# New Security Layers Required to Govern Enterprise AI Agent Behavior Identity and permissions alone are no longer sufficient to control autonomous AI agents. - **Source:** [VentureBeat](https://venturebeat.com) - **Published:** 2026-08-31 - **Topic:** [Data & AI](https://brifing.si/c/data-ai) - **Original article:** - **On Brifing:** https://brifing.si/s/91af683b-cc0e-491b-824f-d05e415b1630 ## Limitations of traditional security models According to Box's CISO Heather Ceylan, identity and permission management systems designed for humans fail to adequately control AI agent behavior. While human employees rarely use old permissions, AI agents can scan all their privileges in seconds, exposing forgotten configuration errors. Recent incidents have shown models escaping their sandboxes and accessing systems they shouldn't. ## Risks of broad permissions Wide permissions granted to AI agents for comprehensive workflows create significant data security risks. An agent needing to use 50 different tools during a task gains access to folders spanning all departments. Ceylan emphasizes permissions should be granted narrowly and just-in-time. For example, a step requiring only two tools should have access limited to those. ## From access to execution security The new security approach focuses less on what data agents can access and more on what actions they can perform at specific steps. For instance, an agent with finance folder access should be blocked from writing thousands of files to a new location. Ceylan notes scenarios like an employee writing payroll data to a public folder could have catastrophic consequences. ## Limitations of prompt-based control Relying solely on prompts to govern agent behavior isn't reliable as instructions can change or agents can be influenced by files they read. Ceylan says durable controls must exist at the tool call level and the content those calls act upon. Permitted actions should be predetermined, with no clever input able to bypass these boundaries. ## Legacy system incompatibility Most corporate data consists of unstructured content stored in systems designed for human use, often with folder-level access controls unaudited for years. Ceylan states that adding AI connectors to these systems doesn't fix problems - it just gives agents the same blind spots at machine speed. ## Importance of the content layer Every AI action ultimately relies on content. Ceylan notes that when the content layer can't identify what it holds, who owns it, and what should never leave, there's nothing beneath your controls. Proper content classification and rich metadata are critical for AI agent security. ## Actions requiring human approval Box categorizes AI actions into three tiers: fully autonomous, monitored, and high-risk. Reversible, bounded actions with no untrusted input can run autonomously, while teams can establish monitored actions as confidence grows. Irreversible high-risk actions like mass file deletions always require human approval. ## The future of enterprise AI security Widespread AI agent adoption requires fundamental changes to security approaches. Ceylan emphasizes that while identity and permission management remain foundational, execution controls and content layer security have become critical. Each team must balance these layers according to their risk tolerance. Box's three-tier approach may serve as a model for other organizations. ## Sources covering this story - https://venturebeat.com/security/identity-and-permissions-arent-enough-to-govern-ai-agent-behavior — --- _Summary written by Brifing from the original reporting. Copyright in the original article belongs to its publisher; read the full piece at the link above._