AI Risk Assessment Framework
Systematic approach to identifying, evaluating, and mitigating risks in AI systems throughout their lifecycle, from development to deployment and monitoring.
- • Threat modeling
- • Vulnerability scanning
- • Attack surface analysis
- • Stakeholder interviews
- • Probability assessment
- • Impact analysis
- • Risk scoring
- • Prioritization matrix
- • Control selection
- • Implementation planning
- • Residual risk acceptance
- • Continuous monitoring
Risk Assessment Process
Establish the context for your AI risk assessment by defining system boundaries, stakeholders, and assessment objectives.
Key Activities
- • Define AI system scope and boundaries
- • Identify stakeholders and their concerns
- • Establish risk criteria and acceptance levels
- • Document assessment methodology
Use multiple techniques to comprehensively identify risks across technical, operational, and organizational dimensions.
Technical Risks
- • Model vulnerabilities
- • Data poisoning
- • Adversarial attacks
- • System failures
Operational Risks
- • Deployment errors
- • Monitoring gaps
- • Incident response
- • Maintenance issues
Compliance Risks
- • Regulatory violations
- • Privacy breaches
- • Audit failures
- • Documentation gaps
Ethical Risks
- • Bias and discrimination
- • Fairness issues
- • Transparency gaps
- • Accountability concerns
Analyze each identified risk to determine its likelihood of occurrence and potential impact on the organization.
Risk Matrix
Select and implement appropriate controls to reduce risks to acceptable levels based on your risk appetite.
Risk Avoidance
Eliminate the risk by not proceeding with the activity
Risk Reduction
Implement controls to reduce likelihood or impact
Risk Transfer
Share risk with third parties (insurance, contracts)
Risk Acceptance
Accept residual risk within defined tolerance
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