AI Automated PII and Personal Data Detection System
We build AI-powered PII detection systems that scan your entire data infrastructure — file servers, databases, email, cloud storage, and collaboration tools — and produce a complete data map within days. Our NLP pipeline combines fine-tuned NER models with regex-based validators and context classifiers to deliver detection accuracy of F1 0.89–0.93. We deliver turnkey PII scanning solutions that include infrastructure integration, automated reporting, and GDPR/152-FZ compliance mapping. Contact us to request a demo on a sample of your data.
Most companies do not know where their personal data is stored. Regulatory fines arrive precisely because of this gap in visibility. An AI PII detection system resolves the inventory problem in days, not months — without manual document review. The system produces a structured data map across all storage types: file servers, databases, email archives, cloud buckets, and collaboration platforms.
What PII Personal Data the System Detects
Direct identifiers (PII):
- Имена, фамилии, отчества
- Даты рождения
- Паспортные данные, серии и номера документов
- ИНН, СНИЛС, номера полисов ОМС
- Адреса проживания
- Номера телефонов, email-адреса
- Банковские реквизиты (PAN, IBAN, BIC)
- IP-адреса (при наличии привязки к личности)
Indirect identifiers (quasi-identifiers): postal code + date of birth + gender identifies 87% of Americans (Sweeney research). Occupation + employer + residential area also forms a quasi-identifier combination.
Special categories (sensitive data):
- Медицинские диагнозы, рецепты, анализы
- Биометрические данные (фото для распознавания, fingerprints)
- Данные о судимостях
- Политические взгляды, религиозные убеждения
- Сексуальная ориентация
NLP Pipeline Architecture for PII Detection
Stage 1 — Document ingestion: supported formats include TXT, DOCX, XLSX, PDF, CSV, JSON, XML, email (EML/MSG), databases (SQL), and object storage (S3, MinIO). Images and scanned documents use OCR via Tesseract, AWS Textract, or Google Document AI.
Stage 2 — Named Entity Recognition: fine-tuned BERT/RoBERTa for multilingual NER with custom entity types:
Standard NER: PER, ORG, LOC, DATE
Custom: PASSPORT, TAX_ID, SSN, PHONE, CARD_PAN, EMAIL, IP_ADDR, MEDICAL_CONDITION
Regex patterns handle structured data formats (document numbers, card numbers — these have checksum verification). NER and regex work in ensemble for higher precision.
Stage 3 — Context classification: the context model determines whether a detected entity is real personal data or an example/test entry. "Example: John Smith" → not PII. "Client John Smith applied for a loan" → PII. "John Doe" in a document template → not PII. Context classifier F1: 0.89–0.93 depending on domain.
Stage 4 — Structured data scanning: for databases and CSV files, column-level profiling analyzes value distribution statistics, uses an ML column-type classifier based on column name and sample values, and detects PII in free-text fields (comments, notes).
Infrastructure Scanning Coverage
File servers: SMB, NFS — recursive scanning
Email: Microsoft 365 API, Exchange EWS, IMAP
Cloud: AWS S3, Azure Blob, GCP Storage
Databases: PostgreSQL, MySQL, MSSQL, Oracle, MongoDB
CRM/ERP: Salesforce, SAP, 1C (via API)
Collaboration: Confluence, SharePoint, Notion
Incremental scanning: the first run covers the full corpus. Subsequent scans process only new or modified files via change detection — keeping scan time short for ongoing compliance.
Reports and Remediation
The output report includes: a data map showing where each PII type is stored and in what volume, a risk score per storage location (sensitivity × accessibility × retention period), masked examples of found data, mapping to GDPR articles and applicable regulations, and remediation recommendations (delete, anonymize, or move to protected storage).
Performance: 500 GB of structured data or 200K documents processed per business day at standard configuration.
Regular scanning (weekly or monthly) keeps the data map current and simplifies responses to Data Subject Access Requests (DSAR). We include scheduled scan configuration and automated report delivery as part of our turnkey solution.
Implementation and Deployment Timeline
We configure the scanning infrastructure for your environment: deploy the NLP pipeline to your on-premises servers or private cloud, set up connector integrations for all target storage systems, run a baseline discovery scan, and deliver the first full data map with compliance annotations.
Implementation timeline: initial setup and connector configuration — 1–2 weeks. Baseline scan and first data map — 1–2 days per 500 GB corpus. Ongoing automated scanning enabled immediately. Full GDPR/152-FZ compliance report — within 30 days of deployment.