AI Governance Framework Development
Design the decision rights, governance bodies, lifecycle gates, risk tiers and evidence model required to govern enterprise AI consistently.
What this capability solves
Organizations often adopt AI faster than governance can define ownership, acceptable use, risk thresholds and release accountability. A governance framework creates one operating model across business, technology, legal, risk, security and privacy.
Technology is implemented as an operating capability: architecture, integration, governance, assurance, people, procedures and measurable outcomes are designed together.
Capability model
Modular building blocks allow the scope to start with a focused pilot and expand into an enterprise operating model.
Governance Structure
Board/executive oversight, AI committee, accountable owners, risk functions and operational working groups.
AI Inventory
Use-case, model, data, agent and provider inventory with ownership and lifecycle status.
Risk Tiering
Classification based on impact, autonomy, data sensitivity, affected users and business criticality.
Lifecycle Gates
Concept, design, build, test, release, material change and retirement checkpoints.
Decision Rights
Approval, exception, residual-risk acceptance and escalation authority.
Evidence Model
Required assessment, testing, monitoring and approval evidence by risk tier.
How the capability fits together
Final topology, control placement and deployment model are validated during discovery and detailed design.
Controls & governance
- Named business and technical owner
- Use-case risk classification and approval gates
- Data provenance, minimization and access control
- Human accountability for high-impact outcomes
- Security and privacy-by-design controls
- Versioned model/prompt/agent configuration
- Pre-release evaluation and red-team gates
- Continuous monitoring, incident and change control
- Audit-ready evidence and management reporting
Priority use cases
- Enterprise AI governance launch
- Regulated/high-impact AI portfolio
- Agentic AI governance
- Multi-business AI operating model
- ISO/IEC 42001-aligned governance
- AI board reporting
Key deliverables
- AI governance charter
- Roles and RACI
- Risk taxonomy
- AI inventory schema
- Lifecycle gate standard
- Approval/exception matrix
- Committee reporting pack
Integration considerations
- Enterprise IAM and workload identity
- Data lake/warehouse and vector/RAG platforms
- Model/API providers and private models
- Application/API integration layer
- MLOps/LLMOps/AgentOps and observability
- SIEM/SOAR and security tooling
- GRC, privacy and evidence repositories
- ITSM/BPM and business workflow systems
Phased delivery
Each phase ends with evidence, acceptance criteria and a decision gate before broader scale-out.