Agentic AI is changing how software behaves — introducing autonomous agents that act, adapt, and make decisions in real time. But most identity and access models weren’t built for non‑human actors operating continuously at runtime. This fundamental mismatch is the central challenge addressed in the IBM webinar, which explores how to securely deploy these autonomous workloads without stifling innovation.
The core problem is the "runtime identity gap"—the lack of dynamic, least-privilege access for these autonomous agents. Traditional IAM models, designed for static human users or predictable machines, break down when faced with AI that makes independent decisions and interacts across hybrid and multicloud environments. The session highlights the need to modernize identity, secrets, and access management to regain control.
Key solutions include enforcing dynamic, just-in-time access for agent-to-infrastructure and machine-to-machine interactions, ensuring agents only have the permissions they need at any given moment. The presented patterns focus on securing these continuous, non-human interactions. Ultimately, the IBM approach enables platform and security teams to manage these new risks by building security directly into the identity fabric for agentic workloads, allowing for safe and agile adoption of autonomous AI across any environment.
Click on the Youtube Link here to get into the details how to secure agentic AI identity, access and control at runtime source.
