Assessing and Controlling AI Risk: From Intake to Monitoring

Assessing and Controlling AI Risk: From Intake to Monitoring

Assessing and Controlling AI Risk: From Intake to Monitoring

A guided, multi-course journey that builds this skill from the ground up. Work through the chapters in order, or jump to any lesson.

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1Why AI Use Becomes Enterprise Risk 2Recognizing Sensitive Information Disclosure 3Bounding Excessive Agency in Practice 4Handling Policy Mismatch and Unreliable Outputs

5Scoping the Intake Context Map 6Identifying Users and Affected Parties 7Mapping Data Flows and Outputs 8Documenting Connections and Unknowns

9Write Event-Based Risk Statements 10Rate Impact and Likelihood From Evidence 11Document Uncertainty and Evidence Quality 12Propose Treatment and Residual Risk 13Verification, Tolerance, and Output Risk

14Designing Local Access Tiers 15Bounding Tool Agency With Least Privilege 16Spotting Excessive Agency Risks (LLM03)17Separating Users From Service Permissions 18Verifying Access Controls and Shadow Use

19Classify Data Before AI Sharing 20Minimize and Redact for Task Need 21Detect Re-Identification in “Redacted” Text 22Map Disclosure Surfaces Beyond Prompts 23Decide Pause or Proceed Under Unknown Retention

24Translate Policy Into Checkable Conditions 25Build the Policy-Check Record 26Define Evidence, Checks, and Consequences 27Stop a Sensitive-Data Near-Miss

28Define Packet Sections and Reviewability 29Compile Risks With Residual Confidence 30Map Controls to Specific Risks 31Write Policy Status, Alternatives, and Open Questions

32Routing the Packet to the Right Owner 33Applying Risk Tolerance to Escalate Open Items 34Recording the Decision and Binding Conditions

35Turning Conditions Into Monitoring Duties 36Designing Reassessment Triggers 37Reading Telemetry Without Overclaiming 38Updating the Packet and Forcing Re-Decisions

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