Enterprise AI Governance Framework
Design and operationalization of integrated governance frameworks across Traditional AI, Generative AI, and Agentic AI systems.
Includes:
- governance operating models
- policies and standards
- AI inventories
- risk taxonomy
- control libraries
- inherent risk intake questionnaires
- lifecycle governance workflows
- approval and escalation structures
- governance reporting
- monitoring structures
- evidence management
AI governance health check
A structured governance assessment designed to evaluate governance maturity, operational controls, AI visibility, and risk management practices.
Typical Duration: 4–6 weeks depending on organizational complexity.
Focus areas
- AI inventory visibility
- governance structures
- GenAI usage
- Shadow AI exposure
- risk management practices
- third-party AI dependencies
- monitoring capabilities
- documentation maturity
- governance gaps and remediation priorities
The deliverable includes a findings report and a prioritised governance improvement roadmap.
Third-party ai governance & vendor due diligence
Governance and risk oversight for external AI vendors, AI-enabled platforms, and AI service providers.
Includes:
- vendor AI risk assessments
- AI procurement governance
- transparency reviews
- contract review support
- training data governance considerations
- output ownership considerations
- vendor monitoring expectations
- cross-border data considerations
- AI service dependency reviews
Responsible AI training & operational enablement
Practical governance training designed to help organizations operationalize Responsible AI across business, technology, compliance, legal, and risk teams.
Topics Include:
- Responsible AI
- Generative AI governance
- Agentic AI risks
- AI lifecycle governance
- human oversight
- third-party AI governance
- operational governance
- governance workflows
- monitoring and accountability
