AI is embedded everywhere in cybersecurity – in how we detect threats, respond to incidents, and summarise complex events. But there’s a creeping issue that too few are prepared to face: AI hallucination.
It sounds like science fiction. It’s not.
When your security tool starts fabricating threat indicators, misinterpreting context, or offering confidently wrong remediation advice – you’ve got more than a glitch. You’ve got a liability. And in security, that liability scales.
This isn’t just about the future of AI. It’s about your current stack misfiring in real time.
Can AI Tools Become a Security Risk? Absolutely.
The tools themselves aren’t malicious, but how we use them can be because the risks are baked into their architecture and magnified by how they’re deployed:
- False confidence: Hallucinated IOCs or fabricated domains presented as fact.
- Privilege misuse: Over-automated tools escalating actions without oversight.
- Poorly defined context: Ambiguity + AI = unpredictable output.
Even a minor misstep can have significant consequences – from blocking internal systems to misreporting an incident or escalating a false alarm that derails your team.
In regulated sectors or high-stakes environments, the cost isn’t theoretical. It’s operational, reputational, and sometimes even legal.
How AI Hallucinations Happen and Why They’re Dangerous
Hallucinations occur when large language models confidently generate something that simply isn’t true.
In cybersecurity, that looks like:
- Fake IOCs in your threat feed
- Incorrect remediation steps, like shutting down the wrong firewall port
- Fabricated incident summaries including non-existent hosts or attack vectors
Let’s be clear: this isn’t a typo. It’s fiction presented as fact and your SOC acting on it.
🔍 Real-world miss: One platform hallucinated a sender domain during phishing triage. The result? An internal business partner was blocked. Recovery took days. Trust took longer.
AI’s Hidden Vulnerabilities Run Deeper Than Hallucination
Hallucination is just one part of the problem. The full threat surface includes:
- Prompt injection attacks: AI systems manipulated via malicious input.
- Training data leakage: Sensitive data resurfacing in model outputs.
- Supply chain contamination: Poisoned training datasets slipping in unnoticed.
If your AI tools are trained on anything unverified, unlabeled, or externally sourced, you’ve potentially invited threats in through the front door. This is why robust Supply Chain Threat Detection is non-negotiable.
And unlike rule-based systems, most LLMs offer no real audit trail. You’re often left guessing how a conclusion was reached or why it was hallucinated in the first place.
The Specific Risks of Generative AI in Security
Let’s break this down for CISOs:
- Misplaced trust: Just because an output sounds smart doesn’t mean it’s right.
- Data privacy exposure: Fine-tuning with real user data can backfire.
- Model drift: Today’s precision is tomorrow’s mistake without regular tuning.
- Shadow AI: Unsanctioned tools running outside IT’s line of sight – often with access to sensitive data.
You’re not just managing threats anymore. You’re managing tools that might create them.
So, What Should CISOs Do?
Smart leaders assume the breach. Smarter ones assume the AI might be wrong. Here’s how to stay ahead:
✅ Put policy first: Don’t wait for a mistake. Define usage rules, audit processes, and clear responsibilities for AI tools.
✅ Keep humans in the loop: Never fully automate high-stakes actions. Use AI for speed, not autonomy.
✅ Prioritise explainability: Choose tools that justify their output with evidence, not just a paragraph of polished text.
✅ Test with intent: Regularly feed known-good and known-bad scenarios into your AI tools. Look for drift. Spot the hallucinations before they matter.
✅ Secure the AI pipeline: If you’re using external data sources, make Supply Chain Threat Detection part of your AI governance model.
Looking Forward – AI Is Here, But Trust Isn’t Free
The speed of AI adoption is accelerating, but so is the misuse – by attackers, by vendors, and by your own users.
If you don’t define how your organisation will handle hallucinations now, they’ll define your next breach.
AI hallucinations aren’t a futuristic threat. They’re a current risk. In security, the cost of false confidence is high – especially when it comes dressed in the language of certainty.
CISOs who ignore hallucination risk aren’t streamlining operations. They’re gambling with trust.
Choose tools that explain themselves. Test them aggressively and never outsource responsibility to a model trained to please.
