Agentic AI Security

The advent of autonomous AI agents powered by large language models (LLMs) marks a revolutionary shift in artificial intelligence, enabling advanced reasoning, decision-making, and dynamic interaction across industries like finance, healthcare, logistics, and beyond. Leveraging frameworks such as LangGraph and LangChain, these agentic AI systems deliver transformative capabilities but introduce critical security challenges-including prompt injection, memory corruption, intent misalignment, and adversarial attacks-that traditional software security cannot address.
Agentic AI Security: Architecting Resilient Autonomous LLM Systems for Enterprise Trust is the definitive guide for AI engineers, security architects, DevSecOps professionals, and enterprise leaders seeking to design, secure, and deploy robust autonomous LLM systems. This book provides a comprehensive agentic AI security framework, encompassing advanced threat modeling, secure prompt engineering, memory safeguards, anomaly detection, and compliance with global standards such as NIST AI RMF, OWASP GenAI Top 10, and the EU AI Act. Through structured methodologies and practical strategies, readers will master secure AI architecture, adversarial resilience, and scalable agentic workflows for production-grade enterprise environments.
Key takeaways include:

• Architecting secure agentic AI workflows with schema-constrained prompts and guarded tool orchestration
• Implementing memory integrity checks and anomaly detection for robust data handling
• Conducting red teaming and adversarial testing to fortify agents against sophisticated AI security threats
• Scaling autonomous AI systems for high-throughput enterprise applications with performance optimization
• Ensuring enterprise AI compliance with auditable, governance-aligned deployments