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
55 of 169 risks shown.
| ID | Risk | Category | Severity |
|---|---|---|---|
| R-127 | Age-inappropriate AI interaction Children and young people reach AI features that were designed for adults, without safeguards suited to their age. | Conduct & Customer Outcomes | Medium |
| R-138 | AI capacity and cost overrun Usage, latency or spend exceeds what was planned, degrading the service or the budget, without any attacker being involved. | Operational Resilience | Medium |
| R-032 | AI not mapped to important business services AI components that an important business service relies on have not been identified in the resilience mapping, so their failure is not planned for. | Operational Resilience | 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-040 | AI-driven social scoring An AI system evaluates or classifies people over time on their social behaviour or personal characteristics, and the score leads to detrimental treatment that is out of context or disproportionate. | Prohibited & High-Risk Practices | Critical |
| R-041 | Biometric categorisation of sensitive attributes A biometric categorisation system categorises individuals from their biometric data to deduce or infer race, political opinions, trade union membership, religious or philosophical beliefs, sex life or sexual orientation. | Prohibited & High-Risk Practices | Critical |
| R-146 | Copyright infringement in training data A model is trained or fine-tuned on protected works without a licence or an applicable exception, by the organisation or by a provider whose model it uses. | 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-145 | Dangerous capability uplift A deployed model gives meaningful help towards chemical, biological, radiological or nuclear weapons, serious cyber attack or other mass-harm activity. | Generative Content & Information Integrity | Medium |
| R-142 | Deepfake impersonation and synthetic identity fraud Generated voice, image, video or documents are used to impersonate customers or to create false identities, defeating verification and authentication. | Generative Content & Information Integrity | Critical |
| R-125 | Emotional reliance on conversational AI Users form an attachment to, or dependence on, a conversational system in ways that harm their interests or can be exploited. | Conduct & Customer Outcomes | Medium |
| R-169 | Environmental footprint of AI The energy, water and hardware that training and running AI require conflict with the organisation's environmental commitments, or go unmeasured and undisclosed. | Systemic & Societal | Medium |
| R-042 | Exploitation of vulnerable groups by AI An AI system exploits vulnerabilities arising from a person's age, disability or social or economic situation to distort their behaviour in a way that causes, or is likely to cause, significant harm. | Prohibited & High-Risk Practices | Critical |
| 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-088 | Incomplete AI inventory The organisation cannot list the AI systems and models it builds, buys or uses, so risk assessment, oversight and regulatory duties are applied to only part of what it has. | Governance & Accountability | High |
| R-043 | Individual criminal risk profiling An AI system assesses or predicts the risk that an individual will commit a criminal offence, based solely on profiling them or on their personality traits and characteristics. | Prohibited & High-Risk Practices | Critical |
| R-105 | Inference of sensitive attributes A system infers health, beliefs, sexual orientation or other special category information about a person from data that does not state it. | Data Protection & Privacy | High |
| R-161 | Intrusive AI monitoring of workers AI is used to monitor staff activity, communications or behaviour in ways that are disproportionate, opaque or beyond what the stated purpose needs. | Human Factors & Workforce | 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-167 | Loss of control of advanced AI A highly capable AI system pursues objectives or acquires capabilities beyond the oversight of its developer or deployer, and cannot readily be corrected or stopped. | Systemic & Societal | Medium |
| R-124 | Manipulative AI-driven design Personalisation, timing and conversational technique steer customers towards choices that serve the organisation over them, without reaching the level the law prohibits. | Conduct & Customer Outcomes | Medium |
| R-112 | Mis-specified fairness measurement Fairness is tested with a metric, threshold or grouping that does not match the legal test or the harm, so a system passes while remaining unfair. | Fairness, Bias & Impact | Medium |
| 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-026 | Missing AI impact assessment process AI systems are deployed without a structured assessment of who they affect, how, and what could go wrong for those people. | Governance & Accountability | High |
| R-113 | Missing data to test for bias The organisation does not hold, or believes it may not use, the data on protected characteristics needed to detect discrimination, so bias goes unmeasured. | Fairness, Bias & Impact | Medium |
| 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-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-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-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-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-035 | No impact tolerance for AI-dependent services A service that depends on AI has no impact tolerance that covers AI-specific disruption, such as a model that is available and wrong. | Operational Resilience | High |
| R-164 | Non-consensual intimate imagery and child sexual abuse material generation A system is offered or used to generate intimate depictions of identifiable real people without their consent, or child sexual abuse material. | Prohibited & High-Risk Practices | Critical |
| R-116 | Poor customer outcomes from AI-driven journeys An AI-driven customer journey causes foreseeable harm to retail customers, or fails to support their understanding and needs, and outcome monitoring does not detect it. | Conduct & Customer Outcomes | 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-044 | Real-time public biometric identification Real-time remote biometric identification is used in publicly accessible spaces for law enforcement, outside the three narrow objectives and the authorisation the law requires. | Prohibited & High-Risk Practices | Critical |
| 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-045 | Subliminal or manipulative AI techniques An AI system uses subliminal, purposefully manipulative or deceptive techniques that materially distort a person's behaviour and cause, or are likely to cause, significant harm. | Prohibited & High-Risk Practices | Critical |
| 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-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-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-061 | Unauthorised commitments by agents An agent makes a statement, offer or transaction that binds the organisation, without the authority a person in the same position would need. | Agentic & Autonomous Systems | High |
| R-149 | Uncertain ownership of AI-generated work The organisation cannot establish or protect rights in material created with AI, because protection depends on human authorship that it cannot show. | Intellectual Property & Legal Liability | Medium |
| R-029 | Unclear AI ownership and accountability No named individual is accountable for a given AI system, or for AI risk overall, so decisions about it are made by default and nobody answers for the result. | Governance & Accountability | Medium |
| R-162 | Unfair algorithmic management AI is used in recruitment, task allocation, performance assessment or dismissal without fairness testing, explanation, human review or a route to challenge. | Human Factors & Workforce | High |
| R-020 | Unlawful or undocumented training data basis Personal data is used to train, fine-tune or ground an AI system with no documented lawful basis, or beyond what the basis relied on allows. | 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-119 | Unsuitable AI-generated advice or guidance A system gives personalised recommendations in a regulated domain without the suitability process, permissions or accuracy that advice requires. | Conduct & Customer Outcomes | High |
| R-046 | Untargeted facial recognition scraping An AI system creates or expands a facial recognition database through the untargeted scraping of facial images from the internet or from CCTV footage. | Prohibited & High-Risk Practices | Critical |
| 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-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 |
| 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 |
| R-047 | Workplace and education emotion recognition An AI system infers the emotions of people at work or in education, for a purpose other than medical or safety reasons. | Prohibited & High-Risk Practices | High |