NDMO's National Data Governance Interim Regulations and SDAIA's AI Ethics Principles both require demonstrable data governance before AI models can be deployed in regulated environments.
Saudi Arabia's data governance spans multiple regulators: NDMO sets national standards for data classification and sharing; SDAIA governs AI ethics; SAMA enforces financial sector data controls; and the PDPL mandates personal data protection with significant penalties.
Every AI model inherits the quality, bias, and governance posture of its training data. Without classification, you cannot control what data enters which model. Without lineage, you cannot audit model outputs. Without quality controls, your AI delivers confident wrong answers at scale.
Enterprise.AI assesses data readiness across six interdependent dimensions. Each maps to specific NDMO regulations, SAMA expectations, and AI deployment prerequisites.
Taxonomy aligned to NDMO's four-tier classification. Every dataset tagged, every access control mapped, every cross-border transfer rule enforced.
Measured across six axes: completeness, accuracy, consistency, timeliness, validity, and uniqueness. AI accuracy is bounded by input data quality.
CDO mandate, domain data owners, stewardship networks. Decision rights for data creation, modification, sharing, and deletion — formalised and enforced.
Conformance with NDMO Interim Regulations, Open Data Policy, and Data Sharing. PDPL readiness including consent management and breach notification.
End-to-end visibility from source systems through transformation to AI model inputs and outputs. Critical for regulatory audit trails and explainability.
A searchable inventory of all data assets — business glossary, technical metadata, ownership, sensitivity labels, and usage policies.
Rate your institution across each dimension (1–5). The radar chart updates in real time. A score of 3 or above across all dimensions is the minimum threshold for enterprise AI deployment.
For each dimension, assess your current state against the key indicators below.
| Dimension | Key Indicators | NDMO/Regulatory Link | AI Prerequisite |
|---|---|---|---|
| Classification | Taxonomy defined · All datasets tagged · Access controls enforced · Cross-border rules applied | NDMO Data Classification Policy · PDPL Art. 29 | Model risk tiering · Data access for training |
| Quality | Profiling complete · Quality rules automated · Monitoring dashboards live · Remediation SLAs defined | SAMA Operational Risk · NDMO Data Quality Standards | Model accuracy · Bias detection · Output reliability |
| Governance | CDO appointed · Stewards active · Councils meeting · Policies enforced · Escalation paths clear | NDMO National Data Governance Regulations | AI governance integration · Decision rights for AI data use |
| NDMO/PDPL | Consent management · Data subject rights · Breach procedures · Transfer controls · DPO appointed | PDPL · NDMO Open Data · NDMO Data Sharing Regulations | Lawful AI training data · Customer data in models |
| Lineage | Source-to-report tracing · Transformation documented · Impact analysis capability · Audit trail complete | SAMA Model Risk · NDMO Data Lifecycle | Model explainability · Regulatory audit · Bias tracing |
| Catalogue | Business glossary · Technical metadata · Ownership mapped · Sensitivity labels · Search enabled | NDMO Metadata Standards · NDMO Data Inventory | Feature store readiness · Data discovery for AI teams |
Your remediation sequence depends on where you are in the AI journey.
Key NDMO regulations and their data readiness implications for AI-deploying institutions.
Data governance framework · CDO appointment · Data management standards · Annual compliance reporting
Four-tier classification · Labelling standards · Handling procedures · Access controls per tier
Sharing agreements · Purpose limitation · Security requirements · Cross-entity sharing controls
Public dataset publication · Format standards · API requirements · Update schedules
Consent · Data subject rights · Cross-border transfer · Breach notification · DPO appointment
Enterprise.AI delivers a 4-week structured data readiness assessment with stakeholder interviews, automated profiling, NDMO gap analysis, and a prioritised remediation roadmap.