Policy & Governance
AI Risk Management
The practice of identifying, assessing, and mitigating risks associated with AI systems and tools.
AI risk management is the systematic process of identifying potential harms from AI tools and systems, assessing their likelihood and impact, and implementing controls to reduce risk to acceptable levels. For enterprises using third-party AI tools (rather than building their own models), the primary risk domains include:
- Data exposure: sensitive information sent to AI tools that may be used for training or stored insecurely
- Vendor risk: AI providers with unclear data handling, retention, or sharing practices
- Compliance risk: AI usage that violates regulatory requirements (GDPR, HIPAA, SEC guidance)
- Operational risk: dependence on AI tools that may change behavior, pricing, or availability
- Reputational risk: AI outputs that produce biased, incorrect, or harmful results attributed to the organization
Why it matters
Regulators from the SEC to the EU are now explicitly asking organizations to demonstrate that they understand and manage their AI-related risks. A framework like NIST AI RMF provides structure, but the operational work of tracking tools, assessing vendors, and documenting decisions requires purpose-built tooling.