blindsided. Strong programs set up cross-functional ownership:

• Security + IT: identity controls, logging, detection engineering
• Security + Legal: AI vendor terms, breach notification, data processing
• Security + HR: role-based training, insider risk, onboarding/offboarding
• Security + Comms: response messaging, customer trust, regulator posture
• Security + Finance/Fraud: payment abuse, invoice fraud, deepfake risk

AI can automate parts of security operations, but it also increases the cost of sloppy governance. If no one owns the “rules of AI use,” you’ll end up with shadow workflows and untraceable decisions.

A CISO-style checklist for using AI without fooling yourself

Answer first: Use AI to speed triage and simulation, but keep humans accountable for decisions, evidence, and control validation.

AI security tools can create a false sense of confidence if you treat them like oracles. Here’s a grounded approach I’d recommend for teams evaluating AI for security operations:

1. Start with one bottleneck, not “AI everywhere.”

   • Example bottlenecks: alert triage, phishing analysis, identity anomaly investigation

2. Require citations to internal evidence.

   • If an AI tool claims “lateral movement,” it must point to the logs/events that support it.

3. Define what the model is allowed to do.

   • Summarize and recommend? Fine.
   • Auto-isolate endpoints? Maybe, but with guardrails and approval tiers.

4. Test with adversarial prompts and messy data.

   • Real SOC data is incomplete. Your tool should fail gracefully.

5. Track accuracy with simple metrics.

   • False positives reduced?
   • Time-to-triage improved?
   • Analyst workload decreased?

6. Treat prompts and outputs as sensitive artifacts.

   • Prompts can leak incident details, customer data, or internal IP.

This keeps AI in cybersecurity useful—without turning it into a black box that no one can defend during an audit or incident review.

What to do next: build an attacker mindset into your AI strategy

Thinking like an attacker isn’t a vibe. It’s an operational habit. And AI is the fastest way to turn that habit into repeatable practice—simulations, detection tuning, and investigation narratives that your team can act on.

If you’re leading security, here’s the next step I’d take this quarter: pick one critical business process and map the attacker path end-to-end—identity, OSINT, human workflows, technical controls—then use AI-assisted analysis to test what you’d actually see in your logs. Do that three times and you’ll learn more than a year of status meetings.

The “AI in Cybersecurity” series is about practical outcomes: fewer successful intrusions, faster containment, and teams that can keep up with adversaries who don’t sleep. The question worth asking as you plan your 2026 security roadmap is simple: if attackers are using AI to move faster, what exactly are you doing to shorten your own decision cycle?