AI Risk Management

The systematic process of identifying, assessing, mitigating, and monitoring risks associated with AI systems throughout their lifecycle.

Also known as:AI Risk AssessmentAI Risk Framework

What is AI Risk Management?

AI Risk Management is the discipline of identifying, analyzing, and addressing the potential negative impacts of AI systems. It encompasses technical risks (model failures, security vulnerabilities), operational risks (integration issues, skill gaps), and strategic risks (regulatory changes, reputational damage).

Risk Categories

Technical Risks

  • Model accuracy degradation
  • Adversarial attacks
  • Data poisoning
  • System failures

Ethical Risks

  • Bias and discrimination
  • Privacy violations
  • Lack of transparency
  • Unintended consequences

Operational Risks

  • Integration failures
  • Skill gaps
  • Vendor dependencies
  • Scalability issues

Regulatory Risks

  • Non-compliance with AI laws
  • Cross-border data issues
  • Liability concerns

Frameworks and Standards

  • NIST AI Risk Management Framework
  • ISO/IEC 23894 (AI Risk Management)
  • EU AI Act requirements
  • Industry-specific guidelines