The SAIG scheme.
SAIG uses four recognisable levels: one awareness level and three personal-certification levels. The scheme is mapped to Article 4 of the EU AI Act and is grounded in international certification practices for personal certification.
Four levels
Confirms participation in an accredited training. No formal assessment of competences; supports awareness and initial documentation, but does not prove individual proficiency.
Entry-level certificate after a standardised digital test. The holder masters basic knowledge and risk awareness for safe AI use in low-risk contexts.
Core certificate for the labour market. Demonstrates that the holder can apply AI in a structural, verifiable and responsible way in work contexts, with attention to verification, privacy, quality, risk and human responsibility.
Advanced certificate for those who must translate AI literacy into policy, governance, supervision, risk management or organisation-wide implementation.
Attendance or proof of competence
Not every certificate says the same thing.
A certificate of attendance confirms that someone was present at a training session. A SAIG certificate proves that someone has mastered the material — independently assessed, with a fixed pass mark, in a controlled environment. That distinction is precisely what Article 4 requires: not evidence of effort, but evidence of competence.
| Type of proof | What it shows | When it suffices |
|---|---|---|
| Certificate of attendance / training certificate | Presence at a course or webinar | Awareness level, internal awareness |
| SAIG Awareness Badge | Participation in accredited training, registered | First demonstrable step towards compliance |
| SAIG personal certificate | Proven mastery, independently assessed with a pass mark | Demonstrable AI literacy in line with Article 4 |
The competence model
Six competence domains.
Every SAIG level assesses the same six domains. What changes is the depth, the assessment weight and the responsibility.
AI understanding and system insight
Understanding what AI is, how generative AI works and where limitations such as hallucinations, bias and uncertainty lie.
Responsible prompting and AI use
Using AI tools purposefully, scoping tasks and integrating AI appropriately into work processes.
Critical assessment and verification
Checking AI output for accuracy, source quality, bias, completeness and applicability.
Privacy, security and confidentiality
Handling personal data, business-sensitive information, security risk, copyright and data-leak prevention.
Ethics, law and governance
Basic knowledge of the AI Act, GDPR, human oversight, transparency, fairness and accountability.
Role and sector context
Using AI responsibly within one's own role, sector, risk class and escalation route.
Note.SAIG is founded on international certification practices and is committed to lasting quality assurance. No formal accreditation is claimed.