The dangerous failure is not AI that refuses. It is AI that reads more expert than the truth, and in a regulated setting that is exactly what a reviewer waves through. We build the controls that catch it, before anyone sees the output.
A model writing your local content drifts to the patterns it saw most in training, so a system generating South African compliance text reaches for the GDPR clause, not the POPIA one. The wrong answer reads more sophisticated than the right one, which is why a reviewer skimming is reassured by exactly the text that would embarrass them. Courts and regulators are now sanctioning hallucinated citations across jurisdictions. "AI-generated" is no longer a neutral phrase.
It runs continuously, not once, because models change under you, retrieval goes stale, and the law moves. Each stage maps to how enterprise AI assurance is actually done: test and evaluate, ground, gate, and monitor.
Checks that catch GDPR concepts wearing POPIA citations, and sections that do not exist in the Act, with frozen baselines.
Human approval bound to a content hash, so approving one version cannot silently approve another. Spend governed per unit.
A staged pipeline with QC gates, and a machine-readable contract between systems instead of one parsing another's prose.
From our own codebase. It is why we build gates that fail closed, not prompts that ask nicely.
A practice that names its own limits is the one a legal gatekeeper trusts. Here is what we will not do, and what we do instead. It is a selling point, not a disclaimer.
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