AI Governance & Responsible AI
AI adoption without governance creates exposure, not value. Every RADA AI engagement is built around control, oversight and accountability from the start — not added afterward.
Governance built into how AI gets adopted, not bolted on after
Data confidentiality & privacy
Client and employee data handled under clear confidentiality standards, consistent with Kenya's Data Protection Act, 2019.
Human oversight & approval protocols
Sensitive decisions keep a named human reviewer, a defined escalation path and an audit trail — by design, not by exception.
AI use policies & governance
A clear, board-approved policy defining what AI may be used for, by whom, and under what controls.
Model & vendor risk
Underlying models and platforms assessed for reliability, security posture and concentration risk before adoption.
Audit trails & accountability
A traceable record of what an AI system did, when, and who approved it — available for internal or external audit.
Data residency & cybersecurity
Clarity on where data is processed and stored, and the security controls protecting it throughout.
Employee use of public AI tools
Staff are often already using public AI tools informally, without policy, oversight or a shared understanding of what data is safe to share. This is usually the first governance gap worth closing — before any formal AI programme begins.
We help you set clear, practical rules for public AI tool use, train staff on them, and build the internal alternatives that make the safe option also the easy one.
Put governance in place before you scale AI adoption
A diagnostic is the fastest way to see where your governance gaps actually are.