Azure AI Content Safety SDK for Python

Detect harmful user-generated and AI-generated content in applications.

Installation

pip install azure-ai-contentsafety

Environment Variables

CONTENT_SAFETY_ENDPOINT=https://<resource>.cognitiveservices.azure.com CONTENT_SAFETY_KEY=<your-api-key>

Authentication

API Key

from azure.ai.contentsafety import ContentSafetyClient from azure.core.credentials import AzureKeyCredential import os

client = ContentSafetyClient( endpoint=os.environ["CONTENT_SAFETY_ENDPOINT"], credential=AzureKeyCredential(os.environ["CONTENT_SAFETY_KEY"]) )

Entra ID

from azure.ai.contentsafety import ContentSafetyClient from azure.identity import DefaultAzureCredential

client = ContentSafetyClient( endpoint=os.environ["CONTENT_SAFETY_ENDPOINT"], credential=DefaultAzureCredential() )

Analyze Text

from azure.ai.contentsafety import ContentSafetyClient from azure.ai.contentsafety.models import AnalyzeTextOptions, TextCategory from azure.core.credentials import AzureKeyCredential

client = ContentSafetyClient(endpoint, AzureKeyCredential(key))

request = AnalyzeTextOptions(text="Your text content to analyze") response = client.analyze_text(request)

Check each category

for category in [TextCategory.HATE, TextCategory.SELF_HARM, TextCategory.SEXUAL, TextCategory.VIOLENCE]: result = next((r for r in response.categories_analysis if r.category == category), None) if result: print(f"{category}: severity {result.severity}")

Analyze Image

from azure.ai.contentsafety import ContentSafetyClient from azure.ai.contentsafety.models import AnalyzeImageOptions, ImageData from azure.core.credentials import AzureKeyCredential import base64

client = ContentSafetyClient(endpoint, AzureKeyCredential(key))

From file

with open("image.jpg", "rb") as f: image_data = base64.b64encode(f.read()).decode("utf-8")

request = AnalyzeImageOptions( image=ImageData(content=image_data) )

response = client.analyze_image(request)

for result in response.categories_analysis: print(f"{result.category}: severity {result.severity}")

Image from URL

from azure.ai.contentsafety.models import AnalyzeImageOptions, ImageData

request = AnalyzeImageOptions( image=ImageData(blob_url="https://example.com/image.jpg") )

response = client.analyze_image(request)

Text Blocklist Management

Create Blocklist

from azure.ai.contentsafety import BlocklistClient from azure.ai.contentsafety.models import TextBlocklist from azure.core.credentials import AzureKeyCredential

blocklist_client = BlocklistClient(endpoint, AzureKeyCredential(key))

blocklist = TextBlocklist( blocklist_name="my-blocklist", description="Custom terms to block" )

result = blocklist_client.create_or_update_text_blocklist( blocklist_name="my-blocklist", options=blocklist )

Add Block Items

from azure.ai.contentsafety.models import AddOrUpdateTextBlocklistItemsOptions, TextBlocklistItem

items = AddOrUpdateTextBlocklistItemsOptions( blocklist_items=[ TextBlocklistItem(text="blocked-term-1"), TextBlocklistItem(text="blocked-term-2") ] )

result = blocklist_client.add_or_update_blocklist_items( blocklist_name="my-blocklist", options=items )

Analyze with Blocklist

from azure.ai.contentsafety.models import AnalyzeTextOptions

request = AnalyzeTextOptions( text="Text containing blocked-term-1", blocklist_names=["my-blocklist"], halt_on_blocklist_hit=True )

response = client.analyze_text(request)

if response.blocklists_match: for match in response.blocklists_match: print(f"Blocked: {match.blocklist_item_text}")

Severity Levels

Text analysis returns 4 severity levels (0, 2, 4, 6) by default. For 8 levels (0-7):

from azure.ai.contentsafety.models import AnalyzeTextOptions, AnalyzeTextOutputType

request = AnalyzeTextOptions( text="Your text", output_type=AnalyzeTextOutputType.EIGHT_SEVERITY_LEVELS )

Harm Categories

Category Description

Hate

Attacks based on identity (race, religion, gender, etc.)

Sexual

Sexual content, relationships, anatomy

Violence

Physical harm, weapons, injury

SelfHarm

Self-injury, suicide, eating disorders

Severity Scale

Level Text Range Image Range Meaning

0 Safe Safe No harmful content

2 Low Low Mild references

4 Medium Medium Moderate content

6 High High Severe content

Client Types

Client Purpose

ContentSafetyClient

Analyze text and images

BlocklistClient

Manage custom blocklists

Best Practices

• Use blocklists for domain-specific terms

• Set severity thresholds appropriate for your use case

• Handle multiple categories — content can be harmful in multiple ways

• Use halt_on_blocklist_hit for immediate rejection

• Log analysis results for audit and improvement

• Consider 8-severity mode for finer-grained control

• Pre-moderate AI outputs before showing to users