Imagine: in a year, you face a Roskomnadzor inspection or GDPR audit. Are you sure you know where all your users' personal data is stored? That it hasn't leaked into Nginx logs or Redis backups? Without automation, compliance is a nightmare: hundreds of person-hours manually hunting for PII, responding to subject requests, proving to regulators that you have everything under control. We build AI systems that do this continuously and without errors.
Our team has over seven years of experience in this area and has completed more than 30 projects for medical, banking, and fintech companies. Certified specialists ensure compliance with GDPR and 152-FZ. The AI system handles routine monitoring and detection, leaving only truly uncertain decisions to humans. Get a consultation to assess your workload.
Key tasks we automate
PII Discovery. Automatic detection of personal data in databases, file systems, cloud storage. NLP + regex + Named Entity Recognition for Russian: names, addresses, INN, SNILS, phone numbers, passport data, medical data.
Tools: Microsoft Presidio (with Russian recognizers), AWS Macie, custom NER models based on RuBERT for specific formats. Periodic scanning + real-time monitoring of new data.
Consent management. Tracking consents: who gave consent for what, when, and which policy version. Upon consent withdrawal, automatic cascading to all downstream systems (not just deletion from one table).
Data subject rights automation. Requests for access (SAR), deletion, rectification, portability. The AI agent finds all subject data across all systems, generates a report or performs deletion. For GDPR: 30-day response deadline — impossible without automation at scale.
Data Lineage. Where personal data came from, where it is transmitted, where it is stored. Automatic data map construction by analyzing API traffic, SQL queries, ETL pipelines.
How PII Discovery finds data in unstructured sources?
The main technical challenge is not missing personal data in ticket comments, application logs, email archives, or document screenshots.
from presidio_analyzer import AnalyzerEngine
from presidio_analyzer.nlp_engine import NlpEngineProvider
configuration = {
"nlp_engine_name": "spacy",
"models": [{"lang_code": "ru", "model_name": "ru_core_news_lg"}]
}
provider = NlpEngineProvider(nlp_configuration=configuration)
analyzer = AnalyzerEngine(nlp_engine=provider.create_engine())
from presidio_analyzer import PatternRecognizer, Pattern
inn_recognizer = PatternRecognizer(
supported_entity="RU_INN",
patterns=[Pattern("INN_10", r"\b\d{10}\b", 0.6),
Pattern("INN_12", r"\b\d{12}\b", 0.6)]
)
analyzer.registry.add_recognizer(inn_recognizer)
Problem: false positives on numbers (order number of 10 digits ≠ INN). Contextual rules reduce FPR: INN without surrounding context ("INN:", "Identification number") — lower confidence. Final recognition accuracy: 96% with false positive rate below 2%.
Why automating Right to Erasure is critical?
This is the hardest part technically. "Delete all data of user X" means:
- Find all mentions in PostgreSQL (50+ tables), MongoDB, Redis
- Find in backups (and delete or mark there too)
- Find in Elasticsearch logs
- Forward the request to all external integrations (CRM, email provider, analytics)
- Confirm deletion and create an audit record
The AI agent with access to the data catalog automatically traverses all sources, executes deletion, and creates a compliance document. Execution time: 2–15 minutes vs. days of manual work. Manual process takes 20–50 times longer and has a 30% error rate. Under GDPR Article 17, you are obliged to delete data upon request — without automation, it is practically impossible at scale.
Comparison: manual compliance vs AI automation
| Metric | Manual Process | AI Automation |
|---|---|---|
| Time per SAR request | 1–2 days | 4 minutes |
| PII miss rate | 30% | <2% |
| Monitoring frequency | Quarterly | Daily + real-time |
| Staff cost | 2 FTE | Not required |
Practical case: medical service
A client of ours — a medical service with 500,000 users, health data (special category under both regulatory regimes). 15–20 SAR requests per month plus a Roskomnadzor inspection.
Before automation: 2 specialists spent 1–2 days on each SAR. During the inspection, personal data was found in Nginx logs (email addresses in URL query parameters) — a compliance violation that had existed unnoticed for 3 years.
After implementing our system:
- PII Discovery found 7 additional personal data sources not in the registry
- SAR request processed in 4 minutes automatically; a human only reviews the result
- Logs are automatically masked at ingestion: email →
e***@***.com - Consent versioning: when the policy is updated, a list of users requiring re-consent is automatically generated
The client saved significantly on the salaries of two specialists. The Roskomnadzor inspection passed without orders. Order an audit of your infrastructure — we will show where PII is hiding.
What is included in the work
| Stage | Result |
|---|---|
| Analysis | Data inventory, flow map, gap report |
| Design | AI system architecture, metric agreement |
| Implementation | PII Discovery, consent management, SAR automation |
| Testing | Penetration test, load test, validation |
| Deployment | Integration, CI/CD, documentation, staff training |
How we ensure regulatory compliance?
The system undergoes regular penetration tests, uses a set of detectors covering all PII categories under 152-FZ and GDPR. We implement continuous monitoring — if a new data source appears without PII scanning, the system sends an alert. All consent changes are recorded in a blockchain-like audit trail. Contact us to evaluate your project — we will prepare an individual proposal.
Technical debt of compliance
A typical problem: legacy systems without proper data mapping. For these, AI-powered discovery works externally: analyzing traffic between services, SQL query logs, API response bodies — building a data map without source code access. Timelines: 6–10 weeks for basic PII Discovery and SAR automation, 4–6 months for a full compliance framework with data lineage and continuous monitoring.