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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24 of 105 controls shown.
| ID | Control | Family | Type |
|---|---|---|---|
| C-079 | Accessibility and inclusive design review AI-driven channels are designed and tested with assistive technology and with people who have access needs before release, and an equivalent alternative route is kept available. | 07 Fairness & Customer Outcomes | Preventive |
| C-025 | Agent sandboxing and isolation Code execution and tool use by agents run in isolated environments with restricted network, file and system access, so that a failure is contained where it occurs. | 10 Security & Resilience | Preventive |
| C-016 | AI literacy programme Everyone who uses, relies on or makes decisions about AI receives training in what these systems are, how they fail and what the organisation's rules require. | 12 Skills, Capability & Culture | Directive |
| C-001 | AI system monitoring and logging Every production AI system is instrumented to record its inputs, outputs, errors and usage, with thresholds that raise alerts to a named owner who must act. | 11 Monitoring, Assurance & Incidents | Detective |
| C-018 | AI transparency and disclosure labelling People are told, clearly and at the point of contact, when they are dealing with an AI system and when content they are shown was generated by one. | 08 Transparency & Explainability | Directive |
| 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-094 | Human override and stop mechanism A designated person can pause, override or shut down an AI system at any time through a tested mechanism, with the authority to do so settled in advance. | 09 Human Oversight & Contestability | Corrective |
| C-009 | Inference usage quotas and rate limiting Limits on request rate, input size, output size and spend are enforced for each user, tenant and system, with alerts as limits are approached. | 10 Security & Resilience | Preventive |
| 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-095 | Intellectual property and licence clearance Training data, models, components and significant outputs are checked for rights and licence terms before they are used, published or shipped. | 03 Regulatory & Legal Compliance | Preventive |
| C-024 | Least-privilege agent identity and permissions Each agent has its own identity, with permissions scoped to its task and to the user it is acting for, short-lived and reviewed like any other entitlement. | 10 Security & Resilience | Preventive |
| C-031 | Model and data access control Access to model weights, training data, pipelines and inference endpoints is granted by role, limited to need, logged and reviewed. | 10 Security & Resilience | Preventive |
| C-010 | Model artifact signing and integrity verification Approved model artefacts are cryptographically signed at validation, and the signature is verified before an artefact is loaded anywhere in production. | 10 Security & Resilience | Preventive |
| C-065 | Post-market monitoring plan A documented plan sets out how evidence of a system's performance, use and effects is collected, reviewed and acted on throughout its life. | 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-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-103 | Role-based AI training Reviewers, developers, risk and assurance staff, approvers and front-line teams receive training specific to their AI responsibilities, with competence assessed and completion tracked. | 12 Skills, Capability & Culture | Directive |
| C-034 | Safe model loading and artefact scanning Model files and their dependencies are obtained only from approved sources, scanned before use, and loaded in formats and with settings that cannot execute embedded code. | 10 Security & Resilience | Preventive |
| C-030 | Secrets management for AI Credentials used by AI systems are held in a vault, supplied at run time, kept out of prompts, configuration and notebooks, scoped narrowly and rotated. | 10 Security & Resilience | Preventive |
| 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 |
| C-096 | Synthetic content marking and provenance Generated and manipulated media carries machine-readable marking and provenance data, a visible disclosure where one is required, and the marking is tested for robustness. | 08 Transparency & Explainability | Preventive |
| C-037 | Tenant and session isolation Every stateful component of an AI system, including caches, memory, retrieval indexes and fine-tuned models, is partitioned by customer and user, and the partitioning is tested. | 10 Security & Resilience | Preventive |
| C-013 | Vector store and embedding access controls A retrieval system's vector store is governed as a production data store: access is authenticated and authorised, source permissions are enforced at retrieval, and writes are restricted and recorded. | 04 Data & Data Governance | Preventive |