What the repository offers
MIT’s AI Risk Repository organizes risks drawn from published frameworks and classifications. Its causal and domain taxonomies provide different ways to explore the material. It is useful for learning, searching, and asking whether a local assessment has overlooked an important category of harm.
Do not copy every entry into a register
A research repository describes a broad landscape. Your organization needs risks tied to a particular use case and context. Select relevant concepts, follow their original sources, and rewrite them as concrete scenarios. Keep the connection to the source so another reader can inspect the reasoning.
Look beyond deliberate attacks
An AI system can create problems through unreliable information, inappropriate use, unequal performance, or misplaced reliance, even without an attacker. Bring those possibilities into the same conversation as data exposure and malicious manipulation. Then assign the right technical, business, or governance owner to each concern.
Use disagreement productively
A risk description may be broad, contested, or dependent on circumstances unlike your own. Record those differences. A reference library is a tool for better reasoning rather than a requirement to accept every listed risk as present. Explain why a scenario is applicable, uncertain, or outside the assessed scope.
Try this with a familiar use case
Choose a document assistant or an AI feature your team understands. Read one of the official resources below, write down a question it raises, and identify the local evidence needed to answer it. Keep your own interpretation separate from the source’s stated requirements.
Official resources
Independent educational guide. SyruSec is not affiliated with or endorsed by the organizations discussed. Full standards and materials remain subject to their publishers’ access and licensing terms.