Author: Apurva Davé

Secret remediation is the process of responding to an exposed credential by revoking it, rotating it and removing every trace of it from your environment.
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.
Agentic AI introduces new cybersecurity risks, primarily concerning autonomous identity, tool chain exposure, and cascading compromises, requiring security teams to urgently adopt least-privilege identity frameworks and real-time monitoring designed specifically for self-directed, persistent workloads.
Securing MCP requires a fundamentally different approach than traditional API security.
How do you govern entities that can learn, adapt, and act independently while maintaining security and compliance?
AI agents are accessing sensitive systems with little oversight. Aembit’s new IAM for Agentic AI gives security teams policy-based control, secretless access, and full auditability—built for the speed and scale of AI.
The core problem is that human IAM was never built for workload scale or behavior.
Instead of treating access as a secrets problem, teams should treat it as an identity problem.
This struggle stems from a reliance on outdated, static credentials and a tension between development velocity and security.
One careless push unlocked 52 AI models, but the real story is how to keep this from happening again.