Controls.
Each entry is a specific control: what it does, where it fits in the prevent, detect, correct and direct lifecycle, and which risks it addresses, graded primary, supporting or compensating. 105 controls.
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19 of 105 controls shown.
| ID | Control | Family | Type |
|---|---|---|---|
| C-006 | Adversarial robustness testing A defined battery of adversarial tests is run against an AI system before release and after every material change, and release depends on the results. | 10 Security & Resilience | Preventive |
| C-044 | AI change management Changes to models, prompts, data sources, tools and configuration are assessed, tested, approved and versioned before they reach production. | 05 Model & System Lifecycle | Preventive |
| C-049 | AI decommissioning procedure Retirement of an AI system follows a plan that covers dependent processes, disposal of data and model artefacts, retention of records and any continuing obligations. | 05 Model & System Lifecycle | Directive |
| C-032 | AI red teaming People skilled in attacking AI systems try to make the system fail as an adversary would, before release and periodically, and their findings are tracked to closure. | 10 Security & Resilience | Detective |
| C-068 | Data quality and representativeness checks Training, test and reference data are assessed for accuracy, completeness, currency and coverage of the population the system will serve, before use and at each refresh. | 04 Data & Data Governance | Detective |
| C-050 | Drift and performance monitoring Input distributions and output quality are measured in production against thresholds that trigger investigation, retraining or withdrawal. | 11 Monitoring, Assurance & Incidents | Detective |
| C-043 | Evaluation suite and regression testing A maintained set of test cases with expected results is run on every change to model, prompt, data or tools, and the results are compared with the approved baseline. | 05 Model & System Lifecycle | Detective |
| C-098 | Explanation method validation The techniques used to explain a model are tested to confirm that they reflect the model's actual behaviour before explanations are relied on internally or given to anyone. | 08 Transparency & Explainability | Detective |
| C-008 | Factuality and hallucination verification Claims, figures and citations in AI-generated output are checked against a trusted source before the output is relied on for a consequential decision or sent to a customer. | 05 Model & System Lifecycle | Detective |
| C-041 | Independent model validation A function independent of the developers tests each model's conceptual soundness, data, performance and limitations before approval and periodically afterwards, to a depth set by the model's tier. | 05 Model & System Lifecycle | Detective |
| C-047 | Intended-use definition and use restrictions The approved purpose, users, population and conditions of use of each AI system are stated precisely, and any use outside them requires reassessment before it begins. | 05 Model & System Lifecycle | Directive |
| C-046 | Model and system documentation Each AI system's purpose, design, data, test results and limitations are documented to a set standard that allows a validator, auditor or regulator to understand it without its authors. | 05 Model & System Lifecycle | Directive |
| C-084 | Model version pinning and change notification Hosted models are called by a fixed version, and a provider's new version is adopted only after the organisation's own regression tests have passed. | 06 Third Party & Supply Chain | Preventive |
| C-066 | Periodic AI review and recertification Each AI system is reassessed on a cycle set by its tier, covering use, performance, risk and controls, and its approval lapses if the review is not completed. | 11 Monitoring, Assurance & Incidents | Detective |
| C-042 | Pre-deployment testing and acceptance Functional, safety, security and user tests defined in advance must pass, with results recorded, before an AI system is released. | 05 Model & System Lifecycle | Preventive |
| C-048 | Retrieval grounding and citation The system answers only from retrieved, approved sources, cites the source for each statement, and declines where the sources do not support an answer. | 05 Model & System Lifecycle | Preventive |
| C-038 | Review of AI-generated code Code produced with an AI assistant passes the same human review, security scanning and dependency checks as any other code before it is merged. | 10 Security & Resilience | Detective |
| C-033 | Secure AI development pipeline The pipelines and tooling that train, evaluate and deploy models are subject to the security controls of production software delivery, including review, scanning and segregation of environments. | 10 Security & Resilience | Preventive |
| C-045 | Staged rollout New AI systems and material changes are released first to a limited population under close monitoring, and extended only when defined criteria are met. | 05 Model & System Lifecycle | Preventive |