Tag: AI Workloads

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.
From Coca-Cola to Campbell Soup, Renee Guttmann knows what lasts as security changes.
How do you govern entities that can learn, adapt, and act independently while maintaining security and compliance?
Aembit’s AWS Secrets Manager integration makes it easier to protect AI and workload access today – and evolve toward short-lived, policy-driven authentication.
From rule-based chatbots to autonomous agentic AI, we’ve come a long way in past three decades.
Credentialitis isn’t just a clever name. It’s a real condition plaguing modern IT teams. Dr. Seymour Keys is here to walk you through the symptoms, the screening, and the treatment.
AI agents face unique risks from static API keys and prompt injection. Learn why workload identity eliminates credentials for LLM workflows.