In this practical course, you'll learn how to treat AI prompts and outputs like governed assets. Mitigate risks like prompt injection, protect sensitive data, and apply enterprise-grade controls to your workflow. Master the art of validating outputs and measuring cost/accuracy to scale AI safely. Secure your output. Protect your data.

This course provides an overview of every stage of the AI Security Lifecycle, from planning and data preparation to deployment, monitoring, and governance—equipping them with the frameworks, tools, and best practices necessary to safeguard AI models and data in enterprise environments.

This course provides an overview of best practices for securely handling AI-generated code, with a focus on "vibe coding" — informal, AI-assisted development often performed by non-technical personnel. Through practical examples and an optional lab, learners will gain skills to integrate secure vibe coding into engineering workflows.

Learn to align AI research with secure principles to ensure due care and faster approvals. You will prioritize adversarial risks using MITRE and OWASP LLM Top 10, design data protection controls for privacy and integrity, build an evaluation process, select safe integration patterns, and communicate risk to review boards with audit-ready artifacts.

Custom AI tools introduce non-determinism, external dependencies, and the risk of excessive agency. Traditional IT controls are necessary but not sufficient. You will learn to set trust boundaries, constrain tool scopes, and enforce latency budgets that match business SLAs.

AI Guardrails are security controls that intercept and validate inputs to and outputs from Large Language Models (LLMs), addressing unique attack vectors that traditional security controls cannot prevent. Learn to build a complete guardrails stack, test it against prompt injection attacks and PII disclosure, and monitor and tune these controls.

AI risk is not a simple extension of traditional IT risk. Models change behavior, which creates new failure modes like prompt injection, insecure output handling, data leakage, and drift. You need controls that watch the interaction pattern, not only the code at rest. In this course, you will tie security frameworks to everyday SOC tasks.

Master the AI Tool Vetting Lifecycle. Learn to define enterprise workflows, map threats (MITRE ATLAS/OWASP LLM), and evaluate vendor evidence consistently. Develop skills to measure model quality/cost and negotiate contracts for strong data protection. Finally, plan deployment, monitoring, and lifecycle controls for sustained safety and compliance.