AI Risk Library
A structured taxonomy of documented AI risks. Each entry covers what the risk is, how it manifests, how to detect it, and what mitigation strategies are available.
Vendor Concentration
When AI deployment depends on a small number of providers, organizations face pricing, availability, and policy risks that cannot be mitigated by technical controls alone.
Prompt Injection: Definition, Attack Patterns, Detection, and Mitigation
Prompt injection allows attackers to override AI system instructions through crafted inputs. Patterns, detection methods, and mitigations.
Overreliance on AI: Automation Bias, Skill Atrophy, and Organizational Controls
Overreliance on AI systems leads to degraded human judgment and missed errors. Patterns, warning signs, and organizational controls.
AI Bias: Types, Impact Assessment, Detection, and Mitigation
AI systems can encode and amplify existing biases in training data, model design, and deployment context. Assessment frameworks and mitigation approaches.
AI Data Leakage: Patterns, Detection, and Mitigation
Data leakage through AI systems occurs when sensitive information is exposed through model outputs, training data memorization, or API logging. This entry covers five documented patterns, a risk matrix, detection methods, and regulatory implications.
AI Hallucination: Definition, Patterns, Detection, and Mitigation
AI hallucination occurs when a model generates confident, plausible-sounding information that is factually incorrect. This entry covers how it happens, how to detect it, and what mitigation strategies are available.