AI governance: from principles to practice

We have AI principles… but where is the evidence?

The Saudi Data and AI Authority (SDAIA) has adopted 23 national AI standards, announced at the 10th Standards Forum in October 2026. They cover:

• Governance and management systems
• Training data quality
• Safety, robustness, security and privacy
• Reference frameworks and big data

Standards define how AI requirements are applied, measured and proven.

Principles alone are no longer enough. So how does your organisation move from "we have principles" to "we have controls and evidence"?

1. Classify use cases by impact

Drafting an email is not the same as a recommendation that affects hiring or credit, and controls should scale with impact:

• Low impact (drafting or summarising): human review before use.
• Medium impact (internal decision support): regular testing and performance monitoring.
• High impact (recommendations affecting hiring or credit): the strongest controls and approval before launch.

2. Every use case has an owner, and a written authority to stop it

When a model gets it wrong, everyone should know who can switch it off, and that authority should be documented, not left to judgement.

3. No launch without an evidence file

What isn't documented can't be proven. Before any launch, the file should include:

• A risk assessment
• Test results
• A data review
• An access review

Good governance doesn't slow innovation down. It lets you scale it with confidence.

Need to build AI governance controls and evidence files for your organisation? Contact us.

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Source: Al-Jazirah