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
27 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-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-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-099 | Conformity assessment and registration failure A high-risk system is placed on the market or put into use without the required conformity assessment, declaration, marking or registration. | Governance & Accountability | High |
| R-006 | Data and model poisoning Training, fine-tuning or embedding data is manipulated so that the resulting model carries a vulnerability, a hidden behaviour or a bias its developers did not intend. | Model & Application Security | High |
| R-160 | Deskilling and loss of expertise Reliance on AI erodes the human expertise needed to check its output, to cover for it when it fails and to train the next generation. | Human Factors & Workforce | Medium |
| R-108 | Excessive retention of prompts and logs Prompts, outputs and conversation histories that contain personal data are kept indefinitely by the organisation or its provider. | Data Protection & Privacy | 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-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-090 | Misclassified AI risk tier A system is assigned a lower risk tier or regulatory class than it warrants, so it receives lighter controls than the harm it can cause. | Governance & Accountability | High |
| R-019 | Missing DPIA for high-risk AI processing AI processing that is likely to result in a high risk to individuals goes ahead without a data protection impact assessment, or with one that was never updated. | Data Protection & Privacy | High |
| 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-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-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-134 | No fallback for AI failure When an AI component fails or has to be withdrawn, no manual process, simpler model or degraded mode is ready to take over. | Operational Resilience | High |
| R-109 | Purpose creep in AI data use Data collected for one purpose is used to train or run AI for another purpose that the people concerned would not expect. | Data Protection & Privacy | Medium |
| 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-083 | Self-reinforcing feedback loops A model's outputs shape the data it is later trained or evaluated on, entrenching early errors and narrowing outcomes over time. | 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-107 | Unfulfillable data subject rights Requests for access, rectification, erasure or objection cannot be met because personal data sits inside a model, or in stores that nobody can search. | Data Protection & Privacy | 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-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 |
| 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-163 | Workforce displacement and job quality AI deployment removes roles or makes the remaining work worse, without assessment, involvement of the people affected or a plan for transition. | Human Factors & Workforce | Medium |