Agentic AI Security- Threats, Architectures & Mitigations

Agentic AI Security: Threats, Architectures & Mitigations is a comprehensive course designed to prepare developers, security engineers, AI architects, and risk officers to defend the next generation of autonomous systems. The course begins by grounding learners in the fundamentals of agentic AI, explaining how modern AI agents—unlike traditional models—perceive, reason, plan, and act with increasing autonomy. It explores the pivotal role of OWASP’s Agentic Security Initiative and introduces the architectural foundations of single-agent and multi-agent systems, showcasing the core capabilities of agents, including memory, tool use, and goal decomposition. Learners are introduced to orchestration layers, agent frameworks like LangChain and AutoGen, and real-world agentic patterns and use cases. As the course progresses, it delves into threat modeling with STRIDE, PASTA, and MAESTRO frameworks, before detailing OWASP’s reference agentic threat model and taxonomy navigator.

The midsection focuses on deep-dives into specialized threats—reasoning drift, memory poisoning, tool misuse, identity spoofing, HITL exploitation, and multi-agent coordination failures. Six mitigation playbooks provide practical countermeasures: reasoning validation, memory control, tool execution hardening, identity strengthening, HITL optimization, and inter-agent trust assurance. Learners then transition into architectural solutions including modular agent design, execution guards, rollback systems, and defense-in-depth strategies. The deployment section emphasizes containerization, policy-driven API access, and lessons from real-world agent incidents. To ensure proactive defense, the course includes guidance on designing red teams, secure simulation labs, and building vulnerable agents for training purposes using LangChain. Hands-on labs like simulating memory poisoning and consensus manipulation are also included.

The course concludes by integrating agentic threats into existing security frameworks—mapping OWASP threats to MITRE ATLAS and NIST AI RMF—thus aligning advanced agent risks with enterprise governance and compliance expectations. Learners emerge prepared to design, test, and deploy secure, interpretable, and auditable AI agents.