
Discover how Agentic AI's autonomous decision making reshapes cybersecurity risks, and learn threat modeling, governance, and practical mitigations for secure autonomous systems.
Explore agentic AI, the autonomous framework where AI agents perceive, reason, act, and learn to solve complex goals with minimal human oversight, using a large language model and external tools.
Identify how agentic AI differs from generative AI by examining autonomy-driven risks and the threat surface. Learn existing AI vulnerabilities and emerging cybersecurity challenges, with emphasis on applying strong controls.
Misalignment in autonomous agentic ai causes goals to diverge from human intent, triggering unintended and harmful actions. Reinforce guardrails, real-time oversight, and failsafes to prevent cascading failures in multi-agent systems.
Disempowerment risks emerge as over reliance on agentic AI erodes human problem solving and creativity. Balance deployment with augmentation, reskilling, and strong human oversight.
Explore how agentic AI amplifies cyber threats through autonomous, end-to-end attacks—phishing, DDoS, and ransomware—driving a 24/7 army of AI attackers and how defense teams can counter with AI-driven safeguards.
Explore threat modeling for agentic AI, focusing on the maestro framework across seven layers—from foundational models to the agent ecosystem—to identify multi-agent risks and guide defenses.
Apply the maestro framework for threat modeling agenda to agentic ai, detailing the foundation model, data operations, and generic frameworks with practical mitigations.
Apply the maestro framework to a practical seven-layer threat modeling case study for agentic AI in finance, with layer decomposition, cross-layer risk identification, mitigation, and continuous monitoring.
Explore the rise of OpenClaw and personal agentic assistants, their architecture, and security risks, including prompt injections, memory poisoning, credential leakage, and threat modeling using the Maestro framework.
Model OpenClaw threats with Maestro’s seven-layer framework, tracing prompts, memory, tools, and sub-agents; apply cross-layer mitigations like validation, isolation, and zero-trust.
Explore the agentic AI security scoping matrix, detailing four autonomy scopes from no to full agency, and the six security dimensions for implementing secure agentic architectures.
Agentic AI represents the next evolution of artificial intelligence—systems that can autonomously make decisions, plan actions, and interact with the world with minimal human intervention. As AI becomes increasingly autonomous, new risks and security challenges emerge that go beyond traditional cybersecurity concerns.
The "Agentic AI Risk and Cybersecurity Masterclass" is a comprehensive course designed to provide a deep understanding of agentic AI technologies, their unique risk landscape, and the best practices for securing these intelligent systems.
This course explores the principles, components, and security considerations of Agentic AI, equipping you with the knowledge to assess, mitigate, and defend against emerging AI threats.
What You Will Learn
Fundamental principles and architecture of Agentic AI systems
Understanding the risk landscape in autonomous AI and its implications
Security threats unique to Agentic AI, including AI autonomy risks, adversarial manipulation, and decision-based attacks
How prompt injections and model exploitation attacks evolve in an Agentic AI context
Strategies for designing secure Agentic AI systems with ethical safeguards and risk mitigation controls
Compliance and governance frameworks for Agentic AI cybersecurity
Course Outline
Introduction to Agentic AI
What is Agentic AI?
How does it differ from Generative AI
Why security in Agentic AI is critical
Risks in Agentic AI
Overview of the Agentic AI risk landscape
Threat modeling Agentic AI systems
Case Study of Threat Modeling Agentic AI systems
Security in Agentic AI
Creating a Security Framework For Agentic AI
Threat vectors and attack techniques against autonomous AI
Hijacking attacks, data poisoning, and malicious automation
Best practices for hardening Agentic AI models and deploying AI security frameworks
Who Should Take This Course
This course is ideal for individuals looking to understand and mitigate the cybersecurity risks associated with autonomous AI systems, including:
AI engineers & researchers
Cybersecurity professionals
Data Scientists & AI Ethics specialists
IT Managers & risk professionals
Business leaders exploring Agentic AI adoption
Pre-requisites
Basic understanding of AI and cybersecurity concepts is recommended, but no prior knowledge of Agentic AI is required.
Instructor
Taimur Ijlal is a multi-award-winning cybersecurity leader with over 20+ years of global experience in cyber risk management, AI security, and IT governance. He has been recognized with industry accolades such as CISO of the Year, CISO Top 30, and Most Outstanding Security Team.
Taimur’s cybersecurity and AI courses have thousands of students worldwide, and his work has been featured in ISACA Journal, CIO Magazine Middle East, and multiple AI security publications. His books on AI Security and Cloud Computing have ranked as #1 new releases on Amazon.
Join this course to stay ahead of the rapidly evolving landscape of Agentic AI Risk and Cybersecurity!