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laryOsman- Instructor Rating - Reviews - Students - CoursesNo biography available, please complete
📘 About This Course
Most AI compliance projects die the same way. Someone asks "are we compliant with the AI Act?", and the honest answer is a shrug: nobody can name the systems in scope, nobody knows whether the company is a provider or a deployer, and the only evidence is a slide deck written six months ago by a team that has since moved on.
This course builds the other thing — the governance system that answers that question with artefacts instead of opinions. You start from an empty Ubuntu machine and finish with an AI inventory, a risk and impact assessment, a DPIA, a technical file, an incident path, a control matrix, a fairness and explainability audit, and an executive compliance pack, all of them produced on camera and all of them traceable back to a primary legal source.
Every framework is handled the way a practitioner has to handle it. The EU AI Act is worked through from scope and role allocation to prohibited practices, risk classification, transparency, high-risk obligations and GPAI documentation. GDPR is covered where it actually bites on AI: lawful basis, Article 22 automated decisions, DPIA, minimisation and retention. NIS2 and DORA are treated as what they are — security and resilience regimes that now wrap around the AI stack, with real incident-reporting deadlines and a third-party register. NIST AI RMF gives the GOVERN / MAP / MEASURE / MANAGE spine, and ISO/IEC 42001 turns it into a management system you can audit and certify.
The last three modules are where it becomes a job rather than a reading list: an end-to-end algorithmic audit with fairness metrics, explainability, robustness and prompt-injection testing; then vendor due diligence, continuous monitoring, drift and change governance; then a capstone that runs a multi-framework audit across one organisation and produces the board-level pack.
Two rules run through all 51 lessons. First, every regulatory claim is checked against a primary source on camera — EUR-Lex, the Commission and AI Office, ENISA, EDPB, NIST, ISO public pages — and binding law, national transposition, regulator guidance, voluntary frameworks and internal policy are never allowed to blur into each other. Second, the assistant is never the authority: a governed LLM is used to draft, extract and summarise, and there is a lesson that deliberately shows it inventing article numbers with high self-declared confidence, so you learn to catch it.
This is professional education, not individualised legal advice. Where a question turns on jurisdiction-specific interpretation, the course says so and flags it for qualified legal review.
What You'll Learn
Decide, for a concrete system, whether the EU AI Act applies, which role you hold (provider, deployer, importer, distributor, GPAI), and which risk tier and obligations follow.
Build and maintain an AI system inventory and an evidence register that an auditor can actually walk through.
Assemble a high-risk technical file: risk management, data governance, logging and traceability, human oversight, accuracy, robustness and cybersecurity, plus a GPAI evidence matrix.
Run a GDPR workstream for AI: lawful basis, Article 22 automated decisions and meaningful human intervention, a full DPIA, minimisation, retention and access controls.
Apply NIS2 Article 21 measures to an AI stack, triage an AI incident and meet the reporting path; build the DORA control matrix and register of information for a third-party LLM service.
Operate NIST AI RMF end to end (GOVERN, MAP, MEASURE, MANAGE) including generative-AI specific risks, and run an ISO/IEC 42001 AI management system through internal audit and review.
Perform an algorithmic audit: data quality and lineage, fairness metrics and bias testing, explainability, model and system cards, robustness, prompt injection and data-leakage testing.
Run vendor due diligence, continuous monitoring, drift and change governance, and produce an executive compliance pack and regulatory-watch process for the board.
🛠️ Requirements
- Comfortable on the Linux command line, with basic SQL and basic Python; no prior compliance or legal background is assumed.
- A machine or VM with 8 GB of RAM, 4 CPU cores and about 20 GB of free disk, running Ubuntu 22.04 or 24.04.
- The lab stack is free and open source: Docker, PostgreSQL, Adminer, Python, JupyterLab and Git, with synthetic datasets throughout. No GPU and no paid cloud account.
- Access to a governed LLM endpoint is optional: the labs show how to use one safely, and every exercise can be completed without it.
👤 Who This Course is For
- AI governance, risk and compliance officers who need to turn framework text into artefacts and evidence.
- DPOs, privacy engineers and legal or regulatory specialists who have to work with the engineering side of AI.
- Security, GRC and internal audit professionals picking up NIS2, DORA or ISO/IEC 42001 responsibilities for AI systems.
- ML engineers, data scientists and technical leads whose systems are about to fall in scope, and who would rather build the evidence than inherit the finding.
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John Doe
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John Doe
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