Example request:
import requests
import json
import os
url = "https://api.enkryptai.com/guardrails/detect"
payload = json.dumps({
"text": "All programmers are men and women can't code.",
"detectors": {
"bias": {
"enabled": True
}
}
})
headers = {
'Content-Type': 'application/json',
'apikey': os.getenv('ENKRYPTAI_API_KEY')
}
response = requests.request("POST", url, headers=headers, data=payload)
formatted_response = json.dumps(json.loads(response.text), indent=4)
print(formatted_response)
import os
from enkryptai_sdk import *
from dotenv import load_dotenv
load_dotenv()
ENKRYPT_API_KEY = os.getenv("ENKRYPTAI_API_KEY")
ENKRYPT_BASE_URL = os.getenv("ENKRYPTAI_BASE_URL") or "https://api.enkryptai.com"
guardrails_client = GuardrailsClient(api_key=ENKRYPT_API_KEY, base_url=ENKRYPT_BASE_URL)
guardrails_config = GuardrailsConfig.bias()
prompt = "All programmers are men and women can't code."
detect_response = guardrails_client.detect(text=prompt, guardrails_config=guardrails_config)
print(detect_response)
# Print as a dictionary
print(detect_response.to_dict())
Example response:
JSON
{
"summary": {
"bias": 1
},
"details": {
"bias": {
"bias_detected": true,
"debiased_text": "People of all genders can be programmers, with skills in coding not limited to any specific gender.",
"compliance_mapping": {
"owasp_llm_2025": [
"LLM09:2025 Misinformation",
"LLM04:2025 Data and Model Poisoning"
],
"mitre_atlas": [],
"nist_ai_rmf": [
"MEASURE 2.1-2.5 (AI system bias evaluation & management)"
],
"eu_ai_act": [
"Article 10(2)(f), Article 15(3) (Bias detection, correction & mitigation)"
],
"iso_iec_standards": [
"ISO/IEC TR 24027: 5.2 (Bias in AI systems)"
]
}
}
}
}