By combining identity-based access control with content inspection, this closes a gap most teams are still trying to manage with separate tools and after-the-fact controls.
AI agent identity security is the set of practices and controls that treat AI agents as distinct, governable identities with their own authentication, authorization and audit requirements.
Agentic AI guardrails are the technical controls, policy frameworks, and oversight mechanisms that define what an AI agent can do, what it can access and when it needs to stop and ask a human.
The OWASP Top 10 for LLM Applications is the most widely referenced framework for understanding these risks. First released in 2023, OWASP updated the list in late 2024 to reflect real-world incidents, emerging attack techniques and the rapid growth of agentic AI.
JIT access replaces the common practice of issuing and locally storing keys with a workflow that evaluates a workload’s rights every time it tries to access sensitive data.
The dynamic nature of MCP makes a lack of visibility dangerous, as attackers can exploit complex workflows and ephemeral infrastructure to hide malicious activity.
Aembit’s AWS Secrets Manager integration makes it easier to protect AI and workload access today – and evolve toward short-lived, policy-driven authentication.
