INSIGHTS
Governance Patterns for Regulated AI Systems
Enforced controls—not policy PDFs—for regulated AI delivery.
Policies that are not enforced in software do not survive contact with production. Regulated teams need controls that are testable: who approved, on what evidence, under which model and data policy version. I design patterns that map regulation to workflow structure.
Audit trails as data
Treat audit events as first-class records—immutable, correlated, queryable. Separate “model output” from “decision to act” when the latter has legal weight.
Retention and access to audit stores must themselves be governed; otherwise you trade one risk for another.
Gates that cannot be bypassed
High-risk actions route through mandatory review steps in the orchestration layer—not a UI hint. Bypass paths are explicit, logged, and rare.
Safety checks in the graph
Content filters, PII detectors, and policy classifiers belong as explicit nodes with failure semantics. Silent failures are unacceptable in regulated flows.
Define fail-open versus fail-closed per check: emergency read-only modes differ from customer-facing generation.
Evidence packs for releases
Bundle evaluation summaries, model cards, data-flow deltas, and sign-off records per release. Regulators and internal risk committees ask for narrative plus artefacts—automate the artefact side so narrative stays accurate.
Governance is architecture when the stakes are high. I help align legal and engineering artefacts so reviews are substantive, not performative.
If you want help applying this to your architecture, book a strategy call or an architecture review.
Tags: governance · regulated · compliance · safety