In this module, learners dive into the adversarial threat landscape for modern AI systems and practice structured red teaming workflows. You will explore real-world AI threat models, including jailbreaks, prompt injection, leakage, and manipulation attacks, and distinguish benign failures from genuinely adversarial behavior. Through videos, readings, AI dialogues, and a hands-on lab using Giskard, you will learn how to execute automated red teaming, interpret vulnerability reports, and prioritize remediation actions. By the end of the module, you will be prepared to evaluate system readiness under adversarial conditions and document findings in an audit- and security-friendly format.

This module focuses on the regulatory and risk-management frameworks that govern enterprise AI systems, with emphasis on the EU AI Act, the NIST AI Risk Management Framework (RMF), and key copyright and data usage issues. Learners will analyze EU AI Act risk tiers, high-risk obligations, conformity assessments, and post-market monitoring requirements. You will then map AI lifecycle activities to the NIST AI RMF functions and apply NIST-aligned risk assessment techniques. The module also examines training-data licensing, ownership of LLM outputs, enterprise liability, and unauthorized training risks. Through a lab and applied exercises, you will classify AI systems under the EU AI Act, map risks to NIST functions, and produce concise compliance documentation.

In this module, learners explore explainable AI (XAI) techniques and transparency practices for large language models and other complex systems. You will investigate why explainability is challenging for LLMs and compare leading XAI methods such as SHAP, LIME, and attention maps, including guidance on when to use each. The module then turns to stakeholder-facing communication, showing how to generate human-readable explanations and present them effectively to executives and regulators while maintaining faithfulness and reliability. Finally, you will design transparency workflows that satisfy governance and compliance requirements, including documentation of system and decision flows. A hands-on lab guides you through applying SHAP or LIME to a text classifier and drafting a transparency report suitable for audits.

This capstone module addresses practical governance controls for safe AI usage, focusing on guardrails frameworks, PII protection, and Shadow AI mitigation. Learners begin by implementing guardrails for safety and policy enforcement using Guardrails AI and NVIDIA NeMo, including rule-based and semantic guardrails and testing them against attacks. The module then introduces Microsoft Presidio for PII detection and anonymization, demonstrating how to detect, mask, and scrub sensitive data and integrate Presidio into RAG pipelines. Finally, you will examine Shadow AI risks in enterprises, monitoring and enforcement techniques, and organization-wide governance controls. A major lab ties these elements together by red teaming a chatbot with Giskard, implementing Guardrails and Presidio, and producing comprehensive evidence and documentation that serve as the practical course capstone.