Illustrative: a chain of blocks each linked by hash to the one before, with a Merkle tree under the newest
Risk Academy, led by the Nexus Consortium · Course catalogue · Technology and Digital Systems
Artificial Intelligence and Autonomous Systems
AI assurance and audit
AI assurance and audit, taught twice over: as the working methods of the field and as its frontier since 2024, with labs on the Academy's tools, open data and the Nexus Standards.
01Core course · L2 Intermediate
AI assurance and audit: foundations and practice
This area covers how organisations and third parties establish justified confidence in AI systems: impact assessment, conformity assessment, audits of AI management systems, assurance cases and the documentation regulators require.
- Level
- L2 Intermediate
- Study time
- about 24 hours (planning estimate)
- Status
- Planned
- Prerequisites
- An L1 course in the area, or an entry review
Why now
Changes compliance timelines and planning assumptions for organisations deploying high-risk AI systems in the EU. Council of the European Union, Artificial intelligence: Council and Parliament agree to simplify and streamline rules, 7 May 2026
Learning outcomes
- Define AI assurance and the other key terms of the discipline and use them correctly in a written case
- Explain AI system impact assessment and AI risk management frameworks with a worked example and its source
- Apply the discipline's method to a documented case and explain each result with its source
- Analyse a named case against auditing AI management systems, stating evidence, date and limits
Modules
- AI system impact assessment
- AI system impact assessment
- AI risk management frameworks
- Auditing AI management systems
- Conformity assessment under the EU AI Act
- Conformity assessment under the EU AI Act
- Assurance cases and evidence
- Documentation: model cards, system cards and technical files
- Agentic AI assurance and oversight
- Agentic AI assurance and oversight
- Red teaming and third-party evaluation
- Post-market monitoring and incident reporting
- Lab: the discipline's method
- Control coverage is two figures, designed and operating; operating effectiveness is judged from a sample of records, and the sample size bounds the exception rate an auditor can rule out, so the opinion rests on how many records were pulled as much as on what they showed.
- Work the method through a documented case and explain each result
- Read the method's sources and stated values
Labs with Academy tools
- MethodFrom a control list to an audit opinion: what a sample of records can establishThe discipline's method on the discipline page, with its cited sources, worked through a documented case
- InstrumentOrbit propagatorA faculty instrument on the faculty page, run on the faculty's digital twin data
- InstrumentEvaluation ledgerA faculty instrument on the faculty page, run on the faculty's digital twin data
- Twin builderDigital twin builderBuild the faculty's place in 3D, switch layers and read the credits and dates of each
- Map layerWeather forecast, 15 days with ensemble range (ECMWF via Open-Meteo)An open map layer the Academy's licence register admits, read in the map tools
- Map layerNight lights tonight, outages (VIIRS Day/Night Band)An open map layer the Academy's licence register admits, read in the map tools
- Map layerBuilt-up and settlement extent, 2010 (HBASE, Landsat)An open map layer the Academy's licence register admits, read in the map tools
- Foundry roomAssurance room, NXS-NSF (trust, authorisation and assurance)Assurance cases and audit findings for AI systems
- Foundry roomBenchmarks room, NXS-LAB (research, experiments and simulations)Third-party evaluation of AI systems on stated tasks
Assessment
- 40%Lab recordWork the discipline's method through a documented case and explain each result with its source and date
- 40%Case memoApply the discipline's methods to a named place or organisation, stating evidence and limits
- 20%Key terms checkShort answers on the key terms, marked against the definitions
Delivery
- Self-paced online, with the labs in the browser
- Cohort study over six to eight weeks, with a weekly lab review
- Co-delivered with a partner institution, inside its own courses
Partner on this course
- Co-teach a module and bring a current case from your work
- Host a cohort for your learners, with your own schedule and review
- Contribute a lab: a dataset your institution publishes openly, or a model
- Review the course content and its references before it opens
- Translate the course and adapt its cases to your region
How institutions and experts take part
In the Academy LMS
- Course on the learning platform
- Intermediate, 4 topics
- Cohort group
- A community group for each cohort of learners
- Achievement
- Recorded on completion of the assessed work
References
- Council of the European Union, Artificial intelligence: Council and Parliament agree to simplify and streamline rules, 7 May 2026
- Raji et al., Closing the AI Accountability Gap: Defining an End-to-End Framework for Internal Algorithmic Auditing, 2020
- Mökander, Schuett, Kirk and Floridi, Auditing large language models: a three-layered approach, 2023
- Schuett, Three lines of defence against risks from AI, 2023
02Live map
Connected activity in the United Kingdom and Ireland
Night-time lights and population density, the footprint of electrified, connected activity, over the United Kingdom and Ireland.
The interactive map needs JavaScript; the layer list and sources are below.
Layers 2
Connected activity
Keyboard, with the map focused
Hold Ctrl or ⌘ and scroll to zoom
Latest available
NASA GIBS / EOSDIS · ASTER GDEM (METI, NASA) · Blue Marble (NASA) · © OpenStreetMap contributors (ODbL) · Natural Earth
Loading the latest NASA imagery.
Drag to move. Ctrl or ⌘ and scroll, pinch or the buttons to zoom; hover for the values under the pointer; reset returns to the region.
Connected activity, framed on the United Kingdom and Ireland.Satellite and model products as NASA publishes them, each with its own date. They are indicators, not assessments of any place.
Data: NASA GIBS · ASTER GDEM · Blue Marble · © OpenStreetMap contributors · Natural Earth · NASA EONETSources and licences
- We acknowledge the use of imagery provided by services from NASA's Global Imagery Browse Services (GIBS), part of NASA's Earth Science Data and Information System (ESDIS). Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). NASA open data
- We acknowledge the use of imagery provided by services from NASA's Global Imagery Browse Services (GIBS), part of NASA's Earth Science Data and Information System (ESDIS). Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). NASA open data
- VIIRS Black Marble night-time lights, NASA Goddard Space Flight Center; distributed by the NASA Level-1 and Atmosphere Archive and Distribution System (LAADS DAAC); imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). NASA open data
- Center for International Earth Science Information Network (CIESIN), Columbia University. 2018. Gridded Population of the World, Version 4 (GPWv4): Population Density, Revision 11. NASA Socioeconomic Data and Applications Center (SEDAC). Licensed under CC BY 4.0. Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). doi:10.7927/H49C6VHW. CC BY 4.0
- VIIRS near-real-time products (flood, land surface temperature, snow cover, corrected reflectance), NASA LANCE; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). NASA open data
- ASTER GDEM is a product of METI and NASA (ASTER Global Digital Elevation Model V003, NASA LP DAAC); imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). Cite as: NASA/METI/AIST/Japan Spacesystems and U.S./Japan ASTER Science Team (2019). ASTER Global Digital Elevation Model V003. doi:10.5067/ASTER/ASTGTM.003. NASA open data
- Made with Natural Earth. Free vector and raster map data @ naturalearthdata.com. Public domain
- © OpenStreetMap contributors (ODbL), via NASA GIBS (coastlines, boundaries, roads and place names). Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). ODbL
- Blue Marble, NASA Earth Observatory; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). NASA open data
- Context on the globe and flat map: coastlines, boundaries, roads and place names from NASA GIBS reference layers (OpenStreetMap data, ODbL, © OpenStreetMap contributors); Blue Marble imagery as an optional base; 3D terrain and the elevation read-out from the ASTER GDEM elevation colour index (5 m to 200 m classes, exaggerated for legibility). Boundaries are drawn as published and imply no position on any territory.
- Globe: MapLibre GL JS 6.9.1, BSD-3-Clause, © MapLibre contributors.
- Live events: NASA Earth Observatory Natural Event Tracker (EONET); events are indicative and keep their original source links.
- NASA GIBS: We acknowledge the use of imagery provided by services from NASA's Global Imagery Browse Services (GIBS), part of NASA's Earth Science Data and Information System (ESDIS).
- Optional layers (3 sources in the layer list: live, forecast, observed and context) print their provider credit on the map and here when switched on; forecasts are model output and derived indices are estimates.
Layers and dates
- Night-time lights, daily: VIIRS Black Marble (VNP46A2), NASA LAADS DAAC · 12 Oct 2026
- Population density, 2020: Population: CIESIN, Columbia University, Gridded Population of the World v4.11 (2018), NASA SEDAC, CC BY 4.0 · static
- True colour: VIIRS NOAA-20 corrected reflectance, NASA LANCE · 13 Oct 2026
03How learning runs
How learning runs, as a process you can run
Select a scenario and press Play: course, pathway, practice, faculty, cells and record.
04What is changing
What is changing, with standards and evidence
AI assurance and audit: frontier methods
The methods, data, tools and studies that changed AI assurance and audit between 2024 and 2026, taught by reproducing them on open data.
- Level
- L3 Advanced
- Study time
- about 28 hours (planning estimate)
- Status
- Planned
- Prerequisites
- AI assurance and audit: foundations and practice; Working knowledge of a scripting language (Python or R) for the labs
Why now
Changes compliance timelines and planning assumptions for organisations deploying high-risk AI systems in the EU. Council of the European Union, Artificial intelligence: Council and Parliament agree to simplify and streamline rules, 7 May 2026
Learning outcomes
- Explain what changed in AI assurance and audit since 2024 and cite the studies, data or rules behind each change
- Reproduce EU agrees to delay AI Act high-risk obligations on open data and state the uncertainty of the result
- Evaluate a 2024 to 2026 study: its question, data, method, limits and what it changes for practice
- Design an analysis that combines a frontier method with the faculty's instruments, and justify each choice
Modules
- EU agrees to delay AI Act high-risk obligations
- Council and Parliament reached a provisional deal on the Digital Omnibus on AI, moving high-risk obligations to December 2027 for stand-alone systems and August 2028 for systems embedded in products, and adding a ban on non-consensual intimate content generation.
- Changes compliance timelines and planning assumptions for organisations deploying high-risk AI systems in the EU.
- Council of the European Union, 7 May 2026
- Reproduction lab
- Extend the discipline's method with one frontier method and compare its results
- State the uncertainty and the limits of the result
- Peer review against a shared checklist
Labs with Academy tools
- MethodFrom a control list to an audit opinion: what a sample of records can establishThe discipline's method on the discipline page, with its cited sources, worked through a documented case
- InstrumentOrbit propagatorA faculty instrument on the faculty page, run on the faculty's digital twin data
- InstrumentEvaluation ledgerA faculty instrument on the faculty page, run on the faculty's digital twin data
- InstrumentIncident timelineA faculty instrument on the faculty page, run on the faculty's digital twin data
- Twin builderDigital twin builderBuild the faculty's place in 3D, switch layers and read the credits and dates of each
- Map layerWeather forecast, 15 days with ensemble range (ECMWF via Open-Meteo)An open map layer the Academy's licence register admits, read in the map tools
- Map layerNight lights tonight, outages (VIIRS Day/Night Band)An open map layer the Academy's licence register admits, read in the map tools
- Map layerBuilt-up and settlement extent, 2010 (HBASE, Landsat)An open map layer the Academy's licence register admits, read in the map tools
- Research hubResearch hubToday's open data and publications in the faculty's subjects, filtered to this discipline
- Foundry roomAssurance room, NXS-NSFAssurance cases and audit findings for AI systems
- Foundry roomBenchmarks room, NXS-LABThird-party evaluation of AI systems on stated tasks
Assessment
- 50%Method reproductionReproduce one frontier method on open data and state the uncertainty of the result
- 30%Study reviewA critical review of a 2024 to 2026 study: question, data, method, limits, what it changes
- 20%Peer reviewReview two other learners' reproductions against a shared checklist
Delivery
- Self-paced online, with the labs in the browser
- Cohort study over six to eight weeks, with a weekly lab review
- Co-delivered with a partner institution, inside its own courses
Partner on this course
- Co-teach a module and bring a current case from your work
- Host a cohort for your learners, with your own schedule and review
- Contribute a lab: a dataset your institution publishes openly, or a model
- Review the course content and its references before it opens
- Translate the course and adapt its cases to your region
How institutions and experts take part
In the Academy LMS
- Course on the learning platform
- Expert, 2 topics
- Cohort group
- A community group for each cohort of learners
- Achievement
- Recorded on completion of the assessed work
References
- Council of the European Union, Artificial intelligence: Council and Parliament agree to simplify and streamline rules, 7 May 2026
- Raji et al., Closing the AI Accountability Gap: Defining an End-to-End Framework for Internal Algorithmic Auditing, 2020
- Mökander, Schuett, Kirk and Floridi, Auditing large language models: a three-layered approach, 2023
- Schuett, Three lines of defence against risks from AI, 2023
How the mapping to the Nexus Standards is read
The Academy proposes how each discipline teaches toward the Nexus Standards; the mapping is open for public review. Bodies and standards are named so learners can find and apply their published work; the Academy teaches their use and links to the publisher, and no affiliation is implied.
05Take part
Take part in the work
Start from who you are.
06Questions
Questions, answers and key terms
Is the course free?
Yes. Every course and learning material of Risk Academy, led by the Nexus Consortium, is free to study, self-paced or with a cohort. Ordinary participation has no fee; paid work, internships and fellowships are separate arrangements.
What does completing a course give me?
A completion record: the course, its version, the date and what was assessed, private by default and shared by you. It stacks into discipline, department and programme achievements. A record states what was studied and assessed and does not certify competence; it is not a licence, a professional title or an employment status.
Where do the data and the places come from?
Every faculty teaches on a twin of a real place built from open data; each layer carries its provider, licence and date, and every result on the Academy states its source. Maps and figures are indicators for teaching, never an assessment of a place.
How do I take part beyond the course?
Form a competence cell of three to seven people, study a shared pathway, take a supervised assignment in the National Working Groups, or bring a cohort from your institution. For organisations.
Key terms
Term
AI assurance
The processes that build and communicate justified confidence that an AI system meets its requirements.
Term
Impact assessment
A structured analysis of how an AI system may affect individuals, groups and society, as in ISO/IEC 42005:2025.
Term
Conformity assessment
The process of demonstrating that a high-risk AI system meets the requirements of the EU AI Act before it is placed on the market.
Term
Assurance case
A structured argument, supported by evidence, that a system is acceptable for a use.
ReportsContribute
Contribute evidence from this page.
Reports is the Consortium's quarterly publication. What you know about this subject can become evidence, a case or a correction in the next edition, credited to you and published under CC BY 4.0 where it is your own work.
Risk Academy
Related
Around AI assurance and audit: courses
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Study AI assurance and audit with us, or teach it
Every course is free and open. Institutions and experts can co-teach, host a cohort, contribute a lab or review a course before it opens.