AI Model Poisoning And Board Governance: Breaking The Rule

Mukul Arora drives Global Cloud Security, Privacy & AI Governance, GRC & Risk Management Consulting & Advisory at Persistent Systems Ltd.

​Three years ago, I sat in a board meeting where a major enterprise proudly presented its new AI-powered fraud detection system. The slides were impressive, and the demo was flawless. The board approved the investment in under 40 minutes. But no one asked what data the model learned from. I’ve been in enough boardrooms since then to know that meeting wasn’t an exception. It was the rule.

The Questions That Matter Most

We’ve spent considerable energy teaching boards to ask about AI adoption: what it can do, how much it costs and what the competitive risk of not adopting it looks like. But most don’t ask the questions that matter most: Can we trust what this model has learned, and how can we confirm the model’s learnings are correct?

This points to one of the most consequential and least understood risks in enterprise AI today: model poisoning, or the deliberate corruption of the data an AI system learns from.​

What Model Poisoning Looks Like

Model poisoning doesn’t announce itself. It doesn’t trigger your SIEM. It doesn’t show up in your threat intelligence feed. It simply teaches your AI to be wrong consistently, invisibly and at scale.

Consider what that means in practice. Your AI-powered fraud detection system, trained on subtly manipulated data, begins approving the very transactions it was built to block. Your cybersecurity AI classifies active malware as safe because its training set was compromised months before deployment. Your AI hiring tool systematically filters out the right candidates because someone poisoned its learning data upstream. By the time the damage surfaces, the decisions have already been made, the transactions already processed and the risks already materialized.

This is what makes model poisoning uniquely dangerous. It exploits the time between when AI systems are built and when their failures are discovered.