Across the lifecycle an AI system should respect and uphold privacy rights and data protection and keep data secure. This means proper data governance and management of all data the system uses and generates, including privacy measures such as appropriate anonymisation, and an ongoing check that the link between data and the inferences the system draws from it is sound. It also means appropriate data and system security: identifying potential vulnerabilities, assuring resilience to adversarial attack, and accounting for unintended applications and abuse risks with proportionate mitigations.
This control maps to 7 controls across 4 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 7 it maps to, and the evidence behind each claim, over MCP and REST.