Question 1

How should AI assets be scoped and governed to strengthen security?

Accepted Answer

61% of surveyed executives say their organization’s AI models, assets, or data have been compromised during the last 12 months. Strengthening security involves enforcing zero trust authentication for every AI asset, service, and event. AI workflows should be defined and segmented at a granular level. AI agents should be treated as first class identities: authenticated, authorized, monitored, and supervised in real-time. Runtime security can secure the live use of AI, preventing model manipulation and prompt injections.

Question 2

How can AI exposure management improve an organization’s security baseline?

Accepted Answer

76% of executives say that by 2027, AI exposure management will be the primary method for unifying prevention, detection, and response activities. This security approach unifies visibility across hybrid environments so risk can be assessed on demand. Contextual asset management insights can be used to assess risks relative to AI models and agents.

Question 3

How can integrated IT and information security (IS) architecture lay the groundwork for trusted autonomy in enterprises?

Accepted Answer

77% of surveyed executives say their organizations need better integration across hybrid cloud, AI, and security platforms. Security, technology, and AI counterparts need to work together to define the integrated operations environment where AI models, agents, data, and assets can scale securely and reliably across domains. Enterprises should focus on IT/IS interoperability and common governance—not just tool integration.

Question 4

How can enterprises build a secure AI ecosystem where responsibility is shared and accountability is operationalized?

Accepted Answer

82% of executives say that AI security is a shared responsibility across suppliers, providers, and partners. A first step is to decide what AI infrastructure you want to own and what AI outcomes you want to outsource. Other key steps include: assessing current AI procurement practices to rationalize your AI solution and vendor footprint, and picking strategic partners that fill critical operational and security gaps.

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