A Practical Framework for Securing Enterprise AI Across Its Lifecycle
Summary
Darktrace outlines five areas for enterprise AI security: misuse and emergent behavior, runtime monitoring, secure development, supply-chain risk, and organizational readiness, emphasizing visibility and controls across the AI lifecycle.
Key points
- AI security must address models and agents alongside enterprise data, identities, APIs, infrastructure, and third-party integrations.
- Prompt injection, unintended agent actions, and sensitive data in prompt histories can create risk even without a conventional account compromise.
- Runtime safeguards include monitoring agent behavior, controlling outbound connections, and retaining state for investigation.
- Development controls should cover permissions, infrastructure configuration, AI-generated code, and dependencies.
- Supply-chain measures include discovering shadow AI, checking model and data provenance, and assessing how suppliers handle enterprise information.
- The framework also calls for AI-aware reporting, testing, incident readiness, and staff training.
- The article recommends aligning governance with ISO/IEC 42001 and the NIST AI Risk Management Framework.
Article Details
- Topic
- A lifecycle-based framework for securing enterprise AI, including governance, identity and access management, data protection, runtime monitoring, development security, supply-chain security, and organizational readiness.