We design policies and controls covering accountability, transparency, fairness, and data protection across the AI model lifecycle. Roles, responsibilities, and approval gates are defined before any model reaches production. The framework is built to align with SDAIA's AI ethics principles and PDPL requirements.
What's included
- Draft an enterprise-wide AI usage policy defining permitted and prohibited use cases and the data classes allowed to reach a model.
- Stand up a central model registry documenting every AI model in use, its owner, training data, purpose, and lifecycle stage.
- Classify AI use cases by risk tier (low, medium, high) and map each tier to proportionate oversight controls.
- Define roles, a RACI accountability matrix, and mandatory approval gates before any model is promoted to production.
- Establish human-in-the-loop controls with intervention and kill-switch criteria for high-impact decisions.
- Align the framework with SDAIA's AI Ethics Principles and PDPL data-protection obligations.
Methodology & standards
Scoping and inventory: catalogue existing and planned AI systems, and identify stakeholders and regulatory obligations.
Gap assessment: benchmark the current state against SDAIA principles and ISO/IEC 42001, documenting governance gaps.
Framework design: author the policies, standards, procedures, model registry, risk matrix, and approval gates.
Activation and enablement: train teams, stand up the AI governance committee, and embed controls into the development lifecycle.
Periodic review: governance KPIs and a continuous update mechanism that tracks system and regulatory change.
Deliverables
- An approved AI governance policy ready for executive sign-off.
- A populated central model registry covering existing systems.
- A risk-classification matrix with the controls tied to each tier.
- A RACI matrix and a charter for the AI governance committee.
- A procedures handbook for approval gates and pre-production human oversight.
- A roadmap for alignment with ISO/IEC 42001 and SDAIA principles.
Regulatory controls it satisfies
Typical timeline
The core framework is typically delivered in four to eight weeks depending on model count and current governance maturity.
Common questions
Do we need a governance framework if we only use third-party foundation models?
Yes. Accountability for usage, data, and outputs stays with your organisation even with external models. The framework then focuses on usage controls, classifying what data may be sent, provider contract clauses, and output monitoring.
How is AI governance different from responsible-AI compliance?
Governance establishes the structure, policies, roles, and approval gates that control the model lifecycle, while responsible-AI compliance measures how well your live systems honour those controls and regulatory principles and watches for bias and drift. The first builds the foundation; the second verifies it holds.
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