Protect Sensitive Data from AI Exposure

Enterprise-grade proxy that prevents PII, credentials, and confidential data from reaching LLMs — protecting both user inputs and AI outputs in real-time

Schedule Demo → See How It Works
95%
Detection Accuracy
<50ms
P95 Latency
1,713
Requests/Second
100%
Self-Hosted

How Data Hawk Works

Transparent protection in four simple steps

1
📥

User Request

User sends prompt with sensitive data (SSN, API keys, credit cards)

→
2
🛡️

Data Hawk Filter

Real-time detection & redaction using 14+ pattern types in <50ms

→
3
🤖

LLM Processing

Sanitized request sent to OpenAI/Claude/etc. for processing

→
4
✅

Protected Response

Output scanned again, safe response returned to user

Four-Layer Protection System

Comprehensive security for every stage of AI interaction

💬

User Input Protection

Filters user prompts in real-time before reaching any LLM provider

  • Real-time pattern detection (14+ types)
  • SSN, credit cards, emails, API keys
  • 4 redaction modes: MASK, REPLACE, HASH, TOKEN
  • Context-aware confidence scoring
<50ms
Latency
95%
Accuracy
🤖

LLM Output Protection

Scans AI responses for leaked sensitive data before users see them

  • Bidirectional filtering (input + output)
  • Prevents training data leakage
  • Stops hallucinated PII exposure
  • GDPR Article 32 & HIPAA compliant
Both
Directions
Zero
Data Loss
📚

Training Data Protection

Sanitizes documents before LLM training, fine-tuning, or RAG ingestion

  • Batch processing for large datasets
  • Multi-threaded chunk processing
  • Deduplication across files
  • Clean embeddings & vector databases
10K+
Files/sec
100PB+
Capacity
💻

Developer Tool Shield

Protects code and context from IDE tools like Claude Code, Copilot, Cursor

  • Zero code changes (transparent proxy)
  • Filters API keys, DB credentials, secrets
  • Works with Claude Code, GitHub Copilot
  • Productivity without security trade-offs
0
Code Changes
Any
IDE/LLM

Real-World Protection Scenarios

From customer support to developer tools — see Data Hawk in action

🎧

Customer Support AI

❌ WITHOUT Data Hawk
User: "My credit card 4532-1111-2222-3333 was declined"
→ Full card number sent to OpenAI
⚠️ Risk Exposure:
• PCI-DSS Violation
• Potential $500K fine
• Card data in LLM logs
✅ WITH Data Hawk
User: "My credit card 4532-1111-2222-3333 was declined"
→ Redacted: "My credit card [CARD_REDACTED] was declined"
✓ Protected:
• PCI-DSS Compliant
• Full audit trail
• 12ms filtering latency
💰 ROI: Avoided $500K fine + $50K audit costs
💻

Developer AI Tools

❌ WITHOUT Data Hawk
# config.py
DATABASE_URL = "postgres://prod:S3cr3t@db.company.com"
→ Sent to Claude API in context
⚠️ Risk Exposure:
• Production credentials exposed
• Potential security breach
• IP theft risk
✅ WITH Data Hawk
# config.py
DATABASE_URL = "postgres://prod:S3cr3t@db.company.com"
→ Redacted: DATABASE_URL = "[CONNECTION_STRING]"
✓ Protected:
• Credentials filtered
• Developer productivity maintained
• Zero code changes needed
💰 Benefit: 5,000+ devs protected • Zero productivity loss
🔌

Claude Code / Copilot

❌ WITHOUT Data Hawk
Using Claude Code in VS Code:
Code context includes API_KEY="sk-prod-abc123xyz"
→ Entire codebase context sent to Claude API
⚠️ Risk Exposure:
• API keys in conversation logs
• Database credentials exposed
• IP in Claude's training data
✅ WITH Data Hawk
Local proxy intercepts requests:
API_KEY="sk-prod-abc123xyz" → API_KEY="[REDACTED]"
→ Filtered context sent to Claude
✓ Protected:
• Transparent proxy (localhost:9443)
• No IDE configuration needed
• Works with Claude Code & Copilot
💰 Benefit: Enterprise-wide protection • 100% adoption
📚

RAG / Knowledge Base

❌ WITHOUT Data Hawk
Processing company docs for vector DB:
"Employee John Smith, SSN: 123-45-6789, Salary: $150K"
→ Embedded with PII intact
⚠️ Risk Exposure:
• HR data in embeddings
• GDPR Article 17 violation
• Cannot delete from vector DB
✅ WITH Data Hawk
Processing with batch sanitization:
"Employee John Smith, SSN: [REDACTED], Salary: [REDACTED]"
→ Clean embeddings created
✓ Protected:
• PII-free knowledge base
• GDPR compliant
• 10,000+ docs/sec processing
💰 ROI: GDPR compliance + Safe AI training

Flexible Integration Options

Deploy in minutes with zero code changes

📦 Native SDK Integration

Use our purpose-built SDKs for Python, Java, or Node.js with additional features like session tracking and custom rules. 

# Install the Data Hawk SDK
pip install datahawk-shield

# Import and configure
from datahawk import ShieldedOpenAI

client = ShieldedOpenAI(
    shield_url="https://api.datahawk.io",
    api_key="your-openai-key",
    redaction_mode="MASK"  # MASK, REPLACE, HASH, TOKEN
)

# Use exactly like OpenAI client
response = client.chat.completions.create(
    model="gpt-4",
    messages=[{
        "role": "user",
        "content": "My credit card is 4532-1111-2222-3333"
    }]
)
# Automatically redacted to: "My credit card is [CARD_REDACTED]"
Python, Java, Node.js SDKs
Session correlation IDs
Custom redaction modes
Type-safe interfaces

🌐 Organization-Wide Gateway

Deploy as an API Gateway for centralized protection across all teams and applications. Perfect for enterprise-wide enforcement. 

# NGINX Configuration
upstream datahawk_shield {
    server shield-1.datahawk.io:8090;
    server shield-2.datahawk.io:8090;
    server shield-3.datahawk.io:8090;
}

# Route all LLM traffic through Data Hawk
location /v1/ {
    proxy_pass http://datahawk_shield;
    proxy_set_header X-Real-IP $remote_addr;
    proxy_set_header X-Correlation-ID $request_id;
}

# Your apps continue using standard endpoints
# https://api.yourcompany.com/v1/chat/completions
# ↓ Automatically routed through Data Hawk Shield
# ↓ Then forwarded to OpenAI/Claude/etc.
Centralized policy control
Load balanced (3+ nodes)
Zero app changes needed
Team-wide compliance

🔌 Zero Code Changes

The simplest integration — just point your LLM endpoint to Data Hawk. Works with any OpenAI-compatible SDK. 

# Just change your environment variable
OPENAI_BASE_URL="https://shield.datahawk.io/v1"
OPENAI_API_KEY="your-openai-key"

# Your existing code works unchanged
import openai
response = openai.ChatCompletion.create(
    model="gpt-4",
    messages=[{"role": "user", "content": "My SSN is 123-45-6789"}]
)
# Data Hawk automatically filters before sending to OpenAI
Works with standard OpenAI SDK
No code modifications
Drop-in replacement
Supports all LLM providers

Why Choose Data Hawk?

Built for enterprise security and performance

Feature Data Hawk Cloud-Based DLP
Deployment ✓ 100% Self-Hosted ⚠ Cloud SaaS
Data Sovereignty ✓ Complete Control ⚠ Data leaves network
LLM Provider Support ✓ Any Provider ⚠ Limited integrations
Latency (P95) ✓ <50ms ⚠ 100-500ms
Bidirectional Filtering ✓ Input + Output ✗ Input only
Reversible Redaction ✓ Tokenization ✗ Permanent
Pricing Model ✓ Predictable licensing • No per-call fees ⚠ Usage-based charges
Air-Gapped Deployment ✓ Supported ✗ Not possible
Custom Patterns ✓ Full control ⚠ Limited customization
Code Changes Required ✓ Zero ⚠ Varies by provider

Deploy LLM Shield in 30 Minutes

Protect your organization's sensitive data from AI exposure — self-hosted, secure, and compliant

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