Verify AI outputs before use and on an ongoing basis using professional judgement on accuracy, fairness and bias, so outputs are not misleading or overstated; make sure outputs suit diverse communities, including Aboriginal and Torres Strait Islander people, and meet accessibility requirements; complete the required AI literacy and policy training; consider whether AI use may create risks to employees' health, safety or wellbeing and act on them, including by reporting; and report suspected or actual unsafe, biased or harmful AI use through agency governance and escalation processes.
This control maps to 2 controls across 1 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 2 it maps to, and the evidence behind each claim, over MCP and REST.