Why Security Training Keeps Solving the Wrong Problem

If you judged modern cyber risk purely by headlines, you’d think we were already living in a sci-fi thriller.

Every week brings fresh warnings about AI-powered cyberattacks: autonomous agents conducting espionage, self-evolving malware rewriting itself in real time, and ransomware campaigns supposedly driven “80% by AI.” Vendors promise intelligent adversaries that never sleep, never miss, and constantly adapt faster than defenders can react.

And yet, when you talk to people actually responding to incidents, a very different picture emerges.

Most real-world breaches still start the same way they did years ago: phishing emails, stolen credentials, exposed services, unpatched systems, misconfigurations, and identity abuse. The tactics are familiar. The tools are proven. The difference isn’t creativity—it’s scale. Attackers win by being fast, cheap, and relentless, not by inventing novel techniques. Check out my blog, The AI Threat Narrative Is Outrunning Reality to read my take on this matter in further detail.

I argue this disconnect between narrative and reality matters, because it’s reshaping how organizations train their people—and not in a good way.

The AI Threat Narrative Isn’t What Practitioners Are Seeing

Veteran researchers have been increasingly vocal about the gap between hype and evidence. Marcus Hutchins, best known for stopping WannaCry, has repeatedly pointed out that many “AI malware breakthroughs” amount to clever demos rather than operational advantages. They’re interesting experiments, not game-changing weapons.

The now-infamous paper claiming that 80% of ransomware was AI-driven is a perfect example. Despite being briefly associated with MIT infrastructure, the research raised immediate red flags across the security community. Key questions went unanswered: What qualified as “AI-driven”? Was the data measuring attacker behavior or marketing classifications? How was attribution determined?

The lack of methodological rigor made the work look more like demand generation than defensible research—and it was eventually removed. But the damage was already done. The headline lived on, reinforcing fear and shaping perceptions long after the evidence collapsed.

Security training programs absorbed that fear wholesale.

AI Increases Efficiency, Not Autonomy

This is the nuance training often misses.

AI does make attackers more efficient. It can generate phishing lures faster, help refine exploit code, automate reconnaissance, and lower the barrier to entry for basic attacks. It accelerates what already works.

What it does not do—at least today—is operate as an independent, agentic adversary. There is no credible evidence of AI systems being deployed that can autonomously explore environments, adapt objectives, improvise complex strategies, and reliably execute multi-stage campaigns without human supervision.

The mental model people default to is cinematic: the Terminator navigating unfamiliar terrain, learning from resistance, and creatively pursuing its mission. That’s what “AI-powered attacks” feel like in marketing copy.

But today’s AI systems are brittle. They require constant prompting, break under ambiguity, hallucinate confidently, and struggle with long-horizon reasoning. Attackers know this. That’s why serious threat actors prioritize reliability over novelty. A boring attack that works beats a clever one that fails.

How Training Ends Up Solving the Wrong Problem

Here’s where the real risk emerges.

When training programs accept inflated AI narratives at face value, they start preparing defenders for threats that don’t meaningfully exist yet—while neglecting the ones that cause the most damage today.

1. Employees get drilled on spotting “AI-generated phishing” while still reusing passwords. Security teams attend workshops on hypothetical autonomous malware while MFA coverage remains incomplete. Cloud engineers worry about rogue AI agents while basic identity permissions sprawl unchecked.
2. Budgets follow the same pattern. Organizations pour money into AI-branded tools and futuristic training modules while fundamentals like patching discipline, access controls, monitoring, and segmentation remain under-resourced.
3. Preparing for the wrong war doesn’t make you safer. It leaves you exposed while the real one keeps being fought—quietly, repeatedly, and successfully.

The Real AI Problem Is Governance, Not Attackers

Ironically, the most immediate AI risks organizations face today aren’t offensive—they’re internal.

Data leakage through AI tools, unsafe model use, regulatory exposure, shadow AI deployments, and unclear accountability are the problems actually keeping CISOs up at night. These are governance, compliance, and operational challenges—not agentic adversaries inventing zero-day campaigns on the fly.

Security training should reflect that reality.

Instead of chasing speculative threats, programs need to double down on what attackers demonstrably exploit: social engineering, identity abuse, living-off-the-land techniques, supply-chain exposure, and cloud misconfigurations. These are the techniques driving real incidents, real losses, and real downtime.

If AI isn’t the dominant threat yet, why does so much of our training assume it is?

Resetting the Training Paradigm

Effective security education isn’t about predicting the most dramatic future—it’s about addressing the most probable present. Until attackers actually deploy autonomous, creative AI systems at scale, training should stay grounded in evidence, not headlines.

Because hype may sell products—but realism is what prevents breaches.

Learn how CyberEd.io helps organizations reset the AI threat training paradigm and refocus security education on what actually works.