What Is AI Risk Management? A Complete Guide
What AI risk management is, the categories of AI risk, the NIST AI RMF and ISO 42001, and a step-by-step plan to build your programme.
Updated October 2026
Practical AI security, from someone who has run it in production.
Guidance on securing AI systems and meeting compliance obligations in African organisations, written from the experience of running healthcare platforms across four countries.
- Operated in
- Ghana, Nigeria, Kenya and Egypt
- Last role
- CTO, CarePoint (formerly African Health Holding)
- Certifications
- CISA and CDPSE
- Education
- MSc IT, UWE Bristol
Latest analysis
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AI Security Risk Assessment Framework for African Enterprises: A Step-by-Step Guide
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The seven-pillar framework
AI security and compliance, split into seven domains. Each pillar opens with a guide to the domain, then links to articles that go deeper. Start with the one closest to the problem in front of you.
- AI Cybersecurity FundamentalsThreat models, attack vectors and defences specific to AI systems.5 articles
- AI Risk ManagementAssessing AI risk you can't easily measure, and reporting it to the board.3 articles
- AI Regulatory Compliance & StandardsThe EU AI Act, GDPR and African data protection law, in practice.5 articles
- Data Privacy & AIWhere training data, prompts and outputs go, and how to control them.4 articles
- Enterprise AI Governance, Risk & ComplianceGovernance that keeps pace with AI adoption without slowing it down.5 articles
- AI Security ToolsWhat to use for monitoring, testing and hardening AI systems.2 articles
- AI Compliance by IndustryHealthcare, fintech and other regulated sectors.3 articles
Free live sessions, with open replays
One practical AI security or compliance problem per session, with time for your questions. Every session is recorded and published here with a transcript and slides.
- Live
- Monthly, online, free. A focused talk followed by open Q&A.
- Replay
- Recording, transcript and slides published here, open to everyone.
The first session is being scheduled
AI risk management isn't about perfect frameworks. It's about identifying what can actually go wrong in your specific context, implementing controls that work with your constraints, and monitoring continuously because AI systems don't stay static.
Patrick Dasoberi
Former CTO of CarePoint (formerly African Health Holding), responsible for healthcare systems across Ghana, Nigeria, Kenya and Egypt. CISA and CDPSE certified, with an MSc in IT from UWE Bristol and seven years teaching software development in Ghana.
Read the full background