The adoption of generative AI is accelerating in enterprises with initiatives like deploying LLM copilots for software development, automating customer service, and integrating AI in financial operations. However, securing these AI systems requires more than just algorithms; it necessitates enforcement of identity and device posture at every access point, as each integration poses new risks. Custom-built AI agents can inadvertently expand internal attack surfaces, while third-party tools often suffer from governance issues. Compromised AI agents in critical systems can lead to significant security breaches.
Generative AI has moved beyond the hype cycle. Enterprises are deploying LLM copilots to accelerate software development, automate customer service workflows, and integrate AI into financial operations.
Most teams miss that every LLM access point or website is a new identity edge, and every integration adds risk unless identity and device posture are enforced.
Securing AI isn't about the algorithm, it's about who or what device is talking to it, and what permissions that interaction unlocks.
AI agents can take actions on a human's behalf and access data like a human would, becoming a backdoor to sensitive business-critical systems.
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