Complete privacy protection for enterprise AI chatbots with patent-pending technology
Traditional privacy solutions require weeks of manual configuration and still miss critical PII. SafeKey Lab uses patent-pending auto-configuration to achieve 99.99% accuracy in seconds.
Binary search algorithm automatically finds optimal privacy parameters for any dataset. No manual tuning, no human error, no weeks of setup.
Multi-head attention transformer with DeBERTa + pattern fusion. Understands context and semantics, not just regex patterns.
Distributed edge computing architecture processes massive scale with sub-50ms latency. Built for hyperscale operations.
Unlike simple regex matching, SafeKey Lab understands context and semantics:
Completely removes PII and replaces with placeholders like [REDACTED] or [SSN_REDACTED]
Partially obscures PII while preserving format and some information for context
Replaces PII with reversible tokens that can be detokenized when needed
Adds mathematically calibrated noise to provide formal privacy guarantees
Ensures each person is indistinguishable from at least k-1 others in the dataset
Generates realistic but fake data that preserves statistical properties
First API to defend against jailbreaking, data extraction, and adversarial attacks on LLMs
Comprehensive dashboards and insights into PII exposure, threat detection, and system performance
Global edge network with data residency controls and regional compliance
Flexible API and SDK support for any architecture with custom privacy rules
Complete audit trails and compliance documentation for enterprise requirements
Advanced enterprise features for large-scale deployments and governance
Sub-50ms response time ensures your chatbot stays fast and responsive
Massive scale processing capability for enterprise AI operations
Enterprise-grade reliability with multi-region redundancy
Advanced PII detection with minimal false positives
Automatic scaling to handle traffic spikes and growing workloads
Worldwide edge computing for low latency anywhere
import requests
# Protect user input before sending to LLM
response = requests.post('https://api.safekeylab.com/v1/protect', {
'text': 'My SSN is 123-45-6789 and email is [email protected]',
'methods': ['redaction'],
'detection_level': 'strict'
}, headers={
'Authorization': 'Bearer sk_live_YOUR_KEY'
})
protected_text = response.json()['protected_text']
# "My SSN is [SSN_REDACTED] and email is [EMAIL_REDACTED]"
# Now safe to send to OpenAI/Claude
openai_response = openai.ChatCompletion.create(
model="gpt-4",
messages=[{"role": "user", "content": protected_text}]
)from safekeylab import SafeKeyStream
# Create streaming connection
stream = SafeKeyStream(api_key="sk_live_YOUR_KEY")
# Process messages in real-time
for user_message in chat_stream:
# Protect each message as it arrives
protected = stream.protect(user_message)
if protected.pii_detected > 0:
log_pii_event(protected.metadata)
# Forward to LLM
llm_response = send_to_llm(protected.text)
# Send back to user
send_to_user(llm_response)from safekeylab import SafeKeyClient
client = SafeKeyClient(api_key="sk_live_YOUR_KEY")
# Process multiple messages at once
messages = [
"Patient John Doe, DOB 01/01/1990",
"Credit card 4532-1234-5678-9012",
"Email [email protected]"
]
# Batch protection for efficiency
results = client.protect_batch(messages, {
'method': 'tokenization',
'preserve_format': True
})
for i, result in enumerate(results):
print(f"Message {i}: {result.protected_text}")
print(f"PII detected: {result.pii_count}")from flask import Flask, request
from safekeylab import SafeKeyClient
app = Flask(__name__)
safekeylab = SafeKeyClient(api_key="sk_live_YOUR_KEY")
@app.route('/chat', methods=['POST'])
def handle_chat():
user_message = request.json['message']
# Protect message before LLM
result = safekeylab.protect(user_message, webhook_url="https://your-app.com/pii-detected")
# Process with LLM
llm_response = send_to_llm(result.protected_text)
return {'response': llm_response}
@app.route('/pii-detected', methods=['POST'])
def handle_pii_alert():
# Handle PII detection alerts
pii_event = request.json
send_security_alert(pii_event)
return 'OK'See how SafeKey Lab can prevent PII leaks and compliance violations in your AI applications