Best AI Content Moderation Tools in 2026

import boto3

rekognition = boto3.client("rekognition", region_name="us-east-1")

response = rekognition.detect_moderation_labels(
    Image={"S3Object": {"Bucket": "uploads", "Name": "user_photo.jpg"}},
    MinConfidence=70
)
for label in response["ModerationLabels"]:
    print(f"{label['Name']}: {label['Confidence']:.1f}% "
          f"(parent: {label.get('ParentName', 'none')})")

# For video moderation
video_response = rekognition.start_content_moderation(
    Video={"S3Object": {"Bucket": "uploads", "Name": "user_video.mp4"}},
    MinConfidence=60,
    NotificationChannel={"SNSTopicArn": "arn:aws:sns:...", "RoleArn": "arn:aws:iam::..."}
)

import requests

response = requests.post(
    "https://api.thehive.ai/api/v2/task/sync",
    headers={"Authorization": "Token hive_..."},
    json={
        "url": "https://example.com/user_upload.jpg",
        "models": {
            "visual_moderation": {},
            "text_moderation": {"text": "user comment here"}
        }
    }
)
result = response.json()
for cls in result["status"][0]["response"]["output"]:
    if cls["score"] > 0.8:
        print(f"FLAGGED: {cls['class']} ({cls['score']:.2f})")

from google.cloud import vision

client = vision.ImageAnnotatorClient()

image = vision.Image()
image.source.image_uri = "gs://my-bucket/user_upload.jpg"

response = client.safe_search_detection(image=image)
safe = response.safe_search_annotation
likelihood_names = {0: "UNKNOWN", 1: "VERY_UNLIKELY", 2: "UNLIKELY",
                    3: "POSSIBLE", 4: "LIKELY", 5: "VERY_LIKELY"}
print(f"Adult: {likelihood_names[safe.adult]}")
print(f"Violence: {likelihood_names[safe.violence]}")
print(f"Racy: {likelihood_names[safe.racy]}")

from openai import OpenAI

client = OpenAI()

response = client.moderations.create(
    model="omni-moderation-latest",
    input=[
        {"type": "text", "text": "user submitted content here"},
        {"type": "image_url", "image_url": {
            "url": "https://example.com/user_image.jpg"
        }}
    ]
)
result = response.results[0]
if result.flagged:
    for category, flagged in result.categories.model_dump().items():
        if flagged:
            score = getattr(result.category_scores, category)
            print(f"FLAGGED: {category} ({score:.3f})")

import requests

# Submit content to ActiveFence for contextual trust-and-safety analysis
response = requests.post(
    "https://api.activefence.com/v1/moderate",
    headers={"Authorization": "Bearer af_..."},
    json={
        "content": "user message here",
        "context": {
            "conversation_id": "conv_123",
            "user_id": "user_456",
            "platform": "gaming_chat"
        },
        "policies": ["toxicity", "grooming", "violent_extremism"]
    }
)
result = response.json()
for signal in result["signals"]:
    print(f"{signal['type']}: {signal['severity']} ({signal['confidence']:.2f})")

import requests

# Moderate an image with multiple detectors
response = requests.get(
    "https://api.sightengine.com/1.0/check.json",
    params={
        "url": "https://example.com/user_photo.jpg",
        "models": "nudity-2.1,weapon,drug,gore-2.0,text-content",
        "api_user": "...",
        "api_secret": "..."
    }
)
result = response.json()
print(f"Nudity: {result['nudity']['sexual_activity']:.2f}")
print(f"Weapon: {result['weapon']:.2f}")
print(f"Gore: {result['gore']['prob']:.2f}")

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

client = ContentSafetyClient(
    endpoint="https://my-resource.cognitiveservices.azure.com",
    credential=AzureKeyCredential("...")
)

# Analyze text with prompt shield
response = client.analyze_text(AnalyzeTextOptions(
    text="user submitted text here",
    categories=["Hate", "SelfHarm", "Sexual", "Violence"],
    output_type="FourSeverityLevels"
))
for item in response.categories_analysis:
    print(f"{item.category}: severity {item.severity}")

import assemblyai as aai

aai.settings.api_key = "..."

config = aai.TranscriptionConfig(
    content_safety=True,
    sentiment_analysis=True,
    auto_highlights=True
)
transcript = aai.Transcriber().transcribe(
    "https://example.com/podcast_episode.mp3",
    config=config
)
for result in transcript.content_safety.results:
    print(f"[{result.timestamp.start/1000:.1f}s] {result.text[:80]}")
    for label in result.labels:
        print(f"  {label.label}: {label.confidence:.2f} ({label.severity})")