Learning series
Catastrophic AI Risk
Catastrophic AI risk may be regulated primarily at the frontier-model level, but risk does not stop at the model provider. This series shows risk and audit practitioners how to reason from severe harm back to the capabilities, conditions, controls, and evidence that matter in their own environment.
Why You Should Care About Catastrophic AI Risk
Why extreme-risk thinking matters even when your organization does not build frontier models.
What California and Illinois Are Regulating
What the state laws actually cover, and where their legal boundaries stop.
Start With the Harm and Work Backward
Use ideas from safety engineering to map severe harm, pathways, controls, and evidence.
Your Model Provider Does Not Own All of Your Risk
How upstream model capabilities and safeguards interact with your deployment context.
Auditing the Risk Chain
Questions practitioners can use to test prevention, detection, feedback, response, and residual risk.
The AuditDIFF Lens
Don't start with the controls you have. Start with the harm you need to prevent.
The series uses catastrophic-risk debates as a practical entry point into ordinary AI risk management. It does not assume every organization faces a legally defined catastrophic risk or inherits a frontier developer's legal duties.
Parts 1 and 2 establish the concept and legal context. Parts 3 through 5 turn the idea into a practitioner method using safety-engineering concepts, third-party risk analysis, feedback mechanisms, and audit questions.