AI Agent Runtime Security Moves Into Production
AI Agent Runtime Security Becomes Urgent
AI agent runtime security is becoming a priority as organizations connect autonomous agents to critical enterprise systems. In this Techstrong TV interview, Nadav Cornberg, co-founder and CEO of Eve Security, joins Mike Vizard to explain why runtime controls are now central to governing agent behavior.
Cornberg says protocols such as MCP make it easy to connect agents to tools and systems. That ease of connection drives the shift. That creates powerful automation, but it also creates uncertainty. An agent with broad access can take a consequential action in a single command. That includes actions its deployment team never intended.
Policy Enforcement Needs a Runtime Layer
The discussion explores why traditional security approaches are not enough for agentic AI. Vulnerability detection can identify issues and give teams time to fix them. AI agents move much faster. Security controls need to evaluate behavior while the agent is acting.
Cornberg describes two key components for AI agent runtime security. The first is an enforcement point, such as an API gateway, MCP gateway or agent hook. The second is a smart decision point that understands what the agent is trying to do. It compares the action against policy and responds quickly enough to stay in the critical path.
Agent Behavior Analysis Changes the Model
Eve Security focuses on profiling normal agent behavior and identifying anomalies. Cornberg compares this to user behavior analytics, but for AI agents. The goal is to understand what an agent normally does, what actions are approved and when a request falls outside expected behavior.
That distinction matters because agentic systems are probabilistic. They may perform the same task in different ways. Runtime governance must therefore look at patterns, objectives and risk, not just static rules.
Critical Systems Need Guardrails
The interview also examines the risk of giving agents access to production environments, financial systems, databases and other sensitive assets without governance. Cornberg warns that treating this as a calculated risk can become a leap of faith.
For CISOs and technology leaders, the takeaway is direct. AI agents are becoming more like digital employees with job descriptions, permissions and the ability to act at machine speed. AI agent runtime security gives organizations a way to observe, govern and interrupt risky behavior before it turns into an operational incident.
