HIGAET AI Governance & Safety
Learn risk assessment, policy design, and safety testing for AI systems, producing governance documentation and red-team reports through structured practical exercises.
Duration
6 weeks · 8-10 hours/week
Level
Intermediate
Delivery
Online
Status
Open for enrollment
Why this technology matters.
AI governance and safety is the discipline of making AI systems trustworthy, lawful, and safe to operate — covering risk assessment, usage policies, safety testing, and release review. As generative AI moves into support desks, operations, and public-facing products, teams need people who can spot misuse, bias, and privacy risks before launch, and document them clearly.
It is used wherever AI affects people or decisions: a support team setting content guidelines for an assistant, a retailer reviewing a high-risk use case before rollout. It helps teams catch failure modes early, set clear rules, and handle incidents consistently. It does not solve poor model quality or missing data on its own — a policy document does not fix an unreliable assistant, and a checklist does not replace testing and monitoring.
By the end you will be able to build an AI risk register covering misuse, bias, privacy, and operational failures, a red-team test plan with documented findings and severity ratings, and a model-release review workflow for high-risk use cases.
Why this course exists
Most AI courses stop at a working demo, while production demands evidence: what can go wrong, what is allowed, and who approves release. This course closes that gap across the Model → Prompt → Context → Retrieval → Tools → Agents → Evaluation → Security → Infrastructure → Production arc, with weight on evaluation, security, and production readiness — from risk registers and policies through red-team testing to release review workflows.
Know exactly what you're signing up for.
Who is this for
Prerequisites
- No previous governance experience required
- Familiarity with AI deployments or software processes
- Comfort writing policies and review documentation
Technologies & tools
Skills you'll gain
A 6 weeks arc, module by module.
- Module 01
Module 01 — Foundations: AI risk landscape, safety principles, and governance models
- Module 02
Module 02 — Risk Assessment: threat modeling and impact classification for AI uses
- Module 03
Module 03 — Policy Design: acceptable use, content rules, and escalation paths
- Module 04
Module 04 — Safety Testing: red-teaming, jailbreak probes, and evaluation suites
- Module 05
Module 05 — Operations: monitoring, incident response, and release gating
- Module 06
Module 06 — Documentation: model cards, system cards, and audit trails
- Module 07
Module 07 — Regulation: overview of major AI regulatory approaches and obligations
- Module 08
Module 08 — Capstone: governance pack with risk register, policy, and safety test report
Practical Training Flow
Learning → Guided Labs → Independent Practice → Industry Project → Capstone → Portfolio → Career Preparation. Practical hours are tracked alongside instructional hours and surfaced on the certificate.
Delivery as HIGAET Practical Training / Experiential Learning.
What you'll be able to do.
- Build AI risk registers covering misuse, bias, privacy, and operational failure modes
- Design usage policies and content guidelines for generative AI deployments
- Develop red-team test plans with documented findings and severity ratings
- Deploy review workflows for model releases and high-risk use cases
- Integrate logging and incident response procedures for AI system events
- Evaluate models for bias, robustness, and safety using structured checklists
- Secure sensitive data handling through access controls and retention rules
- Automate compliance evidence collection for audits and internal reviews
You will build.
Every project ships as HIGAET Practical Training / Experiential Learning — portfolio-ready work, not exercises.
- Project 01
AI risk register
- Project 02
Generative AI usage policy
- Project 03
Red-team test report
- Capstone
Model release governance review package
Speak the language first.
- AI risk registers
- Structured lists of possible harms such as misuse, bias, privacy leaks, and operational failures, each rated by likelihood and impact.
- Usage policies
- Written rules that state what users may and may not do with a generative AI deployment.
- Content guidelines
- Standards for acceptable model outputs, including how to handle sensitive, false, or harmful content.
- Red-teaming
- Deliberately probing an AI system with tricky or adversarial inputs to uncover weaknesses before real users do.
- Severity ratings
- Labels such as low, medium, or high that rank each safety finding so the worst issues get fixed first.
- Safety test plans
- Documented sets of test cases covering bias, privacy, misuse, and failure modes for an AI system.
- Release review workflows
- Approval steps a model or feature must pass before launch, especially for high-risk use cases.
- Operational failure modes
- Ways an AI system can break in production, such as silent wrong answers, outages, or runaway actions.
- Governance documentation
- Records of risks, policies, tests, and decisions that show how an AI system was evaluated and approved.
Fix, check, and go deeper.
Troubleshooting & common mistakes
Risk register misses key failure modes
Walk each lifecycle stage of input, model, output, and deployment use, and add misuse, bias, privacy, and operational risks with owners.
Vague usage policies nobody can enforce
Rewrite rules as specific allowed and prohibited behaviors with examples, then map each rule to a detection or review step.
Red-team findings lack severity ratings
Score each finding by impact and ease of reproduction, then re-sort the report so high-severity items get fixes first.
Safety tests pass but harms slip through
Add adversarial and edge-case prompts for bias, privacy, and misuse, and rerun the suite against fresh model outputs.
High-risk releases bypass review
Define clear triggers for mandatory review, add a checklist gate before release, and log every approval decision.
Before you move on, you should be able to
- Build an AI risk register covering misuse, bias, privacy, and operational failures
- Design usage policies and content guidelines for a generative AI deployment
- Develop a red-team test plan with documented findings and severity ratings
- Deploy a release review workflow for models and high-risk use cases
- Explain how governance documentation supports safe model releases
- Evaluate safety test results and prioritize fixes by risk level
Start your application.
Share a few details and a HIGAET advisor will reach out within one business day with next steps.
Common questions
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A 6 weeks course — AI & Generative Intelligence.