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
20 of 169 risks shown.
| ID | Risk | Category | Severity |
|---|---|---|---|
| R-052 | Agentic supply chain compromise Tools, skills, connectors or sub-agents loaded at run time are malicious, tampered with or silently changed, giving an outside party a route into the agent's actions and data. | Agentic & Autonomous Systems | High |
| R-133 | AI service outage An inference service, model endpoint or supporting platform becomes unavailable, and the business process that depends on it stops or queues. | Operational Resilience | High |
| R-150 | Confidential information disclosed to AI tools Trade secrets, legally privileged material or information held in confidence for others are entered into AI tools and leave the organisation's control. | Intellectual Property & Legal Liability | High |
| R-018 | Cross-border AI data transfer risk Personal data processed by an AI system, often through a cloud or model provider, is sent outside the UK without a valid transfer mechanism. | Data Protection & Privacy | Medium |
| R-033 | Inadequate AI vendor due diligence An AI supplier is taken on without due diligence proportionate to how material its system is, or with a generic assessment that never asks the questions specific to AI. | Third-Party & Supply Chain | High |
| R-008 | LLM application supply chain vulnerabilities Vulnerable, tampered or mislicensed components, including base models, adapters, datasets, libraries and hosting platforms, enter an AI application through its supply chain. | Model & Application Security | High |
| R-067 | Malicious model artefacts and unsafe loading A model file or dependency obtained from a public source executes code or carries hidden behaviour when loaded. | Model & Application Security | High |
| 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-148 | Model and dataset licence breach A model, dataset or component is used in breach of its licence or usage terms, for example commercially under a research-only licence. | Intellectual Property & Legal Liability | Medium |
| R-015 | Model backdoors and trojans A model contains a hidden behaviour that an adversary can activate with a trigger known only to them, while the model behaves normally on all other inputs. | Adversarial ML & Model Integrity | Critical |
| R-132 | Model deprecation and forced migration A provider withdraws a model version on its own timetable, forcing the organisation to migrate to a different model before it has tested the replacement. | Third-Party & Supply Chain | Medium |
| R-165 | Model monoculture and herding Many organisations rely on the same few models, providers and data, so they make the same errors, react in the same way and fail at the same time. | Systemic & Societal | High |
| R-036 | Sub-outsourcing visibility gap An AI supplier relies on its own suppliers for models, hosting, data or labelling, and the organisation has no visibility of that chain and no contractual hold over it. | Third-Party & Supply Chain | Medium |
| R-004 | Third-party model and dataset provenance A model, dataset or component is adopted from an external source with no verified account of where it came from, how it was made, what rights attach to it or how it has changed. | Third-Party & Supply Chain | Medium |
| R-152 | Unallocated liability for AI harm Contracts, insurance and internal accountability do not settle who bears the loss when an AI system causes harm. | Intellectual Property & Legal Liability | High |
| 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-129 | Unassessed AI in third-party products A supplier switches on AI features inside a product already in use, bypassing the organisation's assessment and approval. | Third-Party & Supply Chain | High |
| R-100 | Unrecognised provider obligations An organisation rebrands, substantially modifies or repurposes an AI system and takes on a provider's legal duties without realising it. | Governance & Accountability | Medium |
| R-039 | Vendor concentration and exit strategy gap The organisation depends on a single AI model or supplier for a critical function, with no tested exit plan and no alternative identified. | Third-Party & Supply Chain | High |
| R-130 | Vendor reuse of organisation data A provider uses the organisation's prompts, documents or outputs to train or improve its own models, or keeps and reviews them, beyond what the organisation intended. | Third-Party & Supply Chain | High |