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AEES

AEES Executive Certificate in Data and AI governance

Protect data, trace processing and hold an AI frame. This AEES certification attests data and AI governance.

  • AssessmentProfessional assessment
  • Attempts2
  • PreparationIncluded
  • France
  • Canada
Description

Data and AI governance

A vague familiarity with “Data and AI governance” is no longer enough. This belongs with AI, data and digital. You lead AI, you govern data and you frame a digital change. People are hired to decide, not just to name the topic. This AEES certification is for professionals, managers and leaders who want to limit rights to what is needed, frame a processing or a model and set retention and accountabilities. No diploma is required. What is required is a working command of written French, and the will to show the subject on a real file, in a company, a public body or a partner organisation.

You are not joining a long degree. You take “Data and AI governance” seriously enough to use it, and seriously enough for someone else to read it on a file. Everything happens online, around a job you already have. You prepare if you need to, then you sit a professional assessment. The AEES Executive Certificate, if it is issued, is proof in your name, and it can be checked. It holds in a CV, a move or a cooperation. It is a short specialisation, issued by a higher-education institution. You see at once what you are buying: a targeted title, not a course catalogue.

Afterwards you can carry “Data and AI governance” into a team, a file or a new responsibility, and explain your choices to a manager, a funder or a partner institution. Others will see that you can limit rights to what is needed, frame a processing or a model and set retention and accountabilities, including outside your own organisation. If you succeed, AEES issues an AEES Executive Certificate in your name, with a unique serial number that can be checked in the public register. The award stays in your workspace. It shows a professional judgement you have demonstrated, useful for a CV, a new post or work in common.

Skills

What this certificate attests

The capabilities the assessment attests if you pass.

  • Limit rights to what is needed
  • Frame a processing or a model
  • Set retention and accountabilities
  • Set what you keep
  • Decide who alerts
  • Decide which data the organisation is allowed to hold
ESCO classification — European Skills, Competences, Qualifications and Occupations

ESCO, the European skills vocabulary

This is the European Commission’s classification of skills, competences, qualifications and occupations. This certificate is linked to it through the skills below. Each link opens the official record.

Knowledge & research

Related scientific readings

Scientific readings related to this certificate.

Preparation included

To prepare for the assessment

After purchase, a preparatory course is available: 6 written modules, without an instructor, at your own pace. You are not required to follow it before opening the assessment. Each module sets out the notions, a commented case, the points to keep and a FAQ. It covers Useful data, Basis, Access and Model.

When you are ready, you enter the assessment: a file already open, incomplete facts, conflicting views. You move through successive decisions. This is not a full taught programme with pedagogical supervision.

Structured written course

Notions, objectives and concrete examples, organised progressively.

Applied case

A commented professional situation to anchor the theory.

FAQ and preparation

Frequent doubts, then questions to go further on your own.

  1. Module 1, Useful data: deciding what your organisation is allowed to hold Useful data is not everything you could collect. It is the smallest, clearest set of information you can lawfully hold to achieve an explicit business purpose. This module shows you how to decide what belongs in that set, with a repeatable method you can apply to a real dossier. You will learn to translate broad legal and governance principles into concrete choices: which attributes to collect, what to exclude, which transformations to apply, and how to document the decision in a way that works across privacy, security, compliance, and AI risk teams. You will connect the questions you already face in operations with authoritative guidance from data protection and AI risk frameworks.
  2. Module 2 · Basis: Define Purposes and Boundaries for Data and AI This module teaches the foundation of data and AI governance. Before collecting a field or training a model, you need a purpose that is precise, useful for the business, and suitable under applicable rules. A good purpose lets you decide what to collect and what not to collect, who can access and who cannot, what you can do next and what is out of bounds. It also gives you a way to tell stakeholders why the processing exists and how risk is controlled. You will learn how to translate a business outcome into a purpose statement, select a lawful basis when personal data is involved, and define the boundaries that follow. These boundaries include limits on secondary use, profiling, marketing, training and fine-tuning of AI models, enrichment through external sources, and retention. You will also learn how to record your decisions, so they can be checked, audited and improved over time.
  3. Module 3/6, Access Access is where governance becomes real. It decides who can see data, who can run a model, and who can change a production pipeline. Good intentions about privacy, security and ethics fail without precise access decisions. This module gives you a method you can apply tomorrow: define the minimum rights that make work possible, prove why those rights are needed, and remove everything else. You will structure access around business roles, data classifications and model lifecycle stages. You will translate those decisions into technical controls like identity federation, multifactor authentication, scoped tokens, time-bound privileges and audit trails. Vendor access is treated as a first-class case, not an exception. You will know how to onboard a supplier with narrow, monitored rights, and how to turn those rights off without drama.
  4. Module 4: Model This module treats an AI initiative as a processing activity first, and a technical system second. You will learn to define what the model is allowed to do, what data it may use, who is accountable for each decision, and how long each artifact is kept. By the end, you will be able to document and justify the model in the same way you document any other processing in your organisation. We use established governance concepts such as purpose limitation, data minimisation, role-based access, and risk assessment. Instead of talking in generic terms about innovation, we convert an AI use case into a recordable process with explicit boundaries. That boundary is what lets you approve or reject the use, demonstrate compliance, and maintain control during updates and vendor changes.
  5. Module 5: Retention Retention is the practical expression of purpose limitation and risk control. You decide how long to keep data, models, logs, and proofs, then you make deletion happen reliably. In this module you will learn to design retention rules that serve compliance, reduce risk, preserve necessary evidence, and still let your AI teams reproduce results when needed. You will translate legal and business needs into specific durations and system behaviours. This includes different clocks for raw data, features, model versions, prompts, audit trails, backups, and archives. You will also account for legal holds, sector rules, operational dependencies, and the cost and risk of keeping data. The outcome is a schedule with owners, embedded in your tooling, monitored, and tested.
  6. Module 6: Incident Governance for Data and AI Incidents are the stress test of your governance. A single confused minute in the first hour can multiply damage, cost, and regulatory exposure. This module gives you a practical structure to decide who alerts, who owns, and who closes, across both data and AI. You will set decision rights, thresholds, and documentation so the response is fast, coordinated, and auditable. Data and AI incidents do not look the same. A personal data breach may require notifying a regulator and affected individuals. A degraded AI model can cause silent harm or discriminatory outcomes. Third parties can trigger cascading impacts. Your governance must connect security, data protection, AI risk, legal, and communications, with one named owner and closure criteria that do not depend on personalities.