Practical Runtime Security and Defense for Agentic AI Systems

What happens after your AI agents are deployed?
Most organizations focus on building, securing, testing, and governing agentic AI systems. Far fewer are prepared for the challenges that emerge when autonomous agents begin making decisions, invoking tools, accessing sensitive resources, and collaborating with other agents in real production environments.
How do you detect dangerous behavior before it causes damage? How do you stop a compromised agent in real time? How do you contain failures, enforce policies at runtime, and maintain control as autonomous systems operate at machine speed?
Practical Runtime Security and Defense for Agentic AI Systems provides a hands-on blueprint for continuously protecting autonomous AI agents and multi-agent systems after deployment. Designed for security engineers, AI platform teams, DevSecOps professionals, architects, and enterprise technology leaders, this book focuses on the operational security controls required to defend agentic AI systems in production.
Rather than relying on theory, this book shows you how to implement practical runtime defense strategies, automated response mechanisms, behavioral monitoring pipelines, containment workflows, and self-healing security controls that work in modern enterprise environments.
Inside, you will learn how to:

• Build runtime security architectures for autonomous AI agents and multi-agent systems
• Implement policy enforcement, guardrails, and real-time action controls
• Establish behavioral monitoring and anomaly detection capabilities
• Deploy automated containment, isolation, and kill-switch mechanisms
• Create agent control towers and runtime observability pipelines
• Design self-healing security workflows and autonomous defense systems
• Investigate incidents using traceability, telemetry, and forensic analysis
• Integrate runtime security into AgentOps, DevSecOps, and CI/CD pipelines
• Strengthen operational resilience across cloud, hybrid, and multi-agent environments
• Scale continuous protection strategies for enterprise AI platforms