Build a data governance framework that covers the whole AI lifecycle
AI for Business Institute · D7
Data Governance & Risk
Domain 7 of the AI for Business Institute practitioner standard. Build a data governance framework that covers the whole AI lifecycle; judge data quality by representativeness rather than accuracy; assess residency and data protection where inference and logging actually occur; defend against prompt injection and treat model outputs as untrusted; and control AI agents that can act with scoped permissions, thresholds and an accessible kill switch.
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Time3.4 hours
Structure5 modules · 20 lessons
What you get
$250, and exactly what that buys.
- 5 modules, 20 lessons
One module per framework criterion, about 3.4 hours of material.
- Applied practice, not just reading
Applied scenarios, Card sorting, Matching exercises and more.
- Work at your own pace
Progress is saved as you go, so you can stop and resume on any device.
- Access that does not expire
Once bought, the course stays in your account. There is no renewal to keep it.
- Counts toward the full programme
Complete all nine domains and the Institute issues your programme completion certificate.
This does not include the accreditation exam. This course builds and evidences capability. Accreditation is a separate assessment, available with the full programme for $2,000, and the credential is awarded only if you pass it.
Why this course
Know what good looks like. Practise the decisions that make it real.
Domain 7 of the AI for Business Institute practitioner standard. Build a data governance framework that covers the whole AI lifecycle; judge data quality by representativeness rather than accuracy; assess residency and data protection where inference and logging actually occur; defend against prompt injection and treat model outputs as untrusted; and control AI agents that can act with scoped permissions, thresholds and an accessible kill switch.
Capabilities
What you'll be able to do
judge data quality by representativeness rather than accuracy
assess residency and data protection where inference and logging actually occur
defend against prompt injection and treat model outputs as untrusted
and control AI agents that can act with scoped permissions, thresholds and an accessible kill switch.
How you will learn
Learn. Practice. Prove.
Learn
Short, evidence-led lessons establish the standard and the reasoning behind it.
Practice
Interactive decisions, sorting and scenarios turn concepts into applied judgement.
Prove
A cumulative final exam checks mastery, followed by a spoken capstone.
Inside this courseApplied scenariosCard sortingMatching exercisesPriority rankingConfidence checksCategorisation sprints
Curriculum
A concise view of the complete course.
Data governance framework covering AI data
What a data governance framework is actually for in an AI context, why it must cover the whole AI lifecycle rather than storage alone, and how it differs from a set of data policies.
Data quality and fitness for purpose
Why representativeness rather than accuracy is the data quality property that matters most for AI, how a data-governance failure explains a model that degrades in production, and how to judge data fitness for a specific use.
Privacy, sensitive data and residency
Why data residency turns on where inference and logging occur, how to document data flow so that inference is not overlooked, and how data protection obligations apply across an AI system's processing.
AI-specific security
Why AI systems introduce a threat conventional software does not, how prompt injection works and how it is mitigated, and why an AI system's outputs must be treated as untrusted input by whatever consumes them.
AI risk management, agent authority and incident response
The controls an AI agent that can act without human approval requires, why scoped permissions and thresholds prevent harm before it happens, and why an assistant's own elevated access is a more common failure than a breached access control.
Ready when you are