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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36 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-026 | Agent tool and skill vetting Tools, skills and connectors are reviewed, scanned and approved before an agent may load them, are loaded only from approved sources, and are re-reviewed when they change. | 06 Third Party & Supply Chain | Preventive |
| C-085 | AI bill of materials The models, datasets, libraries, tools and hosted components that make up each AI system are recorded and checked against known vulnerabilities, licence terms and supplier changes. | 06 Third Party & Supply Chain | Detective |
| 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-015 | AI impact assessment process A structured assessment of an AI system's effect on individuals, groups and society is completed and signed off before deployment, and revisited when the system or its use changes. | 03 Regulatory & Legal Compliance | Directive |
| C-055 | AI inventory and registration All AI systems and models, whether built, bought or embedded in other products, are recorded with owner, purpose, tier, data, supplier and status, and the record is reconciled periodically. | 02 Governance & Accountability | Detective |
| C-064 | AI record-keeping and audit trail Inputs, outputs, model and prompt versions, decisions and human interventions are recorded automatically and retained for a defined period, so that any past event can be reconstructed. | 11 Monitoring, Assurance & Incidents | Detective |
| 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-056 | AI risk tiering and classification Each system is assigned an internal risk tier and a regulatory class by documented criteria, the result is reviewed independently, and it is revisited whenever the system or its use changes. | 02 Governance & Accountability | Directive |
| C-007 | Bias testing and fairness monitoring A system's performance and outcomes are measured separately for each relevant group before release and on a set cycle in production, against thresholds that trigger investigation. | 07 Fairness & Customer Outcomes | Detective |
| C-020 | Data minimisation and lawful basis review Before personal data is used to train, fine-tune, ground or run an AI system, a documented review confirms the lawful basis, the compatibility of the purpose and that only necessary data is used. | 04 Data & Data Governance | Directive |
| C-074 | Data protection impact assessment for AI A data protection impact assessment is completed before AI processing that is likely to result in high risk to individuals begins, and is kept current as the system changes. | 03 Regulatory & Legal Compliance | Directive |
| 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-073 | Data subject rights handling for AI A tested procedure locates and acts on a person's data in training sets, vector stores, logs and outputs when an access, rectification, erasure or objection request is received. | 04 Data & Data Governance | Corrective |
| C-069 | Dataset documentation Each dataset used for AI has a maintained record of its source, collection method, contents, rights, lawful basis, known limitations and permitted uses. | 04 Data & Data Governance | Directive |
| 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-088 | Fallback and manual workaround A tested alternative, such as a manual process, a simpler model or a degraded mode, is ready to take over when an AI component fails or has to be withdrawn. | 10 Security & Resilience | Corrective |
| C-061 | High-risk conformity assessment and registration High-risk AI systems complete the required conformity assessment, declaration, marking and registration before they are placed on the market or used, and again after substantial modification. | 03 Regulatory & Legal Compliance | Preventive |
| C-021 | Important business service impact-tolerance mapping The AI components each important business service depends on are identified in the resilience map, and impact tolerances are set that cover AI-specific disruption, including a model that is running and wrong. | 10 Security & Resilience | Directive |
| 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-099 | Information for deployers and users Those who operate and use an AI system receive current, usable instructions covering its purpose, performance, limitations, the meaning of its outputs and the oversight it needs. | 08 Transparency & Explainability | Directive |
| 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-086 | Ongoing AI vendor monitoring Each AI supplier's performance, incidents, model changes, terms and financial and operational standing are reviewed on a cycle set by how material the supplier is. | 06 Third Party & Supply Chain | Detective |
| 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-060 | Prohibited-practice screening Every proposed AI use is checked against the practices the law prohibits before it is approved, the check is recorded, and it is repeated when the use changes. | 03 Regulatory & Legal Compliance | Preventive |
| C-072 | Prompt and log retention limits Prompts, outputs and conversation histories are kept for defined periods and then deleted, by the organisation and by its providers, with deletion verified. | 04 Data & Data Governance | Preventive |
| C-100 | Public AI transparency record The organisation publishes and maintains a record of its significant AI uses, describing what each is for, what data it uses and what safeguards apply. | 08 Transparency & Explainability | Directive |
| C-093 | Root-cause analysis and lessons learned Significant AI incidents and near misses are analysed for their causes, including the control failures that let them happen, and the resulting actions are tracked to completion. | 11 Monitoring, Assurance & Incidents | Corrective |
| C-011 | System prompt hardening System prompts are written to contain no secrets and no security decisions, are placed under version control and review, and are tested against extraction before release. | 10 Security & Resilience | Preventive |
| C-012 | Training data provenance checks The source, collection method, rights and integrity of every dataset are verified and recorded before it is used for training, fine-tuning or retrieval. | 04 Data & Data Governance | Preventive |
| C-022 | Vendor exit strategy and concentration management Each material AI supplier has a documented, costed and tested exit plan, and dependence on each provider is measured across the organisation and held within limits. | 06 Third Party & Supply Chain | Directive |
| C-104 | Workforce impact assessment and involvement Before AI is deployed in ways that change work, its effect on roles, skills, working conditions and monitoring of staff is assessed, with the people affected and their representatives informed and heard. | 12 Skills, Capability & Culture | Directive |