Explainability as an architecture requirement in regulated environments
Life sciences, banking, and healthcare regulators ask how an answer was derived, who could see the inputs, and whether you can demonstrate both months later.
The requirement this answers
“Deliver AI and data solutions within regulated, audited environments”
Design the evidence trail alongside the capability, because retrofitting it costs several times more.
Where I have done this
AML transaction monitoring at Bank of America under OCC and FinCEN obligations. Liquidity risk reporting during the post-2008 regulatory rebuild. HIPAA-bound canonical models at a health insurer. Clinical operations at a leading CRO under FDA 21 CFR Part 11 and ICH GCP E6(R3).
The pattern is consistent. The regulator's question is about lineage and control rather than accuracy claims.
What that means for AI design
Retrieval inherits source access control, so an answer never surfaces material the requester could not otherwise open. Every AI-executed step carries an eval and an audit record. AI components are treated as models under model-risk governance: documented, monitored for drift, with a named owner.
ICH GCP E6(R3), effective July 2025, requires continuous auditable sponsor oversight of delegated activity, which makes connected data infrastructure and real-time visibility an operational requirement.
A useful side effect
Regulated constraints tend to produce better architecture. The lineage built for an auditor is the lineage that lets an agent explain itself. The classification built for privacy is the classification that prevents permission leakage through a vector store. The compliance work and the AI-readiness work overlap substantially.
How I apply it
- Map regulatory obligations to specific architecture constraints in writing, up front
- Generate audit evidence automatically from lineage and execution metadata
- Treat every AI component as a model under model-risk governance
- Prove parity across a full business cycle, including period-end, before decommissioning
What good looks like
- Audit requests are answered from the platform rather than by a manual exercise
- Access decisions are traceable to a policy
- Drift and eval results are reviewed on a governance cadence
- Compliance is consulted at design time and is not surprised at go-live