Policy center
The rules the marketplace runs on, PDPPL, the national AI ethics guidelines, data classification, and model lifecycle duties, with the platform control that enforces each one.
100%
every catalog asset carries a PDPPL tier
96%
4 Limited-risk models pending re-attestation
12/12
all High-risk integrations covered
24 months
immutable, queryable, entity-scoped
Sovereignty by construction
Residency is not a clause in a contract here, governed data, the compute that trains on it, and the inference served from it all sit inside the same national perimeter, and the platform has no path that crosses it.
- No egress from sandboxes
- Weights and data never leave in-country storage
- Derived models inherit the strictest upstream classification
- Every access purpose-bound and audited
Policies in force
Each policy is a live constraint in the publishing and access paths, not a document filed elsewhere.
PDPPL Alignment
Law No. 13 of 2016 · Personal Data Privacy Protection
Every asset carries a PDPPL classification (Public / Government-Restricted / Entity-Private) assigned at publish time and re-validated on each version. Access to restricted assets requires a stated purpose, a bounded duration, and entity-level accountability. Purpose limitation is enforced through entitlements: a grant covers the declared use only, and re-use for a new purpose requires a new request.
- ✓ Classification assigned at publish, audited quarterly
- ✓ Purpose-bound, time-bound entitlements
- ✓ PII scanning on every dataset ingestion
- ✓ DPIA mandatory for High-risk model integrations
National AI Ethics Guidelines
Qatar National AI Strategy · MCIT governance framework
Models are risk-tiered (Minimal / Limited / High) following the national AI governance framework. High-risk models, meaning those affecting individuals' rights, safety, or access to services, require a completed Responsible AI checklist, documented human oversight, and periodic bias re-assessment. The platform's Responsible AI gate blocks publication until these artifacts exist.
- ✓ Risk tiers assigned via structured questionnaire
- ✓ Human-in-the-loop mandatory for High-risk inference
- ✓ Bias assessments re-run on every retrain
- ✓ Explainability method documented per model
Data Classification Policy
National Data Classification Scheme
Datasets follow the national data classification scheme. Government-Restricted data never leaves sovereign infrastructure; Entity-Private data is visible only to the owning entity and named grantees. Derived artifacts (models, embeddings) inherit the strictest classification of their training data unless a documented de-identification review lowers it.
- ✓ Classification inheritance for derived models
- ✓ Sovereign-only storage and compute for restricted tiers
- ✓ Watermarked, logged downloads
- ✓ Annual declassification review cycle
Model Lifecycle & Retirement Policy
TASMU platform standard · MCIT Platform Directorate
A published model is a standing commitment to its consumers, not a one-off deliverable. Owners carry versioning duties for as long as the asset serves traffic: semantic versions with a changelog, a retrain SLA triggered by measured drift, and a fixed deprecation notice period before an endpoint is withdrawn. Retirement is a governed decision with a named migration target, an asset may not simply go dark on the entities that built on it.
- ✓ Semantic versioning with a published changelog per release
- ✓ Retrain SLA: proposal within 10 working days of PSI > 0.25, shipped within 45
- ✓ Deprecation notice: 90 days for Production endpoints, 30 for Staging
- ✓ Consumer migration obligation: a superseding asset and guide before withdrawal
Controls in force
Every policy duty maps to a mechanism and a screen. 7 of 10 run unattended, the rest hold a deliberate human checkpoint.
| Duty | Policy | Enforcing control | Visible in product | Mode |
|---|---|---|---|---|
| Purpose limitation | PDPPL Alignment | Gated access request with a stated purpose and bounded duration | Asset card · Access monitoring | human gate |
| Entitlement expiry | PDPPL Alignment | Renewal nudge at 30 days, endpoint revocation on lapse | Entitlement expiry panel · Automation rule | automated |
| PII detection | Data Classification Policy | PII scan on every ingestion wave, publisher sign-off on any hit | Publishing pipeline · Dataset health score | automated |
| Classification inheritance | Data Classification Policy | Derived asset inherits the strictest upstream tier and gates itself | Model card lineage · Automation rule | automated |
| Sovereign residency | Data Classification Policy | Egress scanner blocks and quarantines off-platform transfers | Access monitoring · flagged events | automated |
| Responsible AI attestation | National AI Ethics Guidelines | RAI checklist gate: publication blocked until artifacts exist | Publishing pipeline stage 3 | human gate |
| Bias re-assessment | National AI Ethics Guidelines | Bias suite re-run on every retrain, results attached to the version | Model card metrics · MLOps pipeline | automated |
| Drift & retrain SLA | Model Lifecycle & Retirement Policy | Weekly PSI job opens a pre-filled retrain proposal for the owner | MLOps drift posture · Automation rule | automated |
| Deprecation & migration | Model Lifecycle & Retirement Policy | Retirement review with a named migration target and consumer notice | Governance · Retirement tab | human gate |
| Accountability record | All policies | Immutable audit entry for every access, publish, and governance decision | Governance · Audit log | automated |
Enforcement is automated where it can be
Policy that depends on someone remembering to apply it is not policy. Classification inheritance, PII scanning, egress blocking, entitlement expiry, and drift-triggered retrain proposals all run unattended on the platform: 7 of 10 controls on this page fire without a human in the loop, and every firing writes to the audit trail. The rules themselves are inspectable and toggleable in the Automation console.
What stays human is deliberate, not residual: approving access to restricted microdata, signing off a Responsible AI attestation, and deciding to retire an asset other entities depend on. Those carry accountability that cannot be delegated to a rule, so the platform routes them to a named officer at the owning entity and to the MCIT governance desk together.