This module explores the unique characteristics and security challenges of agentic AI systems, including their attack surfaces, risks from autonomy and delegation, and the limitations of traditional cybersecurity approaches. Learners will gain insights into secure AI architecture, risk management, and human-agent interactions through real-world case studies and hands-on labs.

This module explores the critical concepts of agent goal integrity in AI systems, focusing on the risks of goal drift, hijacking, and injection. It provides practical insights into securing agent intentions through defensive engineering, intent provenance, and monitoring techniques. Learners will gain skills to audit, detect, and prevent malicious or unintended behavior in autonomous AI agents.

This module covers critical aspects of securing agentic AI systems, including tool interfaces, sandboxing, egress control, and safe orchestration. Learners will explore real-world security risks and gain practical skills in designing and monitoring secure toolchains. The module emphasizes both theoretical concepts and hands-on strategies for mitigating tool misuse and unauthorized delegation.

This module explores the critical aspects of identity and access control for AI agents, including secure credential management, privilege delegation, and methods to prevent identity-based attacks. Learners will gain an understanding of zero-trust principles and how to implement secure identity models in dynamic environments. Practical labs and knowledge checks reinforce hands-on application of these concepts.

This module explores key security strategies for protecting agentic AI systems throughout their supply chain. Learners will gain an understanding of risks like dataset poisoning, typosquatting, and registry compromise, as well as practical methods for securing components, building AI Bills of Materials, and implementing zero-trust architectures.

This module covers critical security concepts in agentic systems, including code safety, remote code execution (RCE) defense, and execution control. Learners will explore techniques for detecting and mitigating security risks in AI-generated code, such as code hallucinations, prompt injection, and unsafe dependencies. The module also includes practical labs on securing code pipelines and execution environments.

This module explores critical aspects of memory and context security in agentic systems, including memory types, poisoning risks, embedding attacks, and secure design practices. Learners will gain an understanding of how to protect agent memory and ensure data integrity in AI systems.

This module covers the fundamentals of secure communication in multi-agent systems, focusing on common risks, attack vectors, and defensive strategies. Learners will gain a deep understanding of how to design and implement secure coordination mechanisms. The content includes practical examples of cryptographic techniques and secure protocol design.

This module explores the causes and consequences of cascading failures in agentic AI systems, focusing on systemic risks, error propagation, and strategies to build resilient architectures. Learners will examine real-world scenarios and mitigation techniques to enhance system reliability and fault tolerance.

This module explores how human cognitive biases and psychological factors influence trust in AI systems, and teaches strategies to defend against manipulation through ethical design, secure interfaces, and human-centered interactions.

This module explores the challenges of managing rogue agents and misaligned AI systems, focusing on detection strategies, security controls, and ethical safeguards. Learners will gain an understanding of how agents can behave unpredictably and the tools to monitor and mitigate such risks effectively.

This module covers the design, integration, and security hardening of agentic AI systems. Learners will gain hands-on skills in threat modeling, red-teaming, and secure architecture implementation. The content emphasizes practical approaches to building resilient and production-ready agent-based systems.

This module offers a reflective summary of the course content, emphasizing key takeaways and encouraging learners to continue their journey in agentic AI security. It provides a final perspective on the skills and knowledge gained throughout the course.