Guide

AI Governance in Healthcare and Financial Services: Best Practices

The governance gap in regulated industries is now quantified: 88% of health systems use AI in some form, yet only 18% have mature governance structures, a 70-point gap (HFMA, August 2025). Financial services trail closely: 59% of finance leaders report active AI use while still struggling with model risk management and explainability. Both sectors face active enforcement: the CFPB has explicitly rejected the 'black box' defense for credit denials, and the ONC's HTI-1 rule mandates algorithmic transparency across health IT.

Key takeaways

AI governance combines frameworks, policies, and runtime controls that keep AI compliant, auditable, and operationally accountable
Healthcare compliance centers on HIPAA, FDA device oversight, and ONC transparency mandates; financial services on SR 11-7, FINRA oversight, and DORA resilience requirements
The most common failure: treating governance as documentation rather than operational control enforced at runtime
Five shared pillars: risk classification, data governance, transparency, continuous monitoring, and human oversight
The safety stakes are real: a 2025 study of 691 FDA-cleared AI/ML devices found 5.8% were recalled 113 times, primarily for software issues, with 489 adverse events reported, including one death

What are the five pillars both sectors share?

Risk classification, tier every AI use case by impact (clinical decision support is not scheduling; credit decisioning is not document search) and scale controls to tier
Data governance, PHI and customer financial data protection enforced at the AI boundary, not just in databases
Transparency and explainability, decision lineage proportionate to stakes, ready for the regulator who rejects "black box"
Continuous monitoring, model and agent behavior watched in production, with drift and exception detection
Human oversight, required checkpoints for high-stakes decisions, with the checkpoint itself logged

What does best practice look like in healthcare?

HIPAA-aligned controls in the AI request path (PHI detection/redaction, least-privilege access for agents and copilots); FDA-aware classification of clinical AI; ONC transparency obligations mapped to evidence the system produces automatically; and an inventory covering EHR-embedded vendor AI, not just internally built tools.

What does best practice look like in financial services?

SR 11-7 discipline extended to LLMs and agents (inventory, monitoring, documentation); fair-lending decision lineage for any credit-adjacent AI; FINRA/SEC conduct controls on AI-generated communications; DORA-grade operational resilience (routing, failover) for AI dependencies; and third-party AI brought under the firm's own controls via proxy-based governance.

How do you build a framework that scales across both?

The unifying move is infrastructure: one runtime control plane enforcing sector-mapped policies across every model, app, and agent, generating audit evidence automatically. Trussed AI provides exactly this for regulated industries, drop-in proxy, sub-20ms enforcement, framework-mapped policy templates (HIPAA, SR 11-7, NIST AI RMF, EU AI Act), with customers reporting ~50% less manual governance workload.

Frequently Asked Questions

Can one governance framework serve both a health plan and its financial operations? Yes, the pillars and enforcement layer are shared; only the policy mappings (HIPAA vs. SR 11-7 et al.) differ.

What's the fastest credibility win with regulators? Per-decision evidence on demand: show that any AI-influenced outcome can be reconstructed, model, version, data, policy results, in minutes.

Where do programs in these sectors most often fail? Vendor AI: EHR-embedded models and fintech vendor tools that sit outside the inventory and outside enforcement. Bring them in scope first.

Related resources