Case Study
Value Discovery & Portfolio
Prioritize GenAI investments with defensible stop criteria and risk-aware gating, using resources on initiatives that can scale.
Read Case Study
Responsible AICase Study
Responsible AI Runtime Governance
Achieve audit readiness through policy-as-code and runtime enforcement, making compliance a continuous property rather than a manual gate.
Executive Outcome
01
Versioned, testable policies that allow teams to validate policy compliance early in delivery and reduce late-stage surprises.
02
Consistent runtime enforcement through declared policy enforcement points, preventing unapproved requests from reaching models.
03
Audit-ready execution where evidence is produced automatically as a byproduct of enforcement, reducing manual review overhead and reconstruction effort.
Engagement focus
Governance-as-code framework for audit-ready GenAI operations.
Context
In regulated environments, GenAI delivery velocity can outpace the capacity of manual review processes. The objective was to invert the bottleneck by making policy enforcement and evidence generation systematic at runtime and in the release process.
The Challenge
- 01Manual reviews did not scale to the pace and breadth of GenAI experimentation.
- 02Policy enforcement varied across teams and providers, creating uneven controls and inconsistent exception handling.
- 03Audit trails were reconstructed from disjointed logs, creating gaps and high operational overhead.
- 04Decentralized access practices increased shadow AI risk and reduced central visibility.
Approach
- →Defined a Governance-as-Code operating model with versioned policy definitions and clear ownership for policy lifecycle management.
- →Established a unified gateway as the policy enforcement point for model traffic to apply consistent controls at runtime.
- →Introduced a policy-stamped request envelope to bind request context, applicable policy version, and enforcement decision into a traceable record.
- →Implemented evidence retention patterns so enforcement decisions and key signals are captured in an immutable, joinable form for audit and incident analysis.
Key Considerations
- Schema and policy discipline require upfront alignment from application teams and disciplined change management.
- A shared enforcement layer becomes a critical service and must be operated with reliability expectations.
- Policy authoring and maintenance require dedicated capability and review practices.
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 or drift across implementations.
Representative Artifacts
01Policy repository structure and control taxonomy (safety, sensitive data, topic bounds)
02Policy-stamped request envelope specification
03Evidence retention and audit record model
04Exception management and waiver workflow
05Compliance reporting and sampling dashboard
Acceptance Criteria
Verified that policy enforcement is applied consistently to production model 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.
Verified that developers receive actionable feedback on policy violations in the delivery workflow.
Continue Exploring
Other Case Studies
0%