FAQ

# Detect custom entities
result = client.tokenize(
    "Order #ORD-2024-XYZ, SKU: PROD-789",
    entities=["order number", "product sku"]
)

# ✅ Easy with policy
result = client.tokenize(text, policy="gdpr_eu")

# ❌ Manual (harder to maintain)
result = client.tokenize(text, entities=["person", "email", ...15 more])

# Only detect high-confidence matches
result = client.tokenize(
    text,
    entities=["person", "email address"],
    score_threshold=0.80  # Higher threshold for fewer false positives
)

# Only detect specific entities
result = client.tokenize(
    text,
    entities=["email address", "phone number"]  # Skip names
)

# Review detected entities before using
for entity in result.detected_entities:
    if entity.score < 0.70:
        # Skip low-confidence detections
        continue

# Skip known safe values (implement client-side)
safe_values = ["John Doe", "support@company.com"]
if original_value not in safe_values:
    # Apply protection

import time

try:
    result = client.tokenize(text)
except APIError as e:
    if e.status_code == 429:
        # Rate limited - wait and retry
        time.sleep(60)
        result = client.tokenize(text)

// Client-side code - NEVER do this
const client = new Blindfold({ apiKey: 'sk-...' });

// Client
fetch('/api/protect', {
  method: 'POST',
  body: JSON.stringify({ text: userInput })
});

// Server (Next.js API route)
import { Blindfold } from '@blindfold/sdk';

export async function POST(req) {
  const client = new Blindfold({
    apiKey: process.env.BLINDFOLD_API_KEY  // Server-side only
  });

  const { text } = await req.json();
  const result = await client.tokenize(text);

  return Response.json(result);
}

# Step 1: Tokenize
protected = client.tokenize("John Doe, john@example.com")

print(protected.text)
# "< person_1>, <email_address_1>"

print(protected.mapping)
# {"<person_1>": "John Doe", "<email_address_1>": "john@example.com"}

# Step 2: Send protected text to AI
ai_response = send_to_ai(protected.text)

# Step 3: Detokenize AI response
original = client.detokenize(
    text=ai_response,
    mapping=protected.mapping
)

print(original.text)
# "Hello John Doe, I received your message at john@example.com"

# AI Chatbot → Use tokenize (reversible)
protected = client.tokenize(user_input)
ai_response = send_to_ai(protected.text)
final = client.detokenize(ai_response, protected.mapping)

# Display to User → Use mask (show last 4)
masked = client.mask("Card: 4532-7562-9102-3456")
# "Card: ***************3456"

# Audit Logs → Use redact (permanent)
logged = client.redact("User SSN: 123-45-6789")
# "User SSN: "

# Analytics → Use hash (consistent IDs)
hashed = client.hash("user@example.com")
# "ID_a3f8b9c2d4e5f6g7" (always same for same input)

# Protect before sending to ANY AI
protected = blindfold.tokenize(user_input, policy="gdpr_eu")

# Send to your AI provider
ai_response = your_ai_provider.chat(protected.text)

# Restore original data
final = blindfold.detokenize(ai_response, protected.mapping)

# HIPAA-compliant processing
result = client.tokenize(
    patient_data,
    policy="hipaa_us"
)