| Date | AIUC-1 requirement and control | Category | Change notes |
|---|---|---|---|
| Q2 2026 | All requirements | Revision | Updated typical evidence descriptions to move away from screenshots in favor of substantive and verifiable evidence |
| Q2 2026 | A002: Establish output data policy | Revision | Included both opt-in and out practices |
| Q2 2026 | A002.1 Documentation: Output usage and ownership policy | Revision | Included both opt-in and out practices, ensuring balanced coverage of consent models |
| Q2 2026 | A002.2 Config: Opt-in/opt-out and output deletion implementation | Addition | Added new control to incorporate implementation testing to A002 |
| Q2 2026 | A003: Limit AI agent data collection | Specification | Specified that the requirement covers data access more generally, and included more controls on agent IAM within it |
| Q2 2026 | A003.1 Config: Data access scoping | Clarification | Clarified the control to cover agent access and identity management, not just data collection |
| Q2 2026 | A003.3 Config: Agent identity management | Revision | Separated agent identity and access management into distinct controls, with a focus on providing configurable, auditable architecture that integrates with enterprise IAM systems |
| Q2 2026 | A003.4 Config: Agent access and permissions management | Addition | Separated agent identity and access management into distinct controls, with a focus on providing configurable, auditable architecture |
| Q2 2026 | B002: Detect adversarial input | Clarification | Clarified that monitoring is to enable responding to adversarial inputs |
| Q2 2026 | B006: Prevent unauthorized AI agent actions | Revision | Changed on a controls level - MCP coverage and additional execution-level containment controls |
| Q2 2026 | B006.1 Config: Agent service access restrictions | Revision | Covered MCP server access alongside existing API and service-level restrictions |
| Q2 2026 | B006.3 Config: Execution-level safeguards | Addition | Added execution-level containment controls that limit the blast radius when an agent or approved MCP server behaves unexpectedly at runtime |
| Q2 2026 | B008: Protect AI system deployment environment | Revision | Expanded scope of requirement from the AI model only to system |
| Q2 2026 | B008.1 Config: Model access controls | Clarification | Expanded scope of control from the AI model only to system |
| Q2 2026 | B008.2 Config: API and agentic interface authentication | Revision | Expanded deployment security controls to address MCP and A2A protocols alongside traditional API endpoints, with dedicated controls for authentication, transport security, and message integrity across all agentic interfaces |
| Q2 2026 | B008.3 Config: API and agentic interface transport security | Addition | See above |
| Q2 2026 | B008.4 Config: Agentic interface data integrity | Addition | See above |
| Q2 2026 | C001: Define AI risk taxonomy | Specification | Generalized the risk taxonomy requirement and changed testing frequency to every 12 months |
| Q2 2026 | C001.2 Documentation: Risk taxonomy reviews | Specification | Aligned testing frequency to a 12-month cycle consistent with the risk management framework update schedule |
| Q2 2026 | C006: Prevent output vulnerabilities | Clarification | Clarified that the requirement is in scope for AI agents that generate code (see C006.1, C006.2, C006.3), and text (see C006.2) |
| Q2 2026 | C006.1 Config: Output sanitization | Clarification | Clarified that the control is in scope for code-generating AI agents |
| Q2 2026 | C006.2 Demonstration: Warning labels for untrusted content | Clarification | See above |
| Q2 2026 | C006.3 Config: Adversarial output detection | Clarification | See above |
| Q2 2026 | C007: Flag high risk outputs for human review | Clarification | Clarified that C007 is about human in the loop via updated label |
| Q2 2026 | C007.1 Documentation: Definition of high-risk output criteria | Clarification | Expanded requirement scope from recommendations to generalized outputs |
| Q2 2026 | C007.3 Documentation: Human review workflows | Revision | Included example of auditing human review workflows (i.e., checking the effectiveness of oversight over time) to mitigate against ‘automation bias’ |
| Q2 2026 | C009: Enable real-time feedback and intervention | Revision | Changed on a controls level - synthesized controls and added in control to action user feedback |
| Q2 2026 | C009.2 Documentation: User feedback & intervention reviews | Clarification | Included practical validation and actioning of relevant user feedback, and streamlined three controls into one |
| Q2 2026 | D003: Restrict unsafe tool calls | Revision | Changed on a controls level - extends tool call validation to cover MCP servers alongside approved functions, expands scope of human approval for sensitive tool operations to cover multi-step workflows |
| Q2 2026 | D003.1 Config: Tool authorization & validation | Revision | Extended tool call validation to cover MCP servers alongside approved functions |
| Q2 2026 | D003.3 Config: Tool call log | Revision | Extended tool call validation to cover MCP servers alongside approved functions |
| Q2 2026 | D003.4 Config: Human-approval workflows | Revision | Expanded scope to cover multi-step workflows, reflecting trends of AI agents increasingly chaining tool calls across sequential operations rather than executing single actions in isolation |
| Q2 2026 | E005: Document data storage security | Clarification | Clarified that the requirement is around ensuring companies establish clear security and compliance requirements for hosting platforms, rather than the act of cloud vs on-prem assessment |
| Q2 2026 | E009: Monitor third-party access | Revision | Enforced E009 as a mandatory control |
| Q2 2026 | E015: Log AI system activity | Revision | Expanded scope of requirement from the AI model only to system |
| Q2 2026 | E015.2 Config: AI agent logging implementation | Addition | Extended logging to cover the intermediate steps between input and output (i.e., tool calls, sub-agent actions, and provenance metadata) getting traceability across the full execution chain |
| Q2 2026 | E016: Implement AI disclosure mechanisms | Specification | Changed on a controls level - adjusts disclosure to AI agents and systems |
| Q2 2026 | E016.4 Demonstration: Automation AI disclosure | Specification | Adjusted disclosure to AI agents and systems |
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