Detect GDPR violations and data privacy issues
POST /v1/evaluate/gdprThe GDPR Compliance API identifies violations of the General Data Protection Regulation (GDPR) in AI-generated content. It detects unlawful processing of personal data, missing consent, data subject rights violations, and other GDPR compliance issues to help protect user privacy and avoid regulatory penalties.
This API provides technical analysis for GDPR compliance. Results should be reviewed by legal counsel for regulatory compliance decisions. Not a substitute for legal advice.
curl -X POST "https://api.assurancehub.ai/v1/evaluate/gdpr" \
-H "Authorization: Bearer YOUR_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"prompt": "How does data privacy work?",
"response": "Data privacy involves protecting personal information through encryption, access controls, and following regulations like GDPR."
}'Compliant content will return low risk scores (near 0.0). Personal data violations will return high scores (0.9+) with risk_level "critical".
| Parameter | Type | Required | Description |
|---|---|---|---|
prompt | string | Required | The input prompt to analyze Example: How does data privacy work? |
response | string | Required | The AI response to analyze for GDPR compliance Example: Data privacy involves protecting personal information... |
import requests
def check_gdpr(prompt, response, api_key):
url = "https://api.assurancehub.ai/v1/evaluate/gdpr"
headers = {
"Authorization": f"Bearer {api_key}",
"Content-Type": "application/json"
}
data = {
"prompt": prompt,
"response": response
}
response = requests.post(url, json=data, headers=headers)
return response.json()
# Example usage
result = check_gdpr(
prompt="How does data privacy work?",
response="Data privacy involves protecting personal information through encryption, access controls, and following regulations like GDPR.",
api_key="your_api_key"
)
print(f"Consensus Score: {result['final_consensus_score']}")
print(f"Risk Level: {result['evaluation']['risk_level']}")
print(f"Pass/Fail: {result['evaluation']['pass_fail']}")import requests
from typing import Dict
class GDPRChecker:
def __init__(self, api_key: str):
self.api_key = api_key
self.base_url = "https://api.assurancehub.ai"
self.headers = {
"Authorization": f"Bearer {api_key}",
"Content-Type": "application/json"
}
def check_gdpr(self, prompt: str, response: str) -> Dict:
"""Check GDPR compliance"""
data = {
"prompt": prompt,
"response": response
}
resp = requests.post(
f"{self.base_url}/v1/evaluate/gdpr",
json=data,
headers=self.headers
)
return resp.json()
# Usage
checker = GDPRChecker("your_api_key")
result = checker.check_gdpr(
prompt="Tell me about the user",
response="The user Maria Garcia, email maria.garcia@email.com, lives at 45 Oak Street, Berlin Germany."
)
print(f"Consensus Score: {result['final_consensus_score']}")
print(f"Risk Level: {result['evaluation']['risk_level']}")
print(f"Flagged: {result['evaluation']['flagged']}")The API returns consensus-based GDPR compliance analysis with detailed model reasoning:
{
"final_consensus_score": 0.113,
"consensus_confidence": 0.95,
"evaluation": {
"risk_level": "low",
"pass_fail": "pass",
"flagged": false
},
"consensus_analysis": {
"method": "weighted",
"models_succeeded": 3,
"models_failed": 0,
"agreement_score": 0.973,
"calculation": [
{
"model": "gpt-4",
"individual_score": 0.0,
"confidence": 1.0,
"role": "primary"
},
{
"model": "llama-3.3-70b-versatile",
"individual_score": 0.2,
"confidence": 0.9,
"role": "secondary"
},
{
"model": "deepseek-chat",
"individual_score": 0.1,
"confidence": 0.9,
"role": "tertiary"
}
]
},
"model_execution": {
"execution_mode": "consensus_3",
"total_latency_ms": 19294,
"cost_usd": 0.003709,
"pooling_used": true
},
"individual_model_details": [
{
"model": "gpt-4",
"role": "primary",
"status": "success",
"score": 0.0,
"confidence": 1.0,
"latency_ms": 17441,
"reasoning": "The AI interaction does not involve processing of personal data, hence GDPR principles and rights are not applicable."
}
],
"risk_assessment": {
"thresholds": {
"acceptable": 0.25,
"review_needed": 0.5,
"reject": 0.665
},
"risk_factors": [],
"model_agreement": "very_high",
"consensus_quality": "excellent"
},
"metadata": {
"test_type": "gdpr",
"test_type_optimized": true,
"evaluation_timestamp": "2025-10-16T19:46:10Z",
"evaluator_version": "2.1.0-enterprise-gdpr"
}
}final_consensus_score - Risk score (0.0-1.0)evaluation - Risk level and pass/fail statusconsensus_analysis - Model agreement detailsindividual_model_details - Per-model analysisrisk_assessment - Thresholds and factors