Govern AI through adapted and updated decision-making and accountability structures that draw on the functions AI cuts across (data and technology governance, privacy, human rights, diversity and inclusion, ethics, cyber security, audit, intellectual property, risk, digital investment and procurement), with implementation led by business or policy areas and supported by technologists. Structures are proportionate and adaptable, set lines of responsibility and give agency leaders clear sight of the AI uses they are accountable for. Leaders commit to safe and responsible use, build a positive AI risk culture, and give staff the information, training and resources to align with government objectives, use AI ethically and lawfully, exercise discretion over AI outputs, identify, report and mitigate risks, consider testing, transparency and accountability requirements, support the community through service changes, and explain AI-influenced outcomes clearly.
This control maps to 3 controls across 3 other frameworks. If you already hold one of them, the evidence you collected for it is the starting point here rather than new work.
Every mapping shown was judged rather than inferred from wording similarity, and the ones that failed review are published too. See the coverage reports and what was rejected.
The graph holds this control, the 3 it maps to, and the evidence behind each claim, over MCP and REST.