The documents this is built on.
This register lists the standards, regulations, and statistical sources used by Fursah. Each entry links to the original publication. When a source informs the code, the relevant file is also listed.
ITU standards and reports
These documents provide the architecture, readiness dimensions, and gap framework used to assess Fursah.
Architectural framework for machine learning in future networks including IMT-2020
Defines the ML pipeline through named nodes: SRC, C, PP, M, P, D and SINK. It also defines the layer that manages them. Clause 8.1 contains the pipeline definition.
The platform's architecture is described in these seven nodes, and each node names the component implementing it and the policy governing it. This is the primary conformance reference.
src/lib/standards.tssrc/components/PipelineDiagram.tsx
AI Ready: Analysis Towards a Standardized Readiness Framework, Report 2.0
Defines 13 dimensions of AI readiness and a three-part framework for identifying gaps.
Fursah assesses its work against all 13 dimensions. It also uses the report's framework to organise policy gaps. Dimension 9 identifies skills gap analysis as a desired output, which is a central function of Fursah.
src/lib/standards.ts
Architectural framework for machine learning sandbox in future networks including IMT-2020
Specifies a sandbox in which an ML model or policy is evaluated before it is allowed to affect a live system.
The governance scenario simulator: a proposed control is stated, checked against the safeguards, and requires a recorded human decision before activation.
src/actions/governance.tssrc/app/admin/governance/page.tsx
Machine learning marketplace integration in future networks including IMT-2020
Covers orchestration, versioning and lifecycle management of ML models across a pipeline.
Every scoring surface stamps its model version onto the audit trail, so a past result can be traced to the ruleset that produced it and that ruleset can be rolled back.
src/lib/intelligence/readiness.tssrc/app/admin/monitoring/page.tsx
Saudi regulatory instruments
These national instruments define what Fursah may collect, who may access it, and the rights available to each person.
Personal Data Protection Law
The national law for lawful processing, data minimisation, individual rights, and transfers outside the Kingdom.
The privacy policy follows this law clause by clause. Fursah does not collect protected characteristics. Consent is specific to each purpose and can be withdrawn. The platform also supports four data-rights requests.
src/lib/policies.tssrc/app/student/data-rights/page.tsxdocs/DPIA.md
AI Ethics Principles
Seven principles for AI in the Kingdom, including fairness, transparency and explainability, accountability, and human oversight.
Every score can be reconstructed from published weights. A named reviewer can also override any automated result. These controls support explainability and human oversight.
src/lib/ai.tssrc/lib/policies.ts
National Data Management and Personal Data Protection Standards
Data classification, quality, retention and governance controls for data held in the Kingdom.
Classification of evidence documents as private by default, the retention posture, and the aggregate-only treatment of institutional reporting.
src/lib/documents.tssrc/lib/cohort.ts
Essential Cybersecurity Controls (ECC) and Cloud Cybersecurity Controls (CCC)
Baseline cybersecurity controls for national organisations and for workloads hosted in cloud environments.
Private object storage with no public bucket access, server-held credentials that never reach the browser, and the hosting-region gap recorded openly in the DPIA rather than left implicit.
src/lib/r2.tssrc/lib/assistant/llm.tsdocs/DPIA.md
Digital Accessibility Standards and Guidelines
Accessibility and interoperability requirements for digital services, referencing WCAG 2.1 Level AA.
The accessibility statement's conformance target, the Arabic runtime layer across every portal, and the keyboard and contrast requirements applied to the interface.
src/lib/i18n/translate.tssrc/components/AccessibleViewControls.tsx
National strategy and statistics
These sources support the problem statement. Each figure on the National Impact page includes a date and source.
Saudi Vision 2030 and the Human Capability Development Program
National strategy, including the commitment to align education with labour-market needs and the published unemployment and participation targets.
The stated alignment on the National Impact page, quoted against specific programme commitments rather than the strategy in general.
src/lib/nationalImpact.ts
Labour Force Survey
The official quarterly labour statistics: unemployment, participation, and employment-to-population ratios.
The labour indicators on the National Impact page, and the evidence for the argument that the Kingdom's constraint is matching quality rather than aggregate participation.
src/lib/nationalImpact.ts
Graduate statistics
Annual graduate totals by degree level across the Kingdom's universities.
The 2023 graduate figure and its degree-level breakdown on the National Impact page.
src/lib/nationalImpact.ts
Global Education Monitoring Report: Saudi Arabia country case study
Tertiary enrolment growth and the shift in the distribution of graduates by field of study.
The field-mix argument: that graduate output grew while concentrating in some fields, which is the distributional problem Fursah addresses.
src/lib/nationalImpact.ts
International frameworks
These voluntary frameworks guide risk assessment and management. Their use does not imply certification.
ISO/IEC 42001:2023: Artificial intelligence management system
Management-system requirements for organisations developing or using AI, including risk and impact assessment.
The structure of the governance surfaces: recorded decisions, model versioning, monitoring with a paused state, and a documented impact assessment.
docs/DPIA.mdsrc/app/admin/monitoring/page.tsx
ISO/IEC 23894:2023: Guidance on risk management for AI
Guidance on identifying, analysing and treating risks specific to AI systems.
The risk register in the DPIA, including the treatment decision recorded against each risk.
docs/DPIA.md
Sustainable Development Goals: targets 4.4, 5.5, 8.5, 8.6 and 10.3
The official target wording against which contribution can be assessed by published indicator.
The SDG alignment on the National Impact page, cited to the numbered target rather than the goal alone.
src/lib/nationalImpact.ts
Fursah governance documents
These project documents explain how Fursah applies and reviews the external sources listed here.
Data Protection Impact Assessment
Assessment of the processing this platform performs: evidence uploads and R2 storage, Workers AI inference, the role-scoped assistant, deterministic scoring and matching, university aggregation and cohort suppression, human verification, and appeals and data rights. Opens with a one-page summary and carries an eight-risk register with residual ratings.
The controls it records are the ones implemented in this repository, and the two verification scripts assert them against live data. Cross-border inference is recorded as blocking for production rather than resolved.
src/lib/cohort.tsscripts/verify-privacy.tsscripts/verify-evidence.ts
Each source is clearly identified
Some national figures reach the public through a news outlet before the official bulletin is available. In these cases, the National Impact page names both the authority and the outlet. It also labels the figure as reported. Arabic documents link to the original publication.
See how these documents shaped the build.
The standards page maps Fursah to ITU-T Y.3172 clause 8.1. It also assesses the platform against 13 AI readiness dimensions and records identified policy gaps.