Skip to main content

AI Risk Knowledge Base

A free, open-source reference for understanding, assessing, and controlling AI risk. Written for every level — from board to technical practitioner. 26 risk entries across 7 domains, four layers of depth per entry.

Get startedSee a full entryGitHub

Each entry covers four layers: a plain-English executive card, a practitioner overview with controls ownership and go-live criteria, an actionable controls detail, and a technical implementation guide with code examples. Built for use alongside your own risk assessments — not as a substitute for them.

A — Technical

Technical Risks

Hallucination, model drift, robustness failures, explainability gaps.

4 risk entries

B — Governance

Governance Risks

Accountability gaps, regulatory compliance, lifecycle governance, supply chain.

4 risk entries

C — Security & Adversarial

Security & Adversarial Risks

Data poisoning, prompt injection, model theft, deepfakes, AI-enabled attacks.

5 risk entries

D — Data

Data Risks

Training data quality, privacy and data protection, intellectual property.

3 risk entries

E — Fairness & Social

Fairness & Social Risks

Algorithmic bias and discrimination, harmful content, misinformation.

3 risk entries

F — HCI & Deployment

HCI & Deployment Risks

Automation bias, shadow AI, scope creep beyond intended use.

3 risk entries

G — Systemic & Macro

Systemic & Macro Risks

Concentration risk, environmental impact, workforce displacement, AI safety.

4 risk entries

Open source under MIT licence. Content is provided for informational purposes. Not legal, regulatory, or professional advice.
Basis: MIT AI Risk Repository · NIST AI RMF 1.0 & AI 600-1 · EU AI Act · OWASP LLM Top 10 · Documented AI incidents.