Among 116 enterprises surveyed, over half have deployed AI agents in production, with a significant number experiencing security incidents or near-misses. Two-thirds enforce strict permissions on AI agents at runtime, yet fewer than 20% isolate their highest-risk agents, revealing a major gap in containment strategies. Credential sharing remains common, occurring in nearly two-thirds of agent fleets, complicating identity management and incident attribution. Most enterprises rely on security tools from major model providers and cloud hyperscalers such as OpenAI, Microsoft Azure, and Anthropic, but satisfaction with current tooling is high despite plans to replace or upgrade security solutions within a year. The data shows a defensive posture focused on monitoring and permission enforcement, but lacking effective isolation to limit damage when breaches occur. Budget allocations for AI agent security are growing, with a third of enterprises dedicating more than 10% of their security budget to this area. Confidence in defense capabilities is split, with a notable portion believing AI-enabled attackers are outpacing defenses. Urgency for better security controls is driven by real incident experience, yet key protections like scoped agent identity and runtime sandboxing remain underutilized, highlighting an urgent need to close the containment gap and rethink current security approaches to autonomous AI agents.
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