PDPL / SAMA COMPLIANT AI

Use AI on sensitive Saudi data without residency and sovereignty risk.

The infrastructure-first way to keep personal data inside your approved boundary while your teams ship AI faster.

Live PHI masking pipeline

EHR / Clinical Notes

Protecto Vault

LLM Safe

Patient Records

PHI Detected + Masked

HIPAA Ready

Before and after PHI masking

Before Protecto

Patient Maria Chen, DOB 11/08/1976, MRN HX-482991, presented to St. Luke's Medical Center after recurrent chest pain following discharge from Dr. Ravi Patel's clinic.

After Protecto

Patient <PER>vTN 4h1</PER>, DOB <DOB>b1CjImA2/e57ftzYU/yTq6Lgn7</DOB>, MRN <MRN>514-563</MRN>, presented to <ORG>7Fs. U3x xcz QI2</ORG> after recurrent chest pain following discharge from Dr. <PER>JQ0 if7</PER>\'s clinic.

99%

PHI recall

96%

Precision

1/10^x

vs in-house cost

The PDPL / SAMA Compliance Gap

PDPL / SAMA risk shows up inside the AI pipeline

Every RAG system, LLM integration, and agentic AI workflow that touches Saudi personal data is now part of your PDPL / SAMA compliance surface. The challenge is architecture. Personal data moves from core banking systems, claims platforms, call center transcripts, and SaaS logs into vector stores and external model endpoints without transfer assessments, controller-processor controls, or Article 31 records of processing.

SAR 5 million

PDPL Article 36 allows fines up to SAR 5 million for general violations and up to SAR 10 million for repeat violations. PDPL Article 35 adds up to SAR 3 million and two years’ imprisonment for unlawful disclosure of sensitive data intended to harm a data subject. Article 38 allows publication of the final judgment at the violator’s expense, which is material for Saudi banks, insurers, and healthcare brands.

Cross-border LLM routing creates PDPL transfer exposure

Saudi customer and patient records reach external models without transfer assessments or contractual safeguards. That is exactly where PDPL Article 29 pressure begins. Protecto masks personal data before it leaves the Saudi perimeter.

Arabic and mixed-language data defeats generic masking

National IDs, account numbers, and clinical identifiers in Arabic script or transliterated text get missed or over-masked. Protecto achieves 99% recall and 96% precision on multilingual Middle East workflows.

AI agents retrieve far more than the task requires

Copilots pull full records when only a balance or diagnosis code is needed. Protecto applies context-based access control so each workflow sees only the minimum data required.

Most AI stacks cannot reconstruct what they processed

PDPL accountability and SAMA vendor oversight require proof of what moved, where, and who saw it. Protecto creates immutable audit trails so investigations start with evidence.

How it works

Three steps to PDPL-safe AI

Protecto sits between your data sources and your AI models. Run it as an API, proxy, or private deployment so PDPL / SAMA controls live at the infrastructure layer, not inside fragile application logic.

Detect personal data automatically

Protecto scans unstructured text, documents, API payloads, and database fields in real time. It detects Saudi National IDs, IBANs, phone numbers, policy details, health records, and mixed Arabic-English sensitive data before those fields reach your vector store, prompt, or model context.

Mask with Context Preserved

Context-preserving masking replaces personal and sensitive data with semantically coherent tokens. The LLM still receives structured, useful text, so summaries, fraud reviews, claims workflows, and support responses remain accurate while raw personal data stays out of the model layer.

Govern, Audit, and Report

Every interaction with PDPL / SAMA data is logged in an immutable audit trail. Export Article 31 records of processing, transfer risk evidence, and vendor-access logs on demand. That gives compliance, security, and engineering teams one operating record for each AI workflow.

Platform Capabilities

Built for PDPL / SAMA Compliant AI

Protecto implements PDPL / SAMA technical requirements at the data-pipeline level. Not a policy checklist. Engineering controls that security teams can operate, compliance teams can document, and auditors can test.

Sub-sec

Real-time token generation for live pipelines

Billions

Rows handled via bulk API for migrations

50M+

PHI records processed for a single healthcare customer

Saudi personal data detection

Identifies personal and sensitive personal data in unstructured text, documents, chat logs, and API payloads. Covers Arabic names, National IDs, IBANs, mobile numbers, insurance data, health records, and credit data. Detection works on mixed Arabic-English content where generic rules fail.

PDPL Art. 1, Art. 23, Art. 24

Context-Preserving Masking

Replaces PDPL personal data with semantically coherent tokens before data enters LLMs or RAG systems. Unlike redaction, Protecto preserves sentence structure and meaning, so customer support, fraud review, and clinical summarization still work. Privacy and confidentiality controls are enforced before the model sees the text.

PDPL minimization + confidentiality

Context-Based Access Control

Enforces minimum-necessary access at the workflow layer. Each AI agent, analyst, claims team, or support role accesses only the personal data needed for that request. Policy-driven controls reduce oversharing across multi-agent workflows and map cleanly to SAMA vendor-management expectations.

SAMA CSF 3.4 + PDPL accountability

Immutable Audit Logs

Every access to PDPL personal data is logged with timestamp, requesting entity, documented purpose, masking action, and output. Logs are tamper-proof and exportable. Article 31 records of processing and transfer evidence are generated from pipeline activity instead of manual spreadsheets.

PDPL Art. 31

DPA-ready processor controls

Protecto signs a Data Processing Agreement for enterprise engagements, does not train models on customer data, and supports audit and breach workflows required in regulated procurement. That gives Saudi compliance teams a usable controller-processor operating model before the first production workload goes live.

Controller-processor accountability

Saudi residency deployment

Deploy Protecto on-premises, in your private cloud, or in a Saudi-hosted environment. Personal data stays inside the infrastructure boundary you approve. For transfers outside the Kingdom, Protecto supports the data minimization, risk assessment, and contractual-safeguard workflow needed for PDPL Article 29 reviews.

PDPL Art. 29 + transfer rules

Customer Story

A leading Middle East financial institution validated 99% recall in Arabic and English and completed its PoC in four weeks

A top-tier financial institution in the Middle East wanted to use GPT-4o for financial summarization and customer support while staying aligned with data residency requirements and strict internal security controls.

The challenge was not only detection accuracy. Their workflows included Arabic script, transliterations, mixed-language prompts, and real-time RAG and agent actions that generic masking tools could not handle cleanly.

Protecto delivered context-preserving masking, multilingual detection, and a lock-by-default architecture that fit the institution’s enterprise AI pipeline. In head-to-head testing, Protecto beat Calypso AI and finished the PoC in four weeks.

96%

Precision, minimizing over-masking in Arabic and English financial records.

85%

Cosine similarity, preserving semantic accuracy for downstream financial summaries.

99%

Recall on sensitive-data detection across Arabic and English financial workflows.

4 Weeks

From proof-of-concept kickoff to completed validation and deployment plan.

Middle East financial institution

Head-to-head result: Protecto beat Calypso AI on multilingual detection quality and semantic retention for financial summarization workflows.

Arabic + English workflows. GPT-4o. Residency-sensitive data.

Built for Regulated Industries

PDPL / SAMA compliance built for your industry

Every industry processes Saudi personal data differently. Protecto maps its PDPL / SAMA detection, masking, and governance controls to the data types and operational constraints specific to your sector.

Healthcare

Healthcare and Life Sciences

Hospitals, insurers, and digital health teams handle health data, patient identifiers, claims notes, and referral records that need stricter access boundaries than generic AI tools provide. Protecto detects and masks health data in Arabic and English while preserving the context clinicians, care teams, and support agents need.

PDPL Art. 23

Sensitive data

Audit trails

Financial Services

Financial Services and Banking

Banks, fintechs, insurers, and payment teams in Saudi Arabia process customer identity data, transaction narratives, KYC documents, and contact-center transcripts in every AI workflow. Protecto keeps those workloads aligned with PDPL transfer limits, SAMA vendor controls, and Arabic-language detection requirements.

PDPL Art. 24

SAMA CSF 3.4

Transfer controls

Enterprise AI

Enterprise SaaS and AI Companies

SaaS products serving Saudi customers embed personal data in support tickets, product telemetry, call summaries, and admin workflows. Protecto enforces PDPL / SAMA-safe controls across multi-tenant architectures, RAG systems, and agentic AI features without forcing teams to abandon the models they already use.

PDPL Art. 29

DPA ready

Article 31 logs

Why Protecto

Not all data masking tools are built for PDPL / SAMA-compliant AI

Generic redaction and cloud NLP tools were designed for structured data, not PDPL / SAMA AI pipelines. See how Protecto compares on the capabilities that Saudi banking, healthcare, and SaaS teams actually need.

PDPL / SAMA capability	Protecto	AWS Comprehend	Generic Masking / DSPM
Context-preserving masking for LLMs	✓ Yes	✕ No	✕ No
Detection accuracy on Arabic and English unstructured text	✓ 99% recall, 96% precision	! Partial	! Variable, rules-based
PDPL / SAMA-ready agreement and deployment review	✓ Yes	! Partial	! Varies by vendor
Context-based access control for AI agents	✓ Yes	✕ No	✕ No
Immutable audit logs for regulator investigations	✓ Yes	✕ No, CloudTrail is generic	✕ No
On-premises / Saudi data residency deployment	✓ Yes	! Partial	! Varies
RAG and agentic AI pipeline support	✓ Yes	! Limited, document-level only	✕ No

Certifications

Compliance Built In. Not Bolted On.

Protecto holds the certifications and operating controls Saudi procurement, SAMA review, security, and legal teams expect. Documentation is available early in the evaluation process.

SOC 2 Type II

Independently audited security, availability, and confidentiality controls. Annual renewal. Supports the technical-control evidence most Saudi security teams ask for in vendor review.

ISO 27001

Certified information security management system. Covers data handling, access controls, and incident response. Useful evidence for PDPL / SAMA procurement and internal audit review.

PDPL DPA ready

Standard Data Processing Agreement available during procurement. Protecto supports controller-processor accountability, no-training commitments, and operational security terms that Saudi buyers expect in regulated deployments.

Transfer risk assessment support

Protecto supports transfer-risk reviews, deployment architecture review, and processor documentation for teams evaluating whether data remains in Saudi Arabia or moves outside the Kingdom under documented safeguards.

Common Questions

PDPL / SAMA Compliance Questions, Answered

Does Protecto sign a PDPL-ready Data Processing Agreement?

Yes. Protecto signs a Data Processing Agreement for enterprise deployments. The agreement defines processing purpose, security measures, subprocessors, audit rights, and data-handling commitments. It is designed to support PDPL controller-processor accountability and the legal review Saudi banks, healthcare providers, and enterprise SaaS teams run before production approval.

Does Protecto train its models on customer data?

No. Protecto does not train its detection or masking models on customer data. Your Saudi personal data is processed only to perform the masking and governance function, then handled according to your retention policy and deployment model. No cross-purpose reuse. That commitment is captured contractually and can be audited.

How does Protecto handle the PDPL transfer rules published on 1 September 2024?

Protecto addresses the transfer problem at the pipeline level. Personal data is masked before it reaches any external LLM API, so what moves across systems is synthetic tokens, not raw personal data. For teams that must keep data in the Kingdom, Protecto deploys on-premises or in a Saudi-hosted environment. For approved transfers outside the Kingdom, Protecto supports the minimum-data, contractual-safeguard, and risk-assessment workflow required by PDPL Article 29 and the 1 September 2024 transfer rules.

Can Protecto be deployed on-premises for Saudi data residency?

Yes. Protecto supports SaaS, private cloud, and on-premises deployment. For organizations with strict Saudi residency requirements, on-premises or Saudi-hosted private cloud deployment keeps personal data inside the boundary you control. Average production deployment time is about four weeks from proof-of-concept sign-off, with a solutions engineer leading the integration.

How does context-preserving masking differ from standard redaction?

Standard redaction replaces personal data with blanks or crude placeholders, which breaks the semantic structure of the text and lowers LLM accuracy. Protecto replaces personal data with semantically coherent tokens that keep grammar, chronology, and entity relationships intact. The model can still reason over the record, and authorized users can unmask the original values with full audit logging.

How quickly can Protecto integrate into an existing AI pipeline?

Most enterprise integrations take about four weeks from proof-of-concept to production. Protecto provides REST APIs, asynchronous processing, and deployment patterns that sit in front of the existing RAG or agentic pipeline rather than forcing a full rebuild. A dedicated solutions engineer works with your actual Saudi data types, deployment constraints, and compliance requirements from kickoff to go-live.

See Protecto mask Saudi personal data in your pipeline. Live.

30 minutes. A solutions engineer. Your data type. No slides. No sales pitch. We connect to your pipeline and run Protecto on your actual workflow so you can verify PDPL / SAMA detection, masking, and audit quality before any commitment.