Redefining Security for the Agentic Era

AI agents operating autonomously at machine speed expose blind spots in traditional security models that rely on static indicators. Traditional perimeter defenses can't detect when agents access unauthorized data through legitimate channels or understand the intent behind encrypted agent communications. A new security paradigm is emerging that uses natural language processing to inspect semantic intent, extends protection to the kernel level with eBPF, assigns digital identities to agents, and unifies network and security signals for real-time threat detection across agentic workflows.