Most companies don't know where their personal data resides. Regulatory fines come precisely from this ignorance. We build an AI-based PII detection system that solves inventory in days, not months. Our NLP pipeline finds direct and indirect identifiers in any source: from file servers to cloud databases. In one project for a fintech startup, we scanned 2 TB of data in 4 days and discovered 340,000 records of unprotected PII, including passport numbers and medical data—all leaking through log files. Our solution saves clients an average of $200,000 annually in compliance costs by reducing leak risks and automating discovery.
Why AI-based PII detection is more accurate than regex
Our AI detection pipeline integrates NER and context classifier to build a comprehensive data map, ensuring compliance with GDPR and 152-FZ and enabling effective data masking. Regex rules only work for rigid patterns—passport series, taxpayer IDs, phone numbers. They miss indirect identifiers (zip code + date of birth identifies 87% of individuals), don't distinguish test data from real data, and yield up to 60% false positives. An AI model analyzes context: the phrase "Example: Ivan Ivanov" won't be marked as PII, but "Client Ivan Ivanov took out a loan" will. An ensemble of NER + regex + context classifier pushes F1 to 0.89–0.93.
How the AI pipeline outperforms traditional methods
Compare our pipeline with popular cloud solutions (AWS Macie, Azure Purview) in a mixed-data scenario: they are often expensive due to per-volume scanning fees and require manual pricing. Our pipeline is vendor-agnostic and can use any GPU instances, reducing cost as you scale. Moreover, cloud services don't always handle Cyrillic PII correctly—we fine-tune on Russian-language corpora. Our AI pipeline scans 1 TB three times faster than cloud APIs while matching accuracy.
| Metric | Our AI pipeline | Cloud APIs |
|---|---|---|
| Speed (scanning 1 TB) | 2-4 hours | 6-12 hours |
| Cyrillic recognition | high | moderate |
| Offline mode | yes | no |
| Domain adaptation | 1-2 days | not possible |
| Cost per TB (approx.) | $2,000 (no per-volume fees) | $5,000–$10,000 |
How we build the NLP pipeline for PII detection
Stage 1: Document ingestion
We support all common formats: TXT, DOCX, XLSX, PDF, CSV, JSON, XML, email (EML/MSG), SQL dumps, object stores (S3, MinIO). For scans and images we add OCR—Tesseract, AWS Textract, or Google Document AI. Scanning is recursive: we traverse folders, mount points, SMB shares.
Stage 2: Named Entity Recognition
We use fine-tuned multilingual BERT/RoBERTa with custom entity types:
Base NER: PER, ORG, LOC, DATE Custom: PASSPORT_RU, INN, SNILS, PHONE_RU, CARD_PAN, EMAIL, IP_ADDR, MEDICAL_CONDITION Additionally, regex patterns for structured data (document numbers, card numbers, taxpayer IDs—with checksums). NER and regex work in an ensemble, cross-validating findings.
Stage 3: Context classification
A separate model determines whether a found entity is real PII or an example/test data. For instance, "John Doe" in a document template is not PII, while "Client John Doe took out a loan" is. The context classifier achieves F1 of 0.89–0.93 depending on domain and language.
Stage 4: Structured data
For databases and CSV, we apply column-level profiling: analyze value distributions, data types, column names. An ML classifier infers column type (e.g., "email", "passport"). Free-text fields (comments, notes) are processed by the same NLP pipeline.
Detection of indirect identifiers
Special attention to indirect identifiers: combination of zip code and date of birth uniquely identifies up to 87% of the US population; department number plus salary identifies up to 63%. Our pipeline detects such linkages even when scattered across different columns or documents.
What is included in implementation?
- Infrastructure audit: identify sources, set access boundaries, prioritize storage. Deliverable: documentation of all data sources and access controls.
- Pipeline deployment: containerized service (Docker, Kubernetes) with GPU support, including full documentation and API access. Integration with SIEM (Splunk, ELK) for alerting.
- Model tuning: adapt NER to your domain (fine-tune on 50-200 labeled documents). Deliverable: tuned model and training report.
- Pilot report: data map for selected segment, risk score, example findings. Deliverable: report with actionable recommendations.
- Team training: workshop on interpreting reports and actions upon leak detection. Deliverable: training materials and recorded session.
- Ongoing support: regular scanning weekly or monthly, with incremental updates, and dedicated support channel.
Our experience and guarantees
We have implemented AI solutions in data privacy for many years. Completed 50+ projects on PII inventory for banks, insurance, and e-commerce. We guarantee detection accuracy >90% on structured data and F1 >0.85 on unstructured data. If metrics deviate, we tune the model at no extra cost. We hold certifications for compliance with GDPR and 152-FZ. Our solutions typically pay for themselves within 3 months by reducing leak risks and compliance costs, saving an average of $200,000 annually.
Comparison: regex vs AI pipeline
| Metric | Regex only | AI pipeline |
|---|---|---|
| Precision | ~60% | >90% |
| Recall | ~50% | >85% |
| Context awareness | no | yes |
| False positives | high | <5% |
| Domain adaptation | manual tweaks | auto-learning |
How long does it take?
Pilot project: 1-2 weeks. Full deployment with integration and training: 4 to 8 weeks. Pricing is customized based on data volume and number of sources. Typical implementation costs range from $15,000 to $50,000. Contact us for a data audit—we'll show which PII is leaking in the shadows. Request a consultation: we'll assess your case in one business day.
Order a pilot scan of your data and get a report within one week. Contact us to schedule a demo.