A structured, measurable, and internationally aligned framework for responsible AI adoption
The 7-Pillar AI Governance Model™ gives organisations a practical framework for adopting AI responsibly. It is not a theoretical policy paper — it is a working advisory and assessment methodology designed to be used in boardrooms, project teams, and regulatory conversations.
The model was developed by Said Sulaiman Al Azri, drawing on 35 years of experience across computer engineering, cybersecurity, AI strategy, and enterprise governance — including founding Oman's first Information Security and Standards Office at Royal Court Affairs and building multiple AI-powered platforms. It is mapped at the sub-dimension level to ISO/IEC 42001, NIST AI RMF 1.0, the EU AI Act, the Oman PDPL, the MTCIT 2025 General Policy, and the Oman National AI Strategy 2025–2030.
Ensure that every AI initiative is anchored to a clear business problem, aligned with organisational strategy and Oman's national priorities, and governed by measurable outcomes that justify continued investment.
Key Assessment Areas:
AI Vision & Strategic Alignment · Use Case Identification & Prioritisation · Roadmap & Budget Governance · Success Metrics & Value Measurement
Establish clear ownership, decision rights, escalation paths, and oversight mechanisms so that every AI-influenced decision can be traced to a responsible human being and every AI system operates within a defined governance structure.
Key Assessment Areas:
Executive Ownership & Governance Body · AI Policy Framework · Roles, Responsibilities & Decision Rights · Escalation, Override & Human Review
Ensure that AI is used to augment human judgement, productivity, and service quality — not deployed as a black-box replacement for leadership — and that explainability and human oversight are proportional to risk.
Key Assessment Areas:
Decision Classification (Assist vs. Automate) · Explainability Requirements · Output Verification & Quality Assurance · Staff Competence in AI Interpretation
Design safe, phased, and measurable implementation across pilots, production, and scale-up — with defined launch criteria, exit criteria, rollback plans, and vendor governance.
Key Assessment Areas:
Pilot-First Methodology · Launch & Exit Criteria · Production Monitoring & Performance Tracking · Rollback, Suspension & Decommissioning · Vendor & Third-Party AI Governance
Protect privacy, fairness, consent, and trust throughout the data lifecycle and the model lifecycle — ensuring AI systems are trained, tested, and operated on data that is lawful, representative, and ethically sourced.
Key Assessment Areas:
Lawful Basis & Consent Management · Data Quality, Representativeness & Provenance · Bias Detection & Mitigation · AI Impact Assessment · Retention, Deletion & Data Lifecycle
Ensure that the systems, access controls, integrations, and vendors supporting AI are secure, resilient, and compliant — treating AI-specific threats like prompt injection, data leakage, and adversarial attacks with the same rigour as traditional cybersecurity threats.
Key Assessment Areas:
AI System Inventory & Approval · Access Control & Identity Management · AI-Specific Threat Mitigation · Infrastructure Resilience & Business Continuity · Secure Development & Integration Practices
Build the capabilities, culture, training programmes, and enterprise risk controls needed for long-term, sustainable AI adoption — ensuring the organisation's people and risk systems evolve at the same pace as its AI ambitions.
Key Assessment Areas:
AI Skills & Competence Development · Culture & Change Management · Enterprise Risk Integration · Incident Management & Learning · Continuous Improvement
Every organisation is assessed across all seven pillars using a consistent 1–5 scoring rubric with defined evidence indicators at each level. Pillar scores are combined into a composite maturity score that places the organisation on a five-level maturity path.
| Level | Score Range | Name | Description |
|---|---|---|---|
| 1 | 1.0 – 1.4 | Ad Hoc | No formal governance; isolated experiments. Curious but unstructured. |
| 2 | 1.5 – 2.4 | Emerging | Some awareness and informal controls. Interested but patchy. |
| 3 | 2.5 – 3.4 | Defined | Policies, roles, and standards documented. Ready for governed pilots. |
| 4 | 3.5 – 4.4 | Managed | Monitoring, review, and improvement active. Running controlled deployments. |
| 5 | 4.5 – 5.0 | Optimised | Governance embedded in operations and culture. Scaling confidently. |
Scoring weights can be adjusted by sector — healthcare, financial services, government, education, energy, and retail each have recommended weight profiles that reflect their specific risk and regulatory environments.
The model is mapped at the sub-dimension level to five authoritative standards and regulations, ensuring that organisations using the framework simultaneously build compliance readiness across multiple obligations.
Global AI management system standard (certifiable)
All 7 pillars mapped to relevant clauses and Annex A controls
US national AI risk management framework
All 7 pillars mapped to Govern, Map, Measure, Manage functions
European risk-based AI regulation
Risk classification and high-risk obligations mapped across Pillars 2–7
Oman's Personal Data Protection Law
Data processing, consent, and data subject rights mapped to Pillars 5–6
Safe and ethical AI use in Oman
Human-centred AI, accountability, and bias mitigation mapped across all pillars
Oman's national AI adoption targets
Talent, transformation, and ethical framework targets mapped to Pillars 1 and 7