Responsible AI governance is often framed as a compliance exercise. In practice, it is what allows organizations to adopt AI faster, because teams know what is expected and decision-makers can approve new uses with confidence.
Build an inventory
You cannot govern what you cannot see. Start with an inventory of AI systems in use or development — including third-party tools and AI features embedded in software you already buy. Capture the purpose, data used, owner and affected users.
Tier by risk
Not every AI use deserves the same scrutiny. Define risk tiers based on factors such as impact on individuals, degree of automation, sensitivity of data and regulatory exposure. Tie each tier to a proportionate set of controls.
Define clear roles
Effective governance needs named owners: a business owner accountable for each system, technical owners responsible for performance, and an oversight function that sets standards and reviews higher-risk uses.
Embed controls in the lifecycle
Controls work best when they are part of how teams already build and buy technology:
- Design — document intended use, data sources and known limitations
- Build — test for accuracy, robustness and fairness against agreed thresholds
- Deploy — confirm human oversight, logging and incident processes are in place
- Operate — monitor performance and drift, and review periodically
Align with recognized frameworks
Frameworks such as the NIST AI Risk Management Framework and ISO/IEC 42001, and regulations such as the EU AI Act, provide useful structure. Map your controls to the frameworks most relevant to your markets so one set of evidence serves multiple purposes.
Keep it proportionate
The goal is not paperwork. It is trustworthy AI delivered at pace. Review your governance process regularly and remove steps that do not reduce real risk.
Need help designing your AI governance program? Contact our AI governance team.
