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
8 of 169 risks shown.
| ID | Risk | Category | Severity |
|---|---|---|---|
| R-030 | AI literacy gap in the deploying organisation Staff who use, rely on or make decisions about AI systems do not understand what those systems can and cannot do, so they misuse or over-trust them. | Human Factors & Workforce | Medium |
| R-070 | AI-enabled social engineering of staff Attackers use generated voice, video and text to impersonate executives, customers or suppliers convincingly enough to defeat staff verification. | Model & Application Security | High |
| R-158 | Automation bias and over-reliance People accept AI outputs without the scrutiny they would give another source, and the system's errors pass into decisions unchallenged. | Human Factors & Workforce | 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-023 | Inadequate AI resourcing and competence AI systems are adopted without the people, skills, tooling and budget needed to build, oversee and operate them safely. | Governance & Accountability | Medium |
| 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-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-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 |