How do I set up a content moderation API for toxicity filtering with audit logs and retention?

Here’s a practical way to set up a content moderation API for toxicity filtering with audit logs and retention.

1) Define the moderation flow

A typical request flow:

1. Client sends content to your API
2. API runs toxicity checks
3. API returns:
   • allowed: true/false
   • severity / labels
   • optional redacted text
4. API writes an audit log entry
5. Logs are retained according to policy

Example response:

{
  "allowed": false,
  "labels": ["toxicity", "harassment"],
  "score": 0.92,
  "action": "block",
  "request_id": "req_12345"
}

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2) Choose a moderation engine

You can use:

• Managed APIs

  • OpenAI moderation
  • Google Perspective API
  • AWS Comprehend / Bedrock guardrails
  • Azure AI Content Safety

• Self-hosted models

  • Detoxify
  • Hugging Face classifiers
  • Custom fine-tuned text classifier

If you want fastest setup, use a managed moderation API.

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3) Create the API endpoint

A simple design:

POST /moderate

Input:

{
  "text": "some user content",
  "user_id": "u_123",
  "source": "chat"
}

Output:

{
  "request_id": "req_12345",
  "allowed": true,
  "labels": [],
  "score": 0.04
}

Backend logic

• Validate input
• Call moderation model/API
• Apply thresholds
• Store audit event
• Return decision

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4) Add audit logs

Your audit log should record:

• request_id
• timestamp
• user_id or account id
• content hash or content reference
• moderation result
• labels and scores
• model/version used
• action taken
• reviewer override info if any

Example audit record:

{
  "request_id": "req_12345",
  "timestamp": "2026-07-19T12:00:00Z",
  "user_id": "u_123",
  "content_hash": "sha256:abc...",
  "labels": ["toxicity"],
  "score": 0.92,
  "decision": "blocked",
  "model_version": "moderation-v1",
  "retention_until": "2027-07-19T12:00:00Z"
}

Important logging practice

• Prefer storing a hash or content reference rather than raw text
• If you must store raw text, encrypt it and restrict access tightly
• Separate operational logs from compliance/audit logs

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5) Implement retention policies

Retention depends on compliance and business needs.

Common approach:

• Raw content: 7–30 days
• Audit metadata: 1–7 years, if needed for compliance
• Security logs: 90–180 days
• Deleted content: keep tombstone record only

Retention enforcement

• Add retention_until to every record
• Use scheduled deletion jobs
• Use lifecycle policies in storage:
  • S3 lifecycle rules
  • PostgreSQL partition expiry
  • log management retention settings

Example rule:

• Delete raw moderation payloads after 30 days
• Keep audit metadata for 365 days
• Keep aggregate metrics longer

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6) Secure the system

Minimum controls:

• TLS everywhere
• Authenticated API access
• Role-based access control
• Encryption at rest
• Secrets management
• Rate limiting
• Tamper-evident audit storage
• Time synchronization for logs

If audit integrity matters, consider:

• append-only storage
• WORM/immutable buckets
• digital signatures or hash chaining

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7) Example architecture

API Gateway → Moderation Service → Moderation Model/API → Audit Log Store → Retention Job / Lifecycle Policy

Storage split:

• Postgres for audit metadata
• S3 / Blob Storage for raw payload archives
• SIEM / log platform for operational monitoring

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8) Example implementation sketch

Pseudo-code:

def moderate_request(text, user_id):
    request_id = generate_id()
    score, labels = moderation_model.predict(text)

    allowed = score < 0.8
    decision = "allowed" if allowed else "blocked"

    audit_event = {
        "request_id": request_id,
        "user_id": user_id,
        "content_hash": sha256(text),
        "score": score,
        "labels": labels,
        "decision": decision,
        "model_version": MODEL_VERSION,
        "retention_until": now_plus_days(365)
    }
    write_audit_log(audit_event)

    return {
        "request_id": request_id,
        "allowed": allowed,
        "labels": labels,
        "score": score
    }

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9) Add human review for edge cases

For borderline cases:

• route to manual review
• store reviewer decisions
• log override reason
• use reviewer actions to improve thresholds

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10) Test and tune thresholds

Evaluate:

• false positives
• false negatives
• performance by language/domain
• bias across user groups

Use a test set and tune thresholds per content type, e.g.:

• chat: stricter
• forum posts: moderate
• support tickets: contextual review

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11) Compliance considerations

Depending on your use case, you may need to align with:

• GDPR / data minimization
• CCPA
• SOC 2
• ISO 27001
• industry-specific retention requirements

Ask:

• Do you need to store raw text at all?
• Is a content hash enough for audit?
• Who can access logs?
• How long must records be kept?

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If you want, I can also give you:

1. a FastAPI example implementation,
2. a database schema for audit logs and retention, or
3. an AWS/GCP/Azure reference architecture.