A real-world example shows an agent correctly denying a request for a specific company's data but leaking other firms' data on a generic prompt. This highlights that agent security isn't about blocking bad prompts, but about solving the deep, contextual authorization problem of who is using what agent to access what tool.

Previously, systems were passively protected because humans wouldn't explore the full extent of their permissions. Hyper-productive AI agents can now perform exhaustive searches of every available data asset and tool, uncovering and exploiting misconfigured permissions that were once hidden in plain sight.

A critical hurdle for enterprise AI is managing context and permissions. Just as people silo work friends from personal friends, AI systems must prevent sensitive information from one context (e.g., CEO chats) from leaking into another (e.g., company-wide queries). This complex data siloing is a core, unsolved product problem.

Companies are encouraging non-technical employees to use AI tools to build solutions and automate workflows. These "citizen developers," lacking a technical background, inadvertently create risks by mishandling sensitive data, deleting system artifacts, or leaking corporate IP into external AI models, creating a new attack surface for security teams to manage.

AI coding agents thrive because developers have broad codebase access and work in a text-based medium. Enterprise knowledge work is stalled by fragmented data access, complex permissions, and multi-modal information (calls, meetings), which are significant hurdles for current AI.

For convenience, tech company employees often use AI agents in "dangerously skip permissions mode," where the AI inherits all of the user's permissions without oversight. This common practice is a major vector for rogue deployments.

For enterprises, the raw capability of foundation models is a security risk, not a selling point. The real product value lies in building "boundaries"—robust permissions, approvals, and audit logs that make powerful models safe to deploy company-wide.

Developers are granting AI agents overly broad permissions by default to enable autonomous action. This repeats past software security mistakes on a new scale, making significant data breaches and accidental destruction of data inevitable without a "security by design" approach.

An AI agent capable of operating across all SaaS platforms holds the keys to the entire company's data. If this "super agent" is hacked, every piece of data could be leaked. The solution is to merge the agent's permissions with the human user's permissions, creating a limited and secure operational scope.