How to Secure AI in the Enterprise

How to Secure AI in the Enterprise outlines a practical, vendor-neutral framework for managing the growing cybersecurity risks associated with enterprise AI adoption. It argues that traditional security approaches are insufficient, as AI expands the attack surface through models, data, agents, APIs, and third-party integrations. Organisations must therefore rethink security across the entire AI lifecycle, from development to deployment and real-time operation.

The report identifies five core categories of AI risk: misuse and emergent behaviours, monitoring and controlling AI in operation, protecting AI development and infrastructure, securing the AI supply chain, and strengthening readiness and oversight.

These risks require monitoring, validation, and controls, as well as real-time oversight of AI agents, including their actions, connections, and data flows.

The guide also highlights the importance of secure configurations, robust architectures, and scanning of both infrastructure-as-code and AI-generated code to prevent vulnerabilities from entering production. It underscores the need to address risks from shadow AI use, third-party vendors, external models, and dependencies, emphasising validation, access control, and continuous assessment.

Lastly, it emphasised workforce training to ensure organisations can detect and respond to AI-driven threats effectively. It frames security around trust, accountability, and resilience, aligning with frameworks that to support governance and compliance.

Ultimately, the guide concludes that securing AI is an ongoing process requiring visibility, clear ownership, and continuous oversight. Organisations that integrate security into every layer of AI adoption will be better positioned to innovate safely whilst maintaining trust and operational resilience.