AI Security & Governance Masterclass: Build, Attack & Defend
Published 7/2026
Created by Arjun Vaid, School of AI
MP4 | Video: h264, 3840x2160 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All Levels | Genre: eLearning | Language: English | Duration: 159 Lectures ( 26h 33m ) | Size: 25.9 GB
Secure AI apps, RAG, tools, memory, and agents while mastering risk, compliance, guardrails, and governance.
What you'll learn

Identify major AI security threats across chatbots, RAG systems, tools, memory, and autonomous agents.

Build and secure AI applications using Python, Ollama, RAG, tool calling, memory, and agents.

Perform and defend against prompt injection, jailbreaks, document poisoning, and memory poisoning.

Apply the OWASP Top 10 for LLM Applications to real-world AI systems.

Implement prompt validation, risk scoring, guardrails, content filtering, and policy enforcement.

Secure AI tool use with input validation, least-privilege permissions, and human approval workflows.

Build an integrated AI Security Gateway for prompts, RAG, tools, memory, and agent actions.

Create AI governance dashboards for inventory, usage, cost, risk, evaluation, drift, and compliance.

Map AI controls to NIST AI RMF, ISO/IEC 42001, and the EU AI Act.

Build an Enterprise AI Governance Command Center with audit trails, incidents, controls, evidence, and executive metrics.
Requirements

No prior AI security or AI governance experience is required.

Basic computer skills and a willingness to learn through hands-on projects.

Some familiarity with Python is helpful, but beginners can follow the guided demonstrations.

A Windows, macOS, or Linux computer capable of running Python development tools.

Permission to install Python, Visual Studio Code, Git, Ollama, and Docker.

Basic understanding of AI, chatbots, or generative AI is useful but not mandatory.

An interest in cybersecurity, AI development, governance, risk, or compliance.

Internet access may be required for downloading software, libraries, and course resources.
Description
This course contains the use of artificial intelligence.
Build the practical skills required todesign, attack, secure, monitor, and govern modern AI applications in this comprehensiveAI Security and Governance Masterclass.
As organizations rapidly adoptgenerative AI,large language models,RAG applications, andautonomous AI agents, security and governance have become essential. AI systems introduce risks that traditional application security alone cannot address, includingprompt 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 upPython,Visual Studio Code,Git, andOllama, then build your first AI chat assistant. You will explore theAI threat landscape, understand theOWASP Top 10 for LLM Applications, and learn how security differs across traditional software and AI-powered systems.
You will perform real-worlddirect prompt injection andAI jailbreak attacks, then create prompt validation, risk scoring, filtering, and guardrail mechanisms. You will build a completeRetrieval-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 coversAI tool-calling security, including parameter injection, excessive permissions, unauthorized actions, input validation, least-privilege access, and human approval for sensitive operations. You will build persistentAI memory, demonstrate memory-poisoning attacks, and implement approval and validation controls before information is stored.
As you progress, you will develop anautonomous AI agent that can plan, reason, use tools, and complete workflows. You will then secure its decision-making process usinghuman-in-the-loop approvals, restricted permissions, action validation, and traceable execution.
You will combine these defenses into an integratedAI Security Gateway covering prompts, RAG, tools, memory, and agents. You will also learn production practices involvingDocker, deployment, security monitoring, logging, audit trails, and incident response.
Beyond technical security, this course provides extensive coverage ofenterprise 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 toNIST AI RMF, theEU AI Act, andISO/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 anEnterprise AI Governance Command Center connecting AI telemetry, risk, controls, compliance evidence, incidents, remediation, and leadership dashboards.
Whether you are anAI engineer,cybersecurity professional,risk manager,enterprise architect,developer,auditor,compliance leader, ortechnology executive, this course will help you build AI systems that are not only powerful-but alsosecure, responsible, compliant, observable, and enterprise-ready.
Who this course is for

AI engineers and machine learning engineers building secure AI applications.

Python developers and software engineers working with LLMs, RAG, tools, and agents.

Cybersecurity professionals expanding into generative AI and agent security.

AI governance, risk, and compliance professionals responsible for oversight and controls.

Enterprise and solution architects designing secure, scalable AI platforms.

Data scientists and MLOps professionals responsible for model monitoring and evaluation.

Auditors, privacy specialists, and responsible AI teams assessing AI systems.

Technology leaders and executives managing enterprise AI risk and adoption.

Students and career changers seeking practical AI security and governance skills.

Anyone interested in building AI systems that are secure, compliant, observable, and enterprise-ready.
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