14 portfolio-connected simulations turn the 24-week syllabus into practical judgement, professional artefacts and defensible governance decisions. Every lab includes a fictional company evidence pack, commercial pressure, assurance testing, trainer review and export-ready work.
Assess a deliberately flawed candidate-ranking system. Map harms, document risks, choose controls and make a final deployment recommendation.
Every syllabus block now has a guided scenario, structured assessment, control work and downloadable evidence pack.
Each lab includes Learn, Test and Demo modes, six workplace-style source documents, seven sector overlays, stakeholder conflict, assurance sampling, scored review and PDF, Word or Markdown outputs.
Build the baseline vocabulary to describe an AI system, its model type, lifecycle, actors, intended purpose and limitations.
Translate fairness, transparency, accountability, robustness and human-centred values into practical questions, controls and launch criteria.
Practise Govern, Map, Measure and Manage on a fictional use case and convert uncertainty into owned risk actions and testing evidence.
Design an ISO/IEC 42001-aligned governance system: scope, context, leadership, objectives, lifecycle controls and continual improvement.
Run an ISO/IEC 42005-style impact assessment from context and stakeholders through impacts, controls, residual effects and approval evidence.
Classify a fictional system, map provider and deployer duties, identify evidence gaps and build an implementation roadmap.
Assess lawful basis, profiling, automated decisions, transparency, minimisation, retention, DPIA triggers and safeguards.
Convert AI risks into an owned control environment with control activities, evidence, monitoring and management reporting.
Plan assurance, test design and operating effectiveness, assess evidence quality and write a practitioner-ready assurance conclusion.
Separate governance, risk, compliance and assurance, then screen an AI use case across privacy, IP, equality, consumer, contract, safety and sector law.
Review an enterprise RAG assistant and tool-using agent for retrieval, hallucination, leakage, prompt injection, permissions and unintended actions.
Run an AI use case through intake, classification, procurement, release, monitoring and retirement, then apply judgement across six regulated sector cases.
Complete a timed integrated scenario, separate harm from risk and control evidence, issue an approval recommendation and prepare a final viva pack.