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Courses / AI Security and Governance
For GRC, compliance and risk

AI Security and Governance

Understand AI security risks and the controls that address them, then operationalize AI governance across your company. Six practical modules, hands-on labs, and a certification earned on a real-world scenario.

6 modules · 6 weeks ~6 hours a week, mostly labs Hands-on labs in your browser Scenario-based certification CPE eligible

Who it's for

  • GRC, compliance and IT risk professionals who now own AI governance
  • Security managers and architects approving AI tools and agents
  • Auditors and assessors who need to evaluate AI systems with confidence

What you need

  • A working knowledge of security or GRC basics
  • No coding or machine learning background
  • A browser: labs run online with synthetic data, nothing to install
  • About 6 hours a week, most of it in hands-on labs
Syllabus

Six modules. Each ends with something you can use at work.

One module a week: short lessons, a hands-on lab, and a deliverable you adapt to your own organization.

Module 01 Week 1

How modern AI systems work

Understand the systems you are securing, from LLMs to autonomous agents, well enough to ask the right questions.

  • How large language models work: prompts, context and why outputs vary
  • Retrieval (RAG): connecting models to company data
  • Tools and autonomous agents: when AI starts taking actions
  • Where AI shows up in the enterprise: copilots, AI inside SaaS, custom apps and agents
  • The AI supply chain: models, data, vendors and integrations
Hands-on labTrace a request through an AI assistant and its agent tools, end to end.
You'll produceAn architecture map of an AI system: data, tools and trust boundaries.
Module 02 Week 2

Practical AI threats and risks

See how AI systems are actually attacked and how they fail, by trying it yourself in a safe lab.

  • Prompt injection, direct and indirect
  • Sensitive data leakage through prompts, outputs and connected data
  • Over-permissioned agents and unintended actions
  • Unsafe output handling, hallucinations and over-reliance
  • Model and supply chain risks, mapped to the OWASP Top 10 for LLM Applications
Hands-on labRun prompt injection and data leakage attacks against a deliberately vulnerable AI app.
You'll produceA threat model for an AI application.
Module 03 Week 3

Security controls for AI systems

Learn which controls actually reduce AI risk, then apply them and prove they work.

  • Identity and least privilege for AI agents and service accounts
  • Input and output guardrails
  • Data protection: classification, retention and leakage prevention
  • Human review and approval for high-impact actions
  • Logging, monitoring and AI incident response
Hands-on labAdd controls to the vulnerable app from Module 2, then re-run the attacks.
You'll produceAn AI security controls baseline for your organization.
Module 04 Week 4

AI risk assessments, end to end

Run a complete AI risk assessment, from scoping to a decision leadership can act on.

  • Scoping: what the system does, who uses it, what it can affect
  • Mapping data flows and the actions an AI system can take
  • Identifying and rating risks by likelihood and impact
  • Treatment decisions: approve, approve with conditions, or reject
  • The risk register, the leadership summary and re-review triggers
Hands-on labAssess a realistic AI use case modeled on popular AI apps and platforms.
You'll produceA completed risk assessment and a one-page leadership summary.
Module 05 Week 5

Operationalizing AI governance

Turn one-off assessments into a governance program that runs every day.

  • AI policies, standards and procedures that people follow
  • Roles and accountability: owners, approvers and oversight
  • The AI intake and approval workflow
  • Keeping a living AI inventory
  • Third-party AI, awareness training and governance metrics
Hands-on labDesign the AI intake and approval process for a sample company.
You'll produceAn AI acceptable-use policy and a governance operating model.
Module 06 Week 6

AI compliance and frameworks

Map your program to the frameworks auditors and regulators ask about, without doing the work three times.

  • The EU AI Act: risk categories and what they require
  • ISO/IEC 42001: the AI management system standard
  • NIST AI RMF: Govern, Map, Measure, Manage
  • Mapping one set of controls to many frameworks
  • Evidence and audit readiness
Hands-on labBuild a compliance mapping and gap analysis for a sample AI program.
You'll produceA compliance mapping workbook and a prioritized gap list.
Capstone · Certification

Earn your certificate on a real-world scenario.

No multiple-choice exam. You are given a realistic company that is rolling out AI, and you do the work a practitioner would do.

01Get the scenarioA realistic organization, its AI systems and its constraints.
02Do the workRisk assessment, controls plan, governance recommendations and compliance mapping.
03Submit itYour work is reviewed by a practitioner against a clear rubric.
04Get certifiedA verifiable certificate, a CPE letter and work samples for your portfolio.
Bonus materials

Templates you keep and reuse

  • AI policy, procedure and standards templates
  • AI risk assessment templates with worked examples modeled on popular AI apps and platforms
  • Compliance mapping workbook across the EU AI Act, ISO 42001 and NIST AI RMF
How it runs

Built to fit around your job

  • Short lessons of about 15 minutes, so most of your time goes into labs
  • Labs run in your browser with synthetic data, safe for a work laptop
  • Live Q&A every two weeks, plus a peer community
  • Content updated as AI tools and regulations change
Founding cohort · 100 seats

Become the person your company trusts with AI governance.

$750 $1,000
Founding pricing for the first 100 learners · 14-day money-back guarantee · Card or invoice