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-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-089 | AI failure scenario testing Severe but plausible AI failure scenarios are exercised against impact tolerances on a set cycle, and the lessons are acted on. | 10 Security & Resilience | 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-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-035 | Content safety filtering Inputs and outputs are classified against defined harm categories by a mechanism independent of the main model, and are blocked, modified or escalated according to policy. | 10 Security & Resilience | Preventive |
| 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-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-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-075 | Fairness requirements and metric selection For each system that affects people, the relevant groups, the legal test, the fairness measures and their thresholds are defined and justified before testing begins. | 07 Fairness & Customer Outcomes | 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-029 | Input filtering and injection defence User input and all retrieved or tool-supplied content are screened for hostile instructions and passed to the model marked as data, separate from the system's own instructions. | 10 Security & Resilience | Preventive |
| 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-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-004 | Output validation and guardrails Every model output is validated against a schema and sanitised by deterministic code before any downstream system, tool or user interface acts on it. | 10 Security & Resilience | Preventive |
| C-071 | Personal data redaction and pseudonymisation Personal data is removed, masked or pseudonymised before data is used for training, evaluation or logging, unless it is needed and the need is recorded, and the result is tested. | 04 Data & Data Governance | Preventive |
| 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-081 | Pricing fairness review AI-driven prices and offers are reviewed, before use and on a set cycle, for differences between customer groups that cost or risk does not justify. | 07 Fairness & Customer Outcomes | Detective |
| C-040 | Privacy-preserving training techniques Where training data is personal or confidential, techniques that limit how much a model retains about any single record are applied, and the remaining exposure is measured. | 04 Data & Data Governance | 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-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-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 |