Risks.
Named risks, not vague hazards: each entry is a specific, recognised AI risk, with the controls that mitigate it, the clauses that cite it and the sources behind it. 169 risks across 16 categories.
Severity by category
A compact map of the whole register before you filter it.
| Category | critical | high | medium | low | Total |
|---|---|---|---|---|---|
| Governance & Accountability | – | 11 | 10 | – | 21 |
| Model Performance & Reliability | – | 7 | 6 | – | 13 |
| Data Protection & Privacy | 2 | 7 | 4 | – | 13 |
| Fairness, Bias & Impact | – | 4 | 5 | – | 9 |
| Conduct & Customer Outcomes | – | 8 | 4 | – | 12 |
| Transparency & Explainability | – | 2 | 4 | – | 6 |
| Human Factors & Workforce | – | 4 | 3 | – | 7 |
| Model & Application Security | – | 12 | 5 | – | 17 |
| Adversarial ML & Model Integrity | 1 | 4 | 3 | – | 8 |
| Agentic & Autonomous Systems | 1 | 10 | 4 | – | 15 |
| Generative Content & Information Integrity | 1 | 2 | 5 | – | 8 |
| Intellectual Property & Legal Liability | – | 5 | 3 | – | 8 |
| Third-Party & Supply Chain | – | 5 | 4 | – | 9 |
| Operational Resilience | – | 7 | 2 | – | 9 |
| Prohibited & High-Risk Practices | 8 | 1 | – | – | 9 |
| Systemic & Societal | – | 2 | 2 | 1 | 5 |
Browse by list, category or framework
31 of 169 risks shown.
| ID | Risk | Category | Severity |
|---|---|---|---|
| R-014 | Adversarial testing coverage gap An AI system receives functional testing and no structured adversarial testing, before release or after, so the organisation has no evidence for the resilience it assumes. | Adversarial ML & Model Integrity | Medium |
| R-066 | AI development environment compromise Training pipelines, notebooks, registries and experiment tooling are less protected than production, giving an attacker a route to data, weights and the deployed model. | Model & Application Security | High |
| R-031 | AI model drift and degradation A deployed model's performance declines as the world it operates in moves away from the data it was trained and validated on, with no change to the model itself. | Model Performance & Reliability | High |
| R-021 | AI system lifecycle controls gap The organisation has no defined lifecycle for AI systems, so there is no consistent checkpoint at which a system is assessed, validated, approved, monitored or retired. | Governance & Accountability | Medium |
| R-151 | Defamation by AI output A system publishes a false statement about an identifiable person or organisation that damages their reputation. | Intellectual Property & Legal Liability | High |
| R-079 | Evaluation contamination and overstated performance Test data overlaps with training data, or benchmarks do not reflect real use, so reported performance is better than the system will deliver. | Model Performance & Reliability | Medium |
| R-059 | Function-calling hallucination An agent invents a tool, a parameter or the result of a call, and the fabricated step is executed or relied on as though it had happened. | Agentic & Autonomous Systems | Medium |
| R-141 | Harmful content generation A system produces toxic, violent, hateful, sexual or otherwise harmful content, and that content reaches users or is published in the organisation's name. | Generative Content & Information Integrity | High |
| R-106 | Inaccurate personal data in outputs A generative model states false information about an identifiable person as though it were fact. | Data Protection & Privacy | High |
| R-024 | Inadequate fairness testing before deployment An AI system that affects people is released without testing whether its performance and outcomes differ between groups. | Fairness, Bias & Impact | Medium |
| R-077 | Inadequate independent model validation Validation is absent, is carried out by the model's own developers, or is not proportionate to the model's tier, so weaknesses are discovered in production. | Model Performance & Reliability | High |
| R-085 | Inadequate pre-deployment testing A system goes live without testing proportionate to its risk, including functional, safety, security and user testing in realistic conditions. | Model Performance & Reliability | High |
| R-095 | Inadequate technical documentation Design, data, testing and limitations are not documented well enough for a validator, auditor or regulator to understand the system. | Governance & Accountability | Medium |
| R-068 | Insecure AI-generated code Code suggested by an AI assistant is accepted with vulnerabilities, invented dependencies or licence problems that review would have caught in code a person wrote. | Model & Application Security | High |
| R-082 | Miscalibrated confidence The system presents uncertain outputs with unwarranted confidence, or gives no usable signal of uncertainty, so users cannot tell when to check. | Model Performance & Reliability | Medium |
| R-009 | Misinformation and confabulation A generative model states false or fabricated information with the same fluency and confidence as accurate information, and the output is relied on. | Generative Content & Information Integrity | High |
| R-087 | Model interdependency and aggregation Models feed other models, so an error or change in one propagates, and the combined uncertainty across the chain is never assessed. | Model Performance & Reliability | Medium |
| R-076 | Model not fit for its intended purpose A model is deployed without evidence that it performs well enough for the specific task, population and conditions in which it is used. | Model Performance & Reliability | High |
| R-155 | Opaque model logic Developers, validators and owners cannot explain why a model produces its outputs well enough to challenge, correct or defend it. | Transparency & Explainability | High |
| R-140 | Out-of-scope responses A system answers questions outside its intended domain, where it has not been tested and the organisation has no authority or competence to assist. | Generative Content & Information Integrity | Medium |
| R-081 | Output inconsistency and non-reproducibility The same input produces materially different outputs on different occasions, so decisions cannot be reproduced, audited or relied on to treat like cases alike. | Model Performance & Reliability | Medium |
| R-080 | Poor generalisation outside training conditions The model meets inputs unlike its training data and fails, without signalling that it is outside its competence. | Model Performance & Reliability | High |
| R-078 | Poor quality or unrepresentative training data Training or reference data is inaccurate, incomplete, out of date or unrepresentative of the population the system serves, and the model inherits the defect. | Model Performance & Reliability | High |
| R-075 | Safeguard erosion through fine-tuning Fine-tuning or other adaptation weakens a base model's safety behaviour, and the change goes untested because the base model had already been assessed. | Adversarial ML & Model Integrity | Medium |
| R-086 | Stale model knowledge A model answers from knowledge that ended at its training cut-off, on matters such as rates, rules or products that have since changed. | Model Performance & Reliability | Medium |
| R-128 | Unannounced upstream model change A provider updates, replaces or reconfigures a hosted model, and the organisation's system behaves differently without any change of its own. | Third-Party & Supply Chain | High |
| R-094 | Uncontrolled AI change Models, prompts, data sources and configuration change in production without assessment, testing, approval or a record. | Governance & Accountability | High |
| R-102 | Uncontrolled AI retirement A system is withdrawn without managing dependent processes, retained data, model artefacts and continuing obligations. | Governance & Accountability | Medium |
| R-111 | Unequal performance across groups A system is less accurate or less useful for some groups, languages, accents or dialects than for others, although everyone is treated by the same process. | Fairness, Bias & Impact | High |
| R-139 | Ungrounded or misattributed answers A retrieval-based system cites a source that does not support its answer, or presents content the model generated as though it came from a source. | Generative Content & Information Integrity | Medium |
| R-084 | Use beyond validated scope A model approved for one purpose is reused for another, or for a new population, without reassessment. | Model Performance & Reliability | High |