25+ years in IT audit and cybersecurity. Writes on auditing AI systems for technology risk, internal audit, and governance practitioners.
Control selection is downstream of classification, and classification is a governance output. Where that chain is missing, what looks like a control framework is really just a control list.
"AI security" collapses two different problems into one conversation: attacks on the model itself, and what using AI does to everything you already have. IBM's 2026 data says most incidents are the second kind, and this piece argues you should scope for it first.
A rules engine, a credit scoring model, and a language model share a label and almost nothing else. This piece argues the deterministic/probabilistic split, not impact or autonomy, is what should decide your testing methodology.