Taming the AI Double Threat with Isovalent Runtime Security

AI is creating a dual security challenge: AI workloads (especially agentic ones) introduce new attack surfaces, while adversaries use AI to compress attack timelines. Agentic workloads are non-deterministic at the application layer but exhibit stable behavioral profiles at the kernel level, making eBPF-based runtime security effective. Isovalent Runtime Security (built on Tetragon and eBPF) addresses both threats through kernel-level enforcement, process/network/file visibility, sandbox policies with default-deny posture, and L7 network introspection. The post argues that existing cloud-native runtime security primitives, extended with the right mental models, are sufficient to secure AI workloads and respond to AI-accelerated attacks.