Why Traditional Security Misses AI Threats

Most organisations assume their existing security posture extends to AI. It doesn't — and the gap is significant.

What Your Security Stack Can't See

Consider what conventional enterprise security tools actually protect:

• ◆SIEM tools monitor network traffic and log events — they can't detect a model returning biased outputs
• ◆EDR solutions watch for malicious processes — they can't see a poisoned training batch
• ◆WAFs block known attack signatures — they don't recognise adversarial examples designed to fool vision models
• ◆DLP tools watch for data exfiltration patterns — they miss model inversion attacks that reconstruct training data

Building AI-Aware Security

Effective AI security requires a different set of controls — layered on top of, not instead of, your existing stack:

• ◆Input monitoring — Statistical analysis of inputs to detect distributional anomalies
• ◆Output validation — Sanity checks on model outputs before they affect decisions
• ◆Model performance tracking — Continuous monitoring for accuracy degradation
• ◆Access control on training pipelines — Treating training data like production credentials
• ◆Model versioning and rollback — The ability to revert to a previous model state quickly