AI Risk and Compliance: Audit and Governance Foundations | Board Infinity
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Intermediate Course 3 of 3 · Managing AI Systems

AI Risk and Compliance: Audit and Governance Foundations

Red team AI systems. Understand regulatory risk. Build explainability and governance controls - from Giskard scans to the EU AI Act, NIST AI RMF, SHAP/LIME, guardrails and Presidio.

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2 weeks at 10 hrs/week 4 modules · 16 assignments Intermediate level Taught in English
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Loading video… Introduction to the course · 5:38

Learn by doing

Red teaming, XAI and guardrail labs, ending in a capstone

Certificate included

Shareable on LinkedIn the day you finish

Technical and policy

For engineers, risk teams and compliance officers

6 Lakh+learners on our Coursera courses
10+domains covered
270+courses
57+specializations
01 About the course

From attack, to regulation, to audit-ready evidence.

Four modules that test AI systems, map them to the rules, explain them and put safety controls in place.

4
Modules
Attack, regulate, explain, control
34
Video lessons
Short and focused
16
Assignments
Quizzes and hands-on labs
2 wks
At 10 hours a week
Self-paced, flexible deadlines

AI Risk and Compliance provides a practical, end-to-end approach to governing, securing and auditing AI systems in enterprise environments.

You begin with adversarial threats - jailbreaks, prompt injection, data leakage and manipulation - and practise structured red teaming. Then come the regulatory frameworks: EU AI Act classifications and obligations, the NIST AI RMF (Govern, Map, Measure, Manage), and copyright and data-usage risks.

The later modules cover explainability with SHAP, LIME and attention visualization, and safety engineering: guardrails with Guardrails AI and NVIDIA NeMo, Presidio for PII, and governance controls against Shadow AI.

Inside the course
01Adversarial Robustness & Red Teaming≈ 4h 02EU AI Act, NIST RMF & Copyright≈ 4h 03Explainability (XAI) & Transparency≈ 4h 04Guardrails, PII & Shadow AI≈ 4h
02 Who it's for

For the people responsible for AI going right.

No formal governance experience is required - basic familiarity with AI/ML concepts and Python helps with the labs.

Governance

AI governance officers

Building governance, monitoring and audit programmes for enterprise AI.

Risk & compliance

Risk analysts & compliance officers

Applying the EU AI Act and NIST AI RMF to real systems and documentation.

Engineering

AI & ML engineers

Red teaming, guarding and scrubbing the systems you build before they ship.

Policy

Policy makers

Understanding how AI risks are actually tested, explained and controlled.

03 What you'll learn

Four capabilities, in the order they build.

These are the four learning outcomes Coursera lists for the course - one for each module.

Module 1

Red team AI systems

Execute adversarial scans with Giskard to identify and prioritize AI vulnerabilities.

Module 2

Apply AI regulation

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

Module 3

Explain model decisions

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

Module 4

Implement safety controls

Guardrails, PII scrubbing with Presidio, and governance controls that mitigate Shadow AI.

1:1 coaching · optional add-on

Build with 1:1 coaching from industry experts.

A dedicated coach who works in AI risk and governance - someone who can review your red-team findings and tell you whether your documentation would survive an audit.

Book a demo session
  • A coach matched to your goal in 24 hours
  • A written roadmap you keep
  • Weekly sessions for as long as you want them

60-minute demo session · 100% refundable · no obligation to continue

Review your findings

Red-team results ranked and remediated the way a security team would.

Audit-proof your documents

EU AI Act and NIST mappings checked against what auditors expect.

Apply it at work

A governance plan for the AI tools your organization already uses.

Aim it at a role

Governance, risk, red teaming or audit - which path fits you.

2,000+Industry coaches
16Domains covered
4.8/5Average coach rating
48,000+Sessions delivered
04 Syllabus

Four modules, from attack to audit.

Coursera lists this course at 2 weeks of study at 10 hours a week, fully self-paced. Every module lists its learning objectives, its content, and what you'll walk out with.

34 videos 12 readings 16 assignments 2 weeks at 10 hrs/week
Learning objectives - by the end of this module you can:
  • Identify LLM threats: jailbreaks, prompt injection, leakage and manipulation
  • Distinguish benign failures from genuinely adversarial behavior
  • Stress-test LLM defenses across architectures
  • Run automated red teaming with Giskard and prioritize remediation
What's inside
8 videos65 min
3 readings90 min
4 assignments + 1 plugin105 min
Topics covered
Career scope in AI governance LLM threat categories Benign vs adversarial failures Prompt injection anatomy Jailbreak techniques Giskard vulnerability scans
You'll leave with: a red-team report on an AI system - vulnerabilities found, ranked and documented.
Learning objectives - by the end of this module you can:
  • Classify AI systems under the EU AI Act risk tiers
  • Explain high-risk obligations, conformity assessments and post-market monitoring
  • Map the AI lifecycle to NIST AI RMF: Govern, Map, Measure, Manage
  • Assess training-data licensing, output ownership and enterprise liability
What's inside
7 videos58 min
3 readings90 min
4 assignments105 min
Topics covered
EU AI Act risk categories High-risk requirements Conformity assessments NIST AI RMF across the lifecycle Training-data licensing Enterprise liability
You'll leave with: concise compliance documentation that maps a system to the EU AI Act and NIST AI RMF.
Learning objectives - by the end of this module you can:
  • Explain why explainability is difficult for LLMs
  • Compare SHAP, LIME and attention maps, and choose the right one
  • Present faithful, human-readable explanations to executives and regulators
  • Design transparency workflows and document system and decision flows
What's inside
10 videos66 min
3 readings90 min
4 assignments105 min
Topics covered
Why XAI is hard for LLMs SHAP, LIME & attention maps Human-readable explanations Faithfulness & reliability Transparency requirements Decision flow documentation
You'll build: SHAP or LIME explanations for a text classifier, and an audit-ready transparency report.
Learning objectives - by the end of this module you can:
  • Build and test rule-based and semantic guardrails with Guardrails AI and NeMo
  • Detect, mask and anonymize PII with Presidio, including in RAG pipelines
  • Recognize Shadow AI and apply monitoring, policy and enforcement
  • Design organization-wide governance controls
What's inside
9 videos60 min
3 readings90 min
4 assignments105 min
Topics covered
Guardrails AI & NeMo Rule-based & semantic guardrails Presidio architecture PII masking in RAG pipelines Shadow AI in enterprises Governance controls
You'll build: the capstone lab - a chatbot red teamed with Giskard, guarded, scrubbed with Presidio and fully documented.
05 Tools & skills

The tools you test, explain and protect with.

The four toolkits used in the course labs, and the skills you leave with.

Red teaming

Giskard

Automated vulnerability scans for AI and LLM systems - Module 1 and the capstone.

Explainability

SHAP & LIME

Explaining model decisions, alongside attention visualization - Module 3.

PII protection

Microsoft Presidio

Detecting, masking and anonymizing sensitive data, including in RAG - Module 4.

Safety

Guardrails AI & NeMo

Rule-based and semantic guardrails for policy enforcement - Module 4.

Skills you leave with

Twelve skills from the course, grouped the way AI risk work actually splits - finding the risks, meeting the rules, and keeping systems safe.

Risk & security

Finding what can go wrong before attackers do.

  • AI security
  • Vulnerability assessments
  • Risk management
  • Risk mitigation

Regulation

Meeting the EU AI Act, NIST and copyright rules.

  • Regulatory compliance
  • Compliance auditing
  • Legal risk
  • Regulatory requirements

Governance & safety

Controls that keep AI safe across an organization.

  • Governance
  • Safety assurance
  • Personally identifiable information
  • Compliance reporting
Certificate

Proof you can link to.

Finish the graded work and Board Infinity issues a shareable certificate through Coursera - add it to your LinkedIn profile, resume or CV.

Issued on completion of the assignments and labs in all four modules. As the final course, it completes the Managing AI Systems specialization certificate as well.

On your profile in one click

Filed under Licenses & Certifications, where recruiters actually look.

Share it anywhere

On social media and in your next performance review.

Earned, not attended

Awarded on 16 assignments and hands-on labs - it certifies what you can do.

Two names on it

Board Infinity as the provider, Coursera as the platform.

06 Career impact

AI regulation is here. Every company needs people who can prove compliance.

These are the roles that rely on exactly this skill set.

Where these skills are in demand

AI Governance Officer

Owning the policies, controls and evidence behind an organization's AI.

AI Risk Analyst

Assessing AI risks against the NIST AI RMF and prioritizing mitigation.

Compliance Officer

Mapping AI systems to EU AI Act obligations and keeping documentation current.

AI Red Teamer

Attacking AI systems with jailbreaks and prompt injection to find weaknesses first.

AI Auditor

Reviewing transparency reports, explanations and evidence for audits.

AI Security Engineer

Building guardrails and defenses into LLM applications.

Privacy Engineer

Detecting and scrubbing PII across AI and RAG pipelines.

Responsible AI Lead

Balancing innovation with safety, transparency and accountability.

The honest comparison

What you're actually paying for

Every topic in this course exists free somewhere on the internet. Nobody is pretending otherwise. What a course adds is the order you learn it in, the practice that makes it stick, and something to show at the end.

What you get
This course
DIY with free content
Technical and regulatoryBoth halves of AI risk
Red teaming, regulation, XAI and guardrails
Legal summaries or security blogs, never both
Hands-on labsReal tools
Giskard, SHAP/LIME, Presidio, Guardrails
Theory with nothing to try
Practice with feedbackLearning that sticks
16 assignments and quizzes
Nobody checks your work
Audit-ready outputEvidence, not notes
Red-team, compliance and transparency documents
Bookmarks and highlights
Proof for recruitersShareable, on LinkedIn
Shareable Coursera certificate
Nothing to show
07 Where to go next

More from the Managing AI Systems specialization.

This course stands on its own - but it's also the final step of a three-course path. Enrolling automatically enrolls you in the specialization.

Course 1

AI Systems Design: RAG Pipelines and LLM Architecture

Problem framing, the modern AI stack, data strategy and a RAG prototype in Streamlit.

≈ 2 weeks
View course
Course 2

MLOps and LLMOps: Deploying and Scaling AI in Production

CI/CD for ML and LLM systems with GitHub Actions and MLflow, and optimized inference.

≈ 2 weeks
View course
You're here Course 3

AI Risk and Compliance: Audit and Governance Foundations

Red teaming, regulation, explainability and safety controls - the capstone of the path.

≈ 2 weeks · 4 modules
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Know exactly what you're signing up for.

4 modules · 34 lessons · 16 assignments · free PDF

08 FAQ

Common questions

Everything learners ask before enrolling, answered plainly.

About the course

No formal governance experience is required. Basic familiarity with AI/ML concepts and Python will help you get the most from the hands-on labs and tools used in the course.

While the course includes technical labs, the regulatory compliance and governance modules are highly relevant for policy makers, risk analysts and compliance officers.

Giskard for automated red teaming, SHAP and LIME for explainability, Microsoft Presidio for PII detection, and Guardrails AI/NeMo Guardrails for safety enforcement.

Systematically testing AI systems for vulnerabilities like jailbreaks, prompt injection and bias, using structured attack methodologies and tools like Giskard.

Coursera lists it at about 2 weeks at 10 hours a week, across 4 modules. It's fully self-paced, so you can spread it out around work.

Career & certificate

Roles like AI Governance Officer, AI Risk Analyst, Compliance Officer, AI Red Teamer and AI Auditor - anywhere AI systems need to be tested, documented and governed.

When you enroll in the course, you get access to all of the courses in the specialization, and you earn a certificate when you complete the work. Your electronic certificate is added to your Accomplishments page, where you can print it or add it to your LinkedIn profile.

To access course materials and assignments and earn a certificate, you purchase the Certificate experience when you enroll. Eligible learners may be able to start with a free trial, and some may see a Full Course, No Certificate option.

Yes. In select learning programs you can apply for financial aid or a scholarship if you can't afford the enrollment fee. If it's available, you'll find a link to apply on the Coursera course page.

Want someone reviewing your audit work with you?

Board Infinity 1:1 coaching pairs you with a practitioner while you take the course.

See 1:1 coaching

Make your AI safe to ship in two weeks.

Four modules. Sixteen assignments. Red-team, compliance and transparency evidence you produced yourself - and a certificate to show for it.

Enroll on Coursera

AI Risk and Compliance: Audit and Governance Foundations · by Board Infinity · offered on Coursera

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