How do enterprises assess the security risks of AI Agents

Enterprises assess AI Agent security risks through systematic processes identifying vulnerabilities in development, deployment, and operation. This ensures trust and protection against misuse.

Key principles include evaluating the entire AI lifecycle: model inputs/outputs, data handling pipelines, integration points with other systems, and ongoing monitoring. Assessment covers technical security (prompt injection, data leakage) and governance (compliance with regulations like GDPR or sector-specific rules). Using established frameworks (NIST AI RMF, MITRE ATLAS) and conducting threat modeling focused on agent-specific behaviors is essential. Involvement of cross-functional teams (security, compliance, AI developers) is crucial.

The process typically follows these steps: 1. Inventory AI Agents and associated data flows. 2. Identify potential threats and attack vectors specific to agent autonomy. 3. Analyze vulnerabilities in the model, data, and supporting infrastructure. 4. Evaluate the potential impact of successful attacks. 5. Prioritize risks and implement controls (e.g., input validation, output filtering, access restrictions, auditing). 6. Continuously monitor performance and security posture post-deployment. This enables secure AI adoption while safeguarding assets.