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
19 of 169 risks shown.
| ID | Risk | Category | Severity |
|---|---|---|---|
| R-071 | Abuse of a deployed AI service Users turn the organisation's AI system to purposes it was never offered for, including generating harmful material, at the organisation's cost and in its name. | Model & Application Security | 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-137 | AI incident response gap AI-specific failures are not covered by the incident process: they have no classification, no playbook and no trained responders. | Operational Resilience | 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-136 | AI monitoring and alerting gap A system's behaviour in production is not measured against thresholds that trigger action, so degradation, misuse and harm are noticed late or by accident. | Operational Resilience | High |
| R-056 | Cascading failures in agent workflows An error or compromise in one agent or step propagates through chained agents and automated workflows faster than people can notice or intervene. | Agentic & Autonomous Systems | High |
| R-022 | Disparate impact monitoring gap Once an AI system is in use, nothing measures whether its outcomes differ between groups, so discrimination that emerges after launch goes undetected. | Fairness, Bias & Impact | High |
| 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-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-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-097 | Post-market monitoring gap No plan exists to collect and act on evidence of how a system performs and what effects it has after release. | Governance & Accountability | Medium |
| R-096 | Record-keeping and logging failure The system does not automatically record the events needed to reconstruct what it did, or the logs are not retained for as long as they may be needed. | Governance & Accountability | High |
| R-058 | Rogue agent behaviour An agent departs from its intended function or acts deceptively or outside its mandate, through misaligned objectives, emergent behaviour or a compromise that persists, while each action appears legitimate. | Agentic & Autonomous Systems | High |
| 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-098 | Serious incident reporting failure A reportable AI incident is not recognised, escalated or notified to the relevant authority within the time required. | Governance & Accountability | High |
| R-011 | Unbounded resource consumption With no limit on the volume, size or cost of requests, a user or attacker drives inference far beyond what the system was sized or budgeted for, degrading service or running up charges. | Model & Application Security | Medium |
| R-038 | Untested AI failure scenarios Scenario testing does not include AI-specific disruption, so the organisation does not know whether it could stay within tolerance if its AI failed. | Operational Resilience | Medium |
| R-060 | Untraceable agent actions Actions taken by agents cannot be reconstructed or attributed to an instruction, a decision and an accountable person, so errors cannot be investigated and accountability fails. | Agentic & Autonomous Systems | High |
| R-093 | Weak independent assurance of AI Second-line review and internal audit do not cover AI with sufficient skill or frequency, so control failures persist undetected. | Governance & Accountability | Medium |