Intelligent DLP Data Classification and Protection — Turnkey

Configuring DLP based on regular expressions leads to 30–45% false positives that SOC ignores. Meanwhile, a confidential salary table containing no keywords passes unnoticed. AI-DLP solves this with semantic analysis. We have developed a system that understands context: "Ivan Petrov" in an HR document — confidential, in a press release — not. Under the hood is an ensemble of fine-tuned BERT, ResNet, and specialized NER. Models are trained on millions of documents and take into account a context window of up to 512 tokens, using sentence embeddings for clustering similar documents. This allows detecting even camouflaged data—for example, PII split across multiple fields. Our experience shows that rule-based DLP misses up to 60% of incidents with context-dependent data, according to the Verizon Data Breach Investigations Report. AI-DLP closes these gaps.

How AI-DLP Overcomes the Limitations of the Classic Approach?

Classic DLP yields up to 45% false positives—SOC simply stops responding. It cannot see data in images, nor distinguish legitimate access from a leak. AI-DLP uses contextual NLP: the model understands that the same data in different documents has different secrecy levels. It also solves the problem of bypassing through encoding—OCR and layout analysis detect data even in scanned PDFs. 80% of corporate data is unstructured—texts, documents, correspondence. Traditional methods are powerless here. AI-DLP handles it through multi-class document classification (Public/Internal/Confidential/Restricted/Top Secret) and specialized NER for PII with context awareness.

Why AI-DLP Implementation Pays Off?

Reducing false positives by 62% frees up SOC—analysts spend time only on real incidents. AI-DLP lowers false positive rate by 3–4 times compared to classic DLP, and audit time shrinks from weeks to minutes—direct savings of SOC resources. If the average fine for PII leakage under GDPR Article 32 is considerable, preventing even one incident pays for AI-DLP implementation many times over. Additionally, automatic compliance report generation saves up to 200,000 rubles monthly on compliance officer salaries.

What We Do: Stack and Case Study

Stack: PyTorch, Hugging Face Transformers, fine-tuned BERT for texts, ResNet with OCR for images, LangChain for orchestration, ChromaDB for storing embeddings. Deployed on Kubernetes with Triton Inference Server—latency p99 < 200 ms.

Example from our practice: banking sector, 50 TB of data. Fine-tuned NER on their corpus—F1 raised from 0.88 to 0.95. False positive rate reduced by 62% compared to the old rule-based system. Audit time cut from two weeks to 15 minutes.

Typical mistakes when implementing DLP:

• Insufficient training of models on client-specific data—leads to high false positives.
• Ignoring encrypted traffic—leaks via VPN go unnoticed.
• Lack of policies for new data types (e.g., genomic data)—gaps in protection.

How AI-DLP is Implemented?

1. Analytics: Data discovery—scanning file servers, SharePoint, S3, email, messengers. Data inventory, mapping.
2. Design: Choosing model architecture, classification policies, integration with existing DLP.
3. Implementation: Fine-tuning models on your data, deploying endpoint agents, configuring network DLP.
4. Testing: A/B test with current DLP, debugging false positives.
5. Deployment: Phased rollout, SOC training, documentation.

Timeline: from 4 to 8 weeks depending on volume. We'll assess your project in 2 days.

What You Get in the End?

• A model trained on your data (fine-tuned BERT + NER + ResNet).
• Documentation: data flow map, classification policies, compliance mapping.
• Integration with existing infrastructure (SIEM, CASB, IRM).
• SOC training: how to interpret alerts, adjust policies.
• Technical support for 3 months.

We are ISO 27001 certified, 5+ years on the market, 50+ DLP implementations. We guarantee PII F1 no less than 0.93.

Comparison of Classic DLP vs AI-DLP

Regulatory Compliance (GDPR, 152-FZ, PCI DSS)

Contact us to assess your project. Get a consultation on deploying AI-DLP in your infrastructure. We'll assess in 2 days. Order a pilot implementation now!