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AI GovernanceCase Study
EU AI Act & ISO/IEC 42001 Readiness
Operationalize EU AI Act and ISO/IEC 42001 readiness by establishing an AI Management System (AIMS) with policy-as-code enforcement, clear ownership, and continuous evidence in production.
Executive Outcome
01
Versioned, testable policies and AIMS processes that allow teams to validate constraints early in delivery, reducing late-stage surprises and waiver sprawl.
02
Consistent runtime enforcement through declared policy enforcement points, preventing unapproved requests, data flows, or tool actions from reaching models.
03
Audit-ready execution where evidence is produced automatically as a byproduct of enforcement and tracing, reducing manual review overhead.
Engagement focus
AIMS implementation and runtime governance aligned to the EU AI Act and ISO/IEC 42001.
Context
In regulated environments, GenAI delivery velocity can outpace the capacity of manual reviews and checklist-based governance. The objective is to shift governance left and down into the runtime, so control enforcement and evidence generation are systematic, consistent, and repeatable across teams and providers. This turns audit readiness from a periodic effort into a continuous property of production operations. ISO/IEC 42001 frames this as an AI Management System (AIMS) with defined scope, ownership, and continuous improvement.
The Challenge
- 01Manual reviews did not scale to the pace and breadth of GenAI delivery, creating bottlenecks and inconsistent outcomes.
- 02Policy enforcement varied across teams and providers, producing uneven controls, inconsistent exception handling, and control drift over time.
- 03Audit trails were reconstructed from disjointed logs, creating evidence gaps and high operational overhead.
- 04Decentralized model access practices increased shadow AI risk and reduced central visibility into usage, data exposure, and tool permissions.
Approach
- →Defined the AIMS scope, organizational context, and leadership commitments, then translated them into an actionable control taxonomy and operating model.
- →Defined a governance-as-code operating model with versioned policy definitions, control taxonomy, and clear ownership for policy lifecycle management.
- →Established declared policy enforcement points for model and tool traffic and standardized exception handling.
- →Introduced a Policy-stamped request envelope to bind request context, applicable policy version, enforcement decisions, and key signals into a traceable record.
- →Established a control evidence model that links each control to enforcement signals, decision records, and sampling-ready artifacts.
- →Implemented evidence retention patterns so enforcement decisions and runtime signals are captured in an immutable, joinable form for audit sampling and incident analysis.
- →Built reporting and sampling views that map obligations to controls, evidence sources, and exception handling for EU AI Act and ISO/IEC 42001.
Key Considerations
- Schema and policy discipline require upfront alignment with application teams and clear change management to avoid friction and bypass patterns.
- A shared enforcement layer becomes a critical service and must be operated with reliability, latency, and availability expectations.
- Policy authoring and maintenance require a dedicated capability, review practices, and controlled rollout to prevent policy drift and breaking changes.
Alternatives Considered
- ✕Manual approval gates: rejected as non-scalable and prone to inconsistent outcomes under volume.
- ✕Library-based controls: rejected because they can be bypassed, drift across implementations, and fail to provide central evidence.
Representative Artifacts
01AIMS Scope and Context Statement (scope boundaries, roles, decision rights)
02Control taxonomy mapping for EU AI Act and ISO/IEC 42001
03Statement of Applicability style control register (applicability, implementation status, evidence sources)
04Policy repository structure and control taxonomy (safety, sensitive data, topic bounds, tool permissions)
05Policy-stamped request envelope specification
06Evidence retention and audit record model
07Exception management and waiver workflow
08Compliance reporting and sampling dashboard
Acceptance Criteria
Verified that policy enforcement is applied consistently to production model and tool traffic through declared enforcement points.
Verified that policy changes are versioned, reviewable, and promotable through defined release discipline.
Verified that blocked or flagged requests generate a complete enforcement record suitable for audit sampling and incident analysis.
Verified that developers receive actionable feedback on policy violations in the delivery workflow.
Verified that AIMS scope, ownership, and control applicability are documented, versioned, and linked to evidence sources used for sampling and continuous reporting.
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