Tag: AI Workloads

Teams can query workload identity data in plain language, investigate activity, and move faster without leaving the Aembit platform.
Most workload credentials, the API keys, tokens and passwords that connect your services, carry “always on” access that never expires.
Built in the open with customers, now ready to run against real agent workflows.
What starts as a tooling decision ends up shaping cost, reliability, and how far your workflows actually scale before they break down.
AI agent identity breaks down when agents authenticate across OAuth, API keys and managed identities simultaneously. Learn why single-protocol solutions fail.
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.
These four architectural patterns reveal how AI agents differ fundamentally from traditional workloads.