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AIGuard Integration Guide

AIGuard's Blindfold service anonymizes sensitive data (PII) before it's sent to LLMs. Ensure your customer data, API keys, and proprietary information never leave your systems unprotected.

How It Works

AIGuard intercepts your outgoing prompt, replaces PII with tokens, and safely restores the PII when the LLM responds.

┌─────────────────────────────────────────────────────────────────┐ │ YOUR APPLICATION │ │ │ │ User sends prompt with PII: │ │ "My email is john@example.com and my phone is 555-123-4567" │ │ │ └─────────────────────────────────────────────────────────────────┘ ┌─────────────────────────────────────────────────────────────────┐ │ AIGUARD BLINDFOLD PROXY │ │ │ │ Step 1: DETECT PII │ │ ✅ Email: john@example.com │ │ ✅ Phone: 555-123-4567 │ │ │ │ Step 2: MASK PII │ │ ✅ [EMAIL_001] instead of john@example.com │ │ ✅ [PHONE_001] instead of 555-123-4567 │ │ │ │ Step 3: FORWARD TO LLM │ │ "My email is [EMAIL_001] and my phone is [PHONE_001]" │ │ │ │ Step 4: RESTORE PII │ │ ✅ [EMAIL_001] → john@example.com │ │ ✅ [PHONE_001] → 555-123-4567 │ │ │ └─────────────────────────────────────────────────────────────────┘ ┌─────────────────────────────────────────────────────────────────┐ │ YOUR APPLICATION │ │ │ │ Response with PII restored: │ │ "Your email is john@example.com and your phone is 555-123-4567" │ │ │ └─────────────────────────────────────────────────────────────────┘

Prerequisites

Before integrating AIGuard, you need to generate your API key.

Requirement Description
AIGuard Account Sign up at app.aiguard.solutions
API Key Generate from your dashboard (Settings → API Keys)
LLM Provider OpenAI, Anthropic, or Google Gemini API key

Supported PII Types

AIGuard detects and masks 16 types of sensitive data:

PII Type Example Masked As
Emailjohn@example.com[EMAIL_001]
Phone (US)555-123-4567[PHONE_US_001]
Phone (Intl)+33 6 12 34 56 78[PHONE_INTERNATIONAL_001]
SSN123-45-6789[SSN_001]
Credit Card4111-1111-1111-1111[CREDIT_CARD_001]
IP Address (v4)192.168.1.1[IP_V4_001]
API Keyapi_key=sk-abc123...[API_KEY_001]
AWS KeyAKIAIOSFODNN7EXAMPLE[AWS_KEY_001]
Private Key-----BEGIN RSA...[PRIVATE_KEY_001]
NameJohn Doe[NAME_001]

Code Examples

Replace your direct LLM calls with AIGuard calls by updating your base URL.

Python / Direct HTTP

python
import requests import os AIGUARD_API_KEY = os.getenv("AIGUARD_API_KEY") def anonymize_prompt(prompt: str, provider: str = "gemini") -> str: """Send prompt through AIGuard for PII anonymization""" response = requests.post( "https://api.aiguard.solutions/api/v1/blindfold/anonymize", headers={ "X-API-Key": AIGUARD_API_KEY, "Content-Type": "application/json" }, json={ "prompt": prompt, "provider": provider, "masking_mode": "full" } ) if response.status_code == 200: data = response.json() return data["response"] else: raise Exception(f"AIGuard error: {response.text}")

Node.js / Express

javascript
const axios = require('axios'); const AIGUARD_API_KEY = process.env.AIGUARD_API_KEY; const AIGUARD_BASE_URL = 'https://api.aiguard.solutions/api'; async function anonymizePrompt(prompt, provider = 'gemini') { try { const response = await axios.post( `${AIGUARD_BASE_URL}/v1/blindfold/anonymize`, { prompt: prompt, provider: provider, masking_mode: 'full' }, { headers: { 'X-API-Key': AIGUARD_API_KEY, 'Content-Type': 'application/json' } } ); return response.data.response; } catch (error) { console.error('AIGuard error:', error.message); throw error; } }

cURL

bash
curl -X POST https://api.aiguard.solutions/api/v1/blindfold/anonymize \ -H "X-API-Key: ag_your_api_key_here" \ -H "Content-Type: application/json" \ -d '{ "prompt": "Customer John Doe (john@example.com) requested a refund.", "provider": "gemini", "masking_mode": "full" }'

Troubleshooting

"Invalid API Key"

  • Verify your API key is correct.
  • Check if the key is active in your dashboard.
  • Ensure you're using the right header (X-API-Key).

"Monthly Limit Exceeded"

  • Check your plan limits in the dashboard.
  • Upgrade to a higher tier to increase your quota limit.

"Tokens Not Preserved"

  • Check the LLM response for token mutations.
  • Use a lower temperature (0.1 recommended) in your LLM request.