Board Infinity

AI Risk and Compliance: Audit and Governance Foundations

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Board Infinity

AI Risk and Compliance: Audit and Governance Foundations

Board Infinity

Instructor: Board Infinity

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Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

2 weeks to complete
at 10 hours a week
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

2 weeks to complete
at 10 hours a week
Flexible schedule
Learn at your own pace

What you'll learn

  • Execute adversarial red teaming scans using Giskard to identify and prioritize AI vulnerabilities

  • Classify AI systems under the EU AI Act and apply NIST AI RMF across the AI lifecycle

  • Generate SHAP/LIME explanations and create audit-ready transparency documentation

  • Implement guardrails, PII scrubbing with Presidio, and governance controls to mitigate Shadow AI

Details to know

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Recently updated!

April 2026

Assessments

16 assignments

Taught in English

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This course is part of the Managing AI Systems: Development, Deployment, and Governance Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 4 modules in this course

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.

What's included

9 videos3 readings4 assignments

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.

What's included

9 videos3 readings4 assignments

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.

What's included

9 videos3 readings4 assignments

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.

What's included

9 videos3 readings4 assignments

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Board Infinity
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