Regulated AI Architecture Brief
Implementing AI in regulated industries with confidence
How healthcare, finance, and high-compliance teams can move from experimentation to governed AI workflows.
Regulated AIApril 2, 20266 min read
Problem
Regulated AI programs stall when reviewability, accountability, sensitive-data handling, and evidence are treated as documentation tasks after design decisions are made.
Architecture principle
Design review, evidence, access, and escalation into the workflow before production so governance is part of system behavior.
Control implications
- Classify workflows by impact and data sensitivity
- Require human review for high-impact outputs
- Separate AI recommendations from approved business records
Architecture and implementation guidance
- Design reviewable workflows rather than fully autonomous paths for high-risk use cases.
- Implement role-based access, logging, retention, and exception handling.
- Separate advisory AI outputs from approved business records.
- Use governance checkpoints before production rollout.
Design tradeoffs
- Automation depth versus human accountability
- Data richness versus minimization and privacy
- Uniform controls versus risk-based workflow treatment
Evidence to design for
- Review and approval records
- Context and output logs
- Access records
- Governance checkpoint evidence
Implementation artifacts
- Regulated workflow reference architecture
- Review and escalation model
- Evidence map
- Control readiness plan
What leadership should decide
- Which workflows may automate decisions
- Where human approval remains mandatory
- What evidence is required for assurance and oversight
What engineering should build
- Reviewable workflow states
- Role-based access and logging
- Evidence capture and exception handling
Continue from architecture thinking to action
Use the related service to understand engagement scope, the industry path to add operating context, and Evidence to inspect how IKEVAR turns architecture ideas into reviewable technical artifacts.
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