AI Governance & Risk
Responsible AI that works in real organizations. We help you establish clear decision rights, embedded controls, and measurable evaluation so delivery accelerates, approvals get easier, and executives stay confident.
- ISO/IEC 42001 and EU AI Act readiness
- Internal risk appetite alignment
- Complements Legal, Compliance, Security, and Model Risk
The Challenge
AI adoption stalls when governance is either too vague ("be responsible") or too heavy (paperwork and approvals that teams route around).
Common failure modes include missing model inventory, inconsistent documentation, unclear ownership, ad-hoc vendor risk, and no testable safety/quality criteria.
Our Approach
We implement a pragmatic Responsible AI control plane: policies and decision rights, a control library, and evaluation practices that are proportionate, testable, and integrated into delivery.
The goal is simple: faster approvals with better evidence.
Key Outcomes
Core Deliverables
Governance Operating Model
The organizational structure that makes AI governance work—defining decision forums, escalation paths, and clear ownership for AI systems and data.
- Model inventory and documentation standards
- Third-party/provider and data boundary governance
Multinational Energy Corporation
The Context
Implemented a governance control plane to classify and track high-risk AI systems automatically.
The Outcome
Result: Full inventory visibility and audit-ready evidence generation embedded in CI/CD chains.