AI Security & Governance Masterclass: Build, Attack & Defend

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Build the practical skills required to design, attack, secure, monitor, and govern modern AI applications in this comprehensive AI Security and Governance Masterclass.

As organizations rapidly adopt generative AI, large language models, RAG applications, and autonomous AI agents, security and governance have become essential. AI systems introduce risks that traditional application security alone cannot address, including prompt injection, jailbreak attacks, document poisoning, memory manipulation, unauthorized tool execution, sensitive data exposure, hallucinations, and uncontrolled agent actions.

This course takes a hands-on, project-based approach. You will not only study AI security concepts—you will build vulnerable AI systems, attack them, understand their weaknesses, and implement practical defenses.

You will begin by setting up Python, Visual Studio Code, Git, and Ollama, then build your first AI chat assistant. You will explore the AI threat landscape, understand the OWASP Top 10 for LLM Applications, and learn how security differs across traditional software and AI-powered systems.

You will perform real-world direct prompt injection and AI jailbreak attacks, then create prompt validation, risk scoring, filtering, and guardrail mechanisms. You will build a complete Retrieval-Augmented Generation application, attack it using malicious documents, poison retrieved context, and secure it through trusted-source validation, context isolation, content sanitization, and enforcement controls.

The course also covers AI tool-calling security, including parameter injection, excessive permissions, unauthorized actions, input validation, least-privilege access, and human approval for sensitive operations. You will build persistent AI memory, demonstrate memory-poisoning attacks, and implement approval and validation controls before information is stored.

As you progress, you will develop an autonomous AI agent that can plan, reason, use tools, and complete workflows. You will then secure its decision-making process using human-in-the-loop approvals, restricted permissions, action validation, and traceable execution.

You will combine these defenses into an integrated AI Security Gateway covering prompts, RAG, tools, memory, and agents. You will also learn production practices involving Docker, deployment, security monitoring, logging, audit trails, and incident response.

Beyond technical security, this course provides extensive coverage of enterprise AI governance. You will build AI inventories, usage dashboards, cost analytics, risk-scoring engines, model-evaluation dashboards, drift-monitoring systems, prompt-governance labs, agent-observability tools, and RAG governance dashboards.

You will learn how to map controls to NIST AI RMF, the EU AI Act, and ISO/IEC 42001. Topics include policy management, control evidence, model cards, AI impact assessments, approval workflows, exception management, guardrail effectiveness, governance KPIs, KRIs, incident remediation, and executive reporting.

The final capstone brings everything together as you build an Enterprise AI Governance Command Center connecting AI telemetry, risk, controls, compliance evidence, incidents, remediation, and leadership dashboards.

Whether you are an AI engineer, cybersecurity professional, risk manager, splunk enterprise architect splk 2002 practice tests 2026, developer, auditor, compliance leader, or technology executive, this course will help you build AI systems that are not only powerful—but also secure, responsible, compliant, observable, and enterprise-ready.