Train on datayou cannot share.Protect what matters.
Synthetic data, federated learning, anonymisation and privacy-preserving computation, so models learn from sensitive data without exposing it.
Why privacy-preserving AI matters now
Personal data, security footage and commercial records are exactly what good models need, and exactly what PDPL and partners will not let you copy around.
PDPL is in force
Personal data needs purpose limits, minimisation and residency.
Data is scarce
Rare events, failures, incidents, fraud, have too few real examples.
Partners will not share
Agencies and companies want insight without handing over raw data.
Faces and plates
Video analytics must protect identities by design.
The answer: synthetic, federated and anonymised approaches that keep data where it belongs.
Sources: SDAIA 2026; MIM National Industrial Strategy; Future Factories Program; GACA 2025; Saudi Aviation Strategy; Matarat Holding
Privacy-preserving AI, by design
We generate synthetic data for rare cases, train across organisations without moving data, and anonymise what must be shared, with PDPL assessments built in.
Synthetic data
Statistically faithful tabular, image and sensor data for training and testing.
Federated learning
Models trained across sites or agencies; only updates move, never records.
Anonymisation & masking
Faces, plates, identifiers and text PII removed or pseudonymised.
Building blocks of a privacy-preserving programme
Six components, assessment and governance apply to every dataset.
Privacy impact assessment
Purpose, lawful basis, minimisation and residency documented per use case.
Synthetic generators
Tabular, time-series and image synthesis with fidelity and privacy metrics.
Federated training
Cross-site and cross-agency learning with secure aggregation.
Anonymisation pipelines
Masking of faces, plates and identifiers in video, images and text.
Secure computation
Encrypted and confidential-computing options for joint analytics.
Privacy monitoring
Re-identification risk and leakage tests before release.
How it works: from sensitive data to a shareable model
Six stages that keep raw data with its owner.
Assess
Purpose, basis, risk
Protect
Mask and pseudonymise
Synthesise
Generate faithful data
Federate
Train without moving data
Test
Leakage and fidelity checks
Release
Model or dataset, documented
Where privacy-preserving AI earns its keep
Eight use cases where the data is sensitive and the model is still needed.
Privacy-safe video AI
Crowd and traffic analytics with faces and plates masked at source.
Cross-agency crowd models
Agencies improve one model without pooling pilgrim data.
Rare-event training
Synthetic failures, incidents and fraud cases to balance datasets.
Health analytics
Synthetic patient data for research and system testing.
Fraud & payments
Models trained across institutions without sharing transactions.
Safe test environments
Synthetic production data for development and vendors.
Mobility insights
Aggregated, anonymised movement data for planning.
Data sharing with partners
Release datasets with measured re-identification risk.
Where it creates value across Saudi sectors
Wherever data is sensitive, shared or scarce.
Industry & factories
Synthetic failure data; vendor testing without real records.
Airports
Passenger analytics with identities protected.
Hajj & crowd mobility
Cross-agency crowd models without pooling personal data.
Fleet & logistics
Driver and shipment models shared across partners.
Utilities & energy
Customer and meter analytics under PDPL.
Retail & FMCG
Shopper analytics with anonymised footfall.
Start with one model blocked by a privacy question, unblock it the right way.
A programme with privacy built in
Illustrative sequence from a multi-agency crowd-safety initiative.
Each agency’s data, purpose and lawful basis documented; raw sharing ruled out.
A density-forecast model trains at each agency; only encrypted updates are aggregated.
Rare incident patterns are synthesised to improve detection without real cases.
Leakage tests pass; the shared model and documentation are released to all parties.
Insight is shared; records never are, and the audit shows it.
Illustrative scenario, not a case study.
Where this technology goes to work
Let us unblock the modelprivacy is holding back.Know now.
A 45-minute walkthrough of your operation, and the one workflow worth proving first.