BasicAgent
AI Risk Management
A practical AI risk management approach with assessment, mitigation, and framework alignment for LLM and ML systems.
AI risk management is the discipline of identifying, assessing, and mitigating risks across AI systems. It is the backbone of compliance and trust.
The risk management workflow
- Risk assessment: identify impact, likelihood, and exposure
- Risk mitigation: controls, monitoring, and approvals
- Evidence: audit logs, evaluation results, incident records
What to document
- risk tiers by system and use case
- mitigation controls and owners
- evaluation and monitoring results
Related pages
- AI risk management framework:
/ai-risk-management-framework/ - AI risk assessment:
/ai-risk-assessment/ - AI risk mitigation:
/ai-risk-mitigation/ - AI compliance framework:
/ai-compliance-framework/
Create account
Build narrative
Follow a coherent path from thesis to lab notes to proof-of-work instead of isolated pages.
Step 1
Intelligence systems office
The strategic map for what is being built and why.
Step 2
Lab notes
Build footprints and progression logs as proof-of-work.
Step 3
Control surface
Governance and monitoring architecture for operational reliability.
Step 4
Private alignment
Convert insight into execution with scoped collaboration.