AI DLP Demo

Traditional DLP vs AI DLP vs Hybrid DLP

Compare pattern-based DLP against AI Detection and NextGuard Hybrid mode. Traditional DLP uses Regex & Dictionary. AI DLP understands context and detects evasion. Hybrid combines both for maximum coverage.

🔬 DLP Demo 📧 Email DLP ⚖️ Compare 🛡️ DSPM ☁️ CASB 💻 Endpoint 🧠 IRM

Personal Identifiable Information detection scenarios

Supports: .pdf, .docx, .xlsx, .pptx, .jpg, .txt, .csv, .json, .xml and more (max 5MB per file)

Traditional DLP (Pattern-Based)

Select a sample and click Scan

AI Detection

Select a sample and click Scan

Hybrid DLP

Select a sample and click Scan

How It Works — NextGuard AI DLP Architecture

Aligned with Gartner Magic Quadrant leaders: Palo Alto Networks, Netskope, Forcepoint, Microsoft Purview

Layer 1 — Pattern Engine (EDM + Regex)

✦ Regex patterns: HKID, Credit Card, IBAN, SSN
✦ Dictionary keyword matching (CONFIDENTIAL, SECRET)
✦ EDM: Exact Data Match against uploaded data fingerprints
✦ Sub-millisecond latency, zero AI cost
✗ Cannot detect obfuscated data (Jo&&@hn)
✗ Cannot decode Base64 / reverse text
✗ Cannot understand context or intent

Layer 2 — NextGuard AI Engine (Private SLM)

✦ Private SLM — data never leaves enterprise boundary
✦ Understands context, intent, and semantic meaning
✦ Decodes Base64, reverse text, leetspeak, homoglyphs
✦ OCR: reads PII from scanned images (JPG/PNG/PDF)
✓ Detects Jo&&@hn as "John"
✓ Catches contextual PII with no direct pattern
⚡ 1–3s latency (AI inference)

Layer 3 — Hybrid + UEBA Risk Engine

✦ Pattern engine + AI engine run in parallel
✦ Results merged: union of all findings
✦ UEBA: User & Entity Behaviour Analytics risk scoring
✦ Risk-Adaptive Policy: auto-escalate AUDIT → BLOCK
✓ Keywords ALWAYS enforced (Pattern)
✓ Evasion ALWAYS caught (AI)
✓ Zero blind spots — strictest action applied

🌐 Web Proxy (SWG) — HTTPS Inspection (Netskope / Palo Alto / Zscaler)

A Secure Web Gateway sits inline between users and the internet. All HTTPS traffic is TLS-decrypted using a trusted enterprise CA, inspected for DLP violations, then re-encrypted and forwarded (or blocked). This is how Netskope SSE, Palo Alto Prisma Access, and Zscaler ZIA enforce DLP on web traffic without breaking encryption.

Use the 🌐 Web Proxy panel above to simulate the full 7-step flow: Client → TLS Handshake → SWG Intercept → TLS Decrypt → DLP Inspect → Decision → Destination.

🤖 GenAI Prompt Protection (Forcepoint / Netskope / Microsoft Purview)

Employees pasting sensitive data into ChatGPT, Copilot, or Gemini is one of the fastest-growing DLP risks in 2025–2026. NextGuard SWG intercepts the HTTPS POST to the GenAI API endpoint, inspects the prompt body, and blocks or redacts PII/credentials before they reach the AI model. No data ever leaves the enterprise boundary.

Select the 🤖 GenAI Protection category to try realistic ChatGPT/Copilot/Gemini scenarios with sensitive data.

🔬 EDM — Exact Data Match (Palo Alto / Netskope / Forcepoint)

EDM fingerprints your actual sensitive data (e.g. employee HKID list, customer database) and detects exact matches — not just patterns. A regex can match ANY 8-digit number; EDM only triggers on YOUR specific records.

Upload a CSV above to simulate EDM fingerprinting. The system hashes each record and checks if scanned content contains any of your specific data.

👤 UEBA — User & Entity Behaviour Analytics (Forcepoint / Netskope)

UEBA monitors user behaviour over time and assigns a dynamic risk score. A user who downloads 50 files before resignation is flagged as high-risk — even if each individual file is clean. Risk-Adaptive Protection auto-escalates enforcement based on score.

The UEBA panel above simulates a user risk context. High-risk users have stricter policies applied automatically — matching Forcepoint's Risk-Adaptive Protection and Netskope's UEBA module.