The Agentic AI Security Masterclass

How Autonomous AI Systems Fail — and How to Secure Them

What you’ll learn
✓ How agentic AI systems differ fundamentally from traditional software and why those differences create new security risks
✓ How autonomous agents plan, reason, delegate, and act — and how those behaviors fail in real-world systems
✓ Why traditional cybersecurity controls are necessary but insufficient for securing agentic AI
✓ How agent goals drift, get hijacked, or become misaligned without any explicit attack
✓ How agent goals drift, get hijacked, or become misaligned without any explicit attack
✓ How memory, context, and retrieval systems become long-term security liabilities
✓ How multiple agents interact, collude, and amplify each other’s mistakes
✓ How human trust, bias, and automation habits are exploited by agentic systems
✓ How to design secure agent architectures with clear boundaries, roles, and enforcement points
✓ How to apply identity, access control, sandboxing, and least privilege to non-human agents
✓ How to detect behavioral drift, reward hacking, and emergent rogue behavior early
✓ How to design and enforce autonomy boundaries, constraint engines, and kill switches
✓ How to build observability into agent decisions, plans, and actions
✓ How to threat-model, red-team, and harden agentic systems for production
✓ How to govern, monitor, and safely evolve autonomous systems over time
✓ How to think critically and responsibly about deploying agentic AI in real organizations

Requirements
● No prior experience with agentic AI is required
● No advanced background in cybersecurity is required
● Basic familiarity with how software systems work is helpful, but not mandatory
● Curiosity about how AI systems behave in the real world
● Willingness to think critically and question assumptions about automation and intelligence
● Comfort with learning through examples, scenarios, and hands-on exploration
● For labs: basic ability to follow technical instructions and experiment in a safe environment

Description
This masterclass examines how security must evolve when AI systems are no longer passive tools, but autonomous actors that plan, decide, and execute actions in real-world environments.

Agentic AI systems introduce a fundamentally different risk profile. Failures often emerge without exploits, without malicious intent, and without clear signals. Systems can behave correctly at a technical level while becoming unsafe, untrustworthy, or misaligned over time. This course is designed to address that gap.

Modern agentic systems plan their own actions, select tools, maintain memory, coordinate with other agents, and act with varying degrees of autonomy. These capabilities introduce risks that do not fit neatly into traditional cybersecurity models. Failures often emerge gradually, look like success at first, and involve no obvious attacker at all. This course is built to address that reality.

Across twelve deeply structured modules, learners are guided through the full lifecycle of agentic systems from a security perspective. The course begins by establishing a clear understanding of what makes agents fundamentally different from earlier AI and software systems, then progressively examines how goals drift, how tools are misused, how memory and context become liabilities, and how autonomy quietly expands beyond what was originally intended.

Rather than focusing on isolated vulnerabilities, the course treats agentic AI as a socio-technical system. It examines how agents interact with infrastructure, data, humans, and each other, and how risk emerges at those boundaries. Learners explore real-world inspired scenarios involving goal hijacking, reward hacking, cross-agent failure loops, credential misuse, memory poisoning, manipulation of human trust, and emergent rogue behavior.

Security is approached as an architectural and behavioral discipline, not a checklist. The course emphasizes designing systems that remain safe even when agents reason incorrectly, receive ambiguous input, or operate under uncertainty. Topics include secure agent architecture, identity and access controls for non-human actors, sandboxed execution, supply chain trust, constraint enforcement, behavioral monitoring, kill switches, observability, governance, and long-term resilience.

Hands-on labs are integrated throughout the course to reinforce learning through experience. Learners are exposed to realistic failure modes and attack patterns in controlled environments, allowing them to see firsthand how easily agentic systems can be influenced, misaligned, or pushed beyond safe boundaries.

By the end of the masterclass, learners gain more than technical knowledge. They develop a durable way of thinking about autonomy, risk, and responsibility in AI systems. They learn how to question agent behavior, design for failure, detect early warning signs, and govern intelligent systems in production with clarity and confidence.

This masterclass equips learners with the architectural thinking, behavioral awareness, and governance mindset needed to secure autonomous systems before trust is lost and damage becomes irreversible.

Who this course is for
■ Software engineers building systems that use agents, automation, or AI-driven workflows
■ Security professionals adapting their skills to autonomous and AI-powered systems
■ AI and ML engineers working with agent frameworks, tools, and orchestration layers
■ Platform and infrastructure teams supporting AI-enabled services
■ Architects designing systems where AI can take real-world actions
■ Product leaders and technical decision-makers accountable for AI risk and safety
■ Researchers and practitioners interested in AI governance, safety, and resilience
■ This course is also appropriate for learners who are new to AI or cybersecurity, as long as they are curious and willing to think critically about how intelligent systems behave.

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