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Full AI & Data Compliance Audit
For organisations that have to prove it — to a regulator, a board, or an enterprise buyer.
A diagnostic tells you what has to change. An audit answers a different question: whether the record you hold would survive being challenged by someone with the authority to disagree. Not whether a control exists, but whether you can evidence that it was exercised.
This engagement combines deep technical analysis of your models and datasets with a strict legal review by qualified counsel, producing an assessment you can put in front of a regulator, a board, or an enterprise client.
What we audit
Models — behavior, robustness, and documentation against the technical requirements of each applicable framework.
Datasets — bias and fairness testing using established, auditable methods, including counterfactual analysis where appropriate.
Transparency & explainability — whether your documentation and disclosures meet what SDAIA, PDPL and the EU AI Act actually require, article by article.
Legal posture — contracts, notices, records of processing, and accountability structures, reviewed by qualified counsel rather than checklist software.
Sector-specific obligations — SAMA requirements for financial institutions, health-data and SFDA rules for healthcare providers, or policyholder-data requirements for insurers — reconciled with PDPL, SDAIA and NCA in the same assessment. You get one consistent position, not parallel checklists per regulator.
What you receive
A comprehensive Audit Report (bilingual EN/AR available) with per-framework findings, evidence references, and compliance scores.
A remediation plan with prioritized actions (P1/P2/P3), owners, and suggested sequencing.
Regulator-ready documentation — structured so it can be produced on request.
A findings presentation for your leadership or board.
Who it's for
Organizations facing a concrete trigger: a regulatory inquiry, an enterprise procurement requirement, an investor due-diligence process, or an internal mandate to get AI governance in order before scale.
How it works
- 01
Scoping & data-handling setup — systems, datasets, and frameworks in scope. The data-access model is agreed up front and adapted to your PDPL obligations: from document-based review, to audits run inside your own environment with only aggregated results shared, through to on-site review where required. Your data never has to leave your infrastructure.
- 02
Technical audit — model and dataset analysis.
- 03
Legal review — obligations mapped and verified by qualified counsel.
- 04
Consolidated reporting — technical and legal findings reconciled into one assessment.
- 05
Delivery & presentation
Timeline
Typically 4–8 weeks, depending on the number of systems in scope and data-access arrangements — confirmed at scoping.
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