Agentic AI tooling: Why runtime security is the missing layer

The text discusses the challenges and strategies for securing AI agent infrastructures, emphasizing the need for enhanced runtime security due to the nondeterministic nature of AI agents' behavior. It highlights the vulnerabilities in agent infrastructure, such as MCP tool poisoning, credential theft, and prompt injection, which can be exploited due to the lack of visibility in machine-to-machine communication and the rapidly evolving tooling ecosystem. The article stresses that traditional security measures based on predictable behavior are insufficient for AI agents, advocating for a layered security approach that includes syscall-level detection, capability scoping, and tool-call-level auditing. It also introduces Sysdig's AI Workload Security as a solution that extends open-source Falco's capabilities by providing continuously updated detection rules and integration with existing security workflows to address these unique challenges.