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
15 of 169 risks shown.
| ID | Risk | Category | Severity |
|---|---|---|---|
| R-034 | Inadequate explainability for affected individuals A person materially affected by an AI-supported decision is not given an explanation they can understand and use to challenge it. | Transparency & Explainability | 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-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-118 | Misleading AI-generated communications Customer communications or promotions written by AI are unclear, inaccurate or unbalanced, or leave out information the customer needs or the rules require. | Conduct & Customer Outcomes | High |
| R-154 | Missing information for deployers and users The people who operate or use a system have not been told its intended purpose, how well it performs, where it fails and what oversight it needs. | Transparency & Explainability | Medium |
| R-131 | Missing provider documentation A provider does not supply the information a deployer or downstream builder needs to assess, integrate and oversee its model. | Third-Party & Supply Chain | Medium |
| 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-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-153 | Overstated AI claims The organisation misdescribes what its AI does, how well it performs or how it is governed, to customers, investors or regulators. | Intellectual Property & Legal Liability | Medium |
| R-157 | Training data transparency gap The organisation cannot say what data a model was trained on, to the level regulators, customers or rights holders require. | Transparency & Explainability | Medium |
| R-037 | Undisclosed AI interaction A person deals with an AI system, by chat, voice or message, without being told that it is not a human. | Transparency & Explainability | Medium |
| R-156 | Unfaithful explanations An explanation given for a model's output does not reflect how the model actually reached it. | Transparency & Explainability | Medium |
| 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-144 | Unmarked synthetic content AI-generated or manipulated content is released without machine-readable marking, or without disclosure to the audience, where the law requires it. | Generative Content & Information Integrity | Medium |