The spine of this page is the Saudi control set: PDPL and its implementing regulations, NDMO data management standards, NCA Essential Cybersecurity Controls, and data residency assigned by classification. Below it are the sector-specific bodies, SDAIA, SAMA and DGA, and what we do about each so your team can answer a reviewer without assembling the evidence by hand.
Six bodies of expectation shape most Saudi data and AI reviews. Each entry below is what it covers, and what we do about it in delivery.
Cloud cybersecurity controls covering classification-driven hosting decisions, tenant separation, key management, logging and supplier assurance.
What we do: We place each workload according to data classification and write down the reasoning, so a reviewer can follow why a component runs where it runs.
Interoperability, digital service quality and data-sharing expectations for government entities and their delivery partners.
What we do: Integrations are built as governed, documented interfaces with defined ownership rather than point-to-point extracts nobody maintains.
Free download · PDF
Nine pages: the five-layer architecture, a residency decision model per component, the PDPL / NDMO / SDAIA / NCA control mapping, and the audit-evidence checklist as a printable list.
English · 17 mapped controls · 12 evidence artefacts · 0.1 MB
No form, no email required. The document describes architecture and delivery practice, it is not a certification, accreditation, audit opinion or regulator endorsement, and control identifiers are our reading of published national expectations.
Every control below names the requirement, where it is enforced in the architecture, and the artefact it produces. Filter by regulator to see the subset a specific reviewer cares about.
| Ref | Requirement | In the architecture | Evidence produced |
|---|---|---|---|
| PDPL-01Governed data layer | Personal data is processed for a defined, recorded purpose and no more data is used than that purpose needs. | Every dataset entering the platform is registered with a purpose, an owner and a classification. Pipelines carry column-level allow-lists, so a downstream product cannot silently widen the fields it consumes. | Dataset register export: purpose, owner, classification, fields in use, consuming products. |
| PDPL-02Governed data layer | Data subjects can exercise access, correction and deletion rights within the statutory window. | A subject identifier is resolved once in the master-data layer, then lineage lists every table, extract, index and embedding store holding that person. Deletion runs as one governed job across all of them, including the AI retrieval index. | Worked DSAR run: request, resolved identity, systems touched, records returned or removed, timestamps. |
| PDPL-03Residency & platform | Cross-border transfer of personal data is controlled and justified. | Classification decides placement. Personal and restricted data is processed on in-country infrastructure, including embeddings and inference; anything that would leave Saudi Arabia requires a written exception with a named approver, and the platform blocks egress by default. | Residency decision record per component, plus the egress policy and the exception log. |
| PDPL-04Assurance & operations | Retention periods are defined and enforced, and breaches are detectable and reportable. | Retention is a property of the dataset, not a manual habit: expiry jobs run on schedule and write what they removed. Access to personal data is logged at query level and monitored for unusual volume or off-pattern access. | Retention schedule, deletion job history, access log sample and the alerting rules in force. |
| NDMO-01Governed data layer | Data governance is operating: ownership, stewardship and decision rights are assigned and exercised. | Each domain has a named owner and steward inside the catalogue, and changes to definitions or classifications route through them. Governance is enforced by the platform rather than described in a document. | Catalogue export showing owners, stewards and the change history behind each definition. |
| NDMO-02Governed data layer | Metadata and lineage are maintained so any published figure can be traced to source. | Lineage is captured automatically from the transformation layer, not drawn by hand, and every metric on a dashboard or in an AI answer links back to the model and source columns behind it. | Lineage graph export for a named metric, from dashboard tile back to source table. |
| NDMO-03Governed data layer | Data quality is measured against defined rules, and issues have owners and remediation paths. | Quality rules run as tests in the pipeline. A failing rule can hold a release rather than surface later as a wrong number, and open issues sit in a queue with an owner and an age. | Quality rule inventory, pass/fail history and the open-issue queue with ageing. |
| NDMO-04Governed data layer | Data is classified, and handling rules follow the classification wherever the data goes. | Classification is applied at the column level and inherited by every derived table, extract, index and embedding. A restricted column cannot lose its classification by being copied into a new model. | Classification register with inheritance view: source column to every downstream object. |
| NDMO-05Governed data layer | Master and reference data are managed so entities are not duplicated across systems. | Customer, supplier, employee and asset entities are resolved into golden records with survivorship rules that are readable, versioned and reversible. | Match and survivorship rule set, duplicate-rate trend, and a sample resolved record with its inputs. |
| SDAIA-01Model layer | Each AI use case has a documented purpose, scope and prohibited uses. | Nothing reaches production without a register entry: what it is for, the data it may see, the decisions it may influence, and an explicit list of what it must not do. | Model and agent register entry, signed off by the business owner. |
| SDAIA-02Model layer | Outputs are evaluated for accuracy and fairness before and after deployment. | Every use case has a held-out evaluation set built with the business, including Arabic-language cases where the workflow is Arabic. Scores are re-run on every change, and regression below threshold blocks release. | Evaluation set, scores by release, and the threshold policy that gates deployment. |
| SDAIA-03Agent & application layer | Human oversight exists where AI affects a person or a material decision. | Agents act inside permissions and approval thresholds. Anything above a threshold, or anything touching a customer outcome, stops for a named human with the full context attached. | Approval matrix, escalation paths and an action log showing what was auto-approved versus reviewed. |
| SDAIA-04Agent & application layer | AI answers are explainable and attributable, not unsourced assertions. | Retrieval answers carry citations to the underlying document or governed metric, and the interface shows when the system has insufficient grounding instead of producing a confident guess. | Sample transcripts with citations, plus the abstention rate and how it is monitored. |
| NCA-01Residency & platform | Hosting decisions follow data classification, with tenant separation for sensitive workloads. | The stack deploys as a private tenant, in your cloud subscription, an in-country region, or on-premises, with no shared inference endpoint for restricted data and no vendor-side copy of your corpus. | Deployment topology diagram, tenancy model and the placement decision per component. |
| NCA-02Residency & platform | Identity, access and privilege are centrally controlled and least-privilege by default. | The platform federates to your identity provider. Roles map to data classifications, and the same entitlements are enforced in dashboards, APIs and AI retrieval, an agent cannot read what its caller cannot read. | Role-to-classification matrix, federation configuration and a permission-filtered retrieval test. |
| NCA-03Residency & platform | Data is encrypted in transit and at rest, with controlled key management. | Transport encryption everywhere, encryption at rest on storage and vector indexes, and customer-managed keys where your policy requires the organisation to hold them. | Encryption and key-management statement per store, including the vector index. |
| NCA-04Assurance & operations | Activity is logged, retained and monitored, and third-party components are assured. | Prompts, retrievals, agent actions and data access write to an immutable log shipped to your SIEM. Every third-party component in the stack is listed with its version, licence and where it runs. | Log schema and retention, SIEM integration note, and the component bill of materials. |
Reference identifiers are Bilytica's own. This mapping is our reading of published national expectations and a description of delivery practice, not a certification, audit outcome or regulator endorsement.
These controls are enforced through the TRUST AI Assurance Framework and the SOVEREIGN in-country framework, part of the Bilytica AI Frameworks.
Compliance work is only cheap the second time if the artefacts come from the platform. Tick what you already hold to see the size of the gap.
We work to the national standards described on this page and design engagements so your organisation can evidence them. Where a specific certification or attestation is required in a tender, tell us which one and we will respond directly with our current position rather than a general claim.
Yes, where the classification requires it. Ingestion, storage, modelling, embeddings, retrieval indexes and model inference can each be placed in-country, including against privately hosted or sovereign model endpoints.
By minimising it. We prefer masked, synthetic or subset data for development, and where production personal data is unavoidable it is accessed under your controls, inside your environment, with the access recorded.
A written architecture and residency position for the proposed solution, the evidence artefacts listed above, and named responses to the control questions in your review pack, in Arabic and English.
A scoped readiness assessment gives you a score per domain, the evidence behind it, and a sequenced plan to close the gaps, before an external assessment produces the number for you.
