24% of enterprises rely on human-in-the-loop models for most AI agent tasks.

11% of enterprises automatically block actions when AI agents exceed their scope.

63% of enterprises use action risk as a primary signal for governing AI agent behavior.

66% of enterprises have clear guardrails for defining AI agent boundaries.

8% of enterprises say AI agents never exceed their intended permissions.

50% of enterprises have at least partially documented governance policies for AI agent usage.

57% of organizations report moderate or high confidence in identity scoping for AI agents.

68% of organizations cannot clearly distinguish between human and AI agent activity.

52% of organizations use workload identities for AI agents, 43% rely on shared service accounts, and 31% allow agents to operate under human user identities.

73% of organizations expect AI agents to become vital within the next year.

AI agents operate across enterprise workflows: 67% are task automation agents, 52% are research agents, 50% are developer-assist agents, and 50% are security or monitoring agents.

85% of organizations use AI agents in production environments.

74% of organizations say AI agents often receive more access than necessary.

79% of organizations believe AI agents create new access pathways that are difficult to monitor.

52% of organizations say AI agents inherit access originally intended for humans or other systems at least sometimes.

Responsibility for AI agent identity and access is fragmented: 28% of organizations assign primary ownership to security leads, 21% to development/engineering, 19% to IT, and 9% to IAM teams.

33% of organizations do not know how often AI agent credentials are rotated.

22% of organizations report that access frameworks are applied very consistently to AI agents.

32% of organizations are uncertain how much time is required to implement and maintain authentication or credential handling for a typical AI agent.

77% of consumers are concerned about AI agents acting on their behalf online