Remediation Automation Playbook

Automate policy violation handling and PII redaction using AISentinel webhooks, SDKs, and workflow orchestrators. This guide complements Webhook Integrations and LangChain examples with end-to-end remediation pipelines.

Workflow Overview

1. Detection: AISentinel emits policy.decision.created events when an evaluation is denied or flagged.
2. Ingestion: Webhook receiver validates signatures and enqueues remediation tasks.
3. Automation: Workers apply fixes—redacting content, regenerating outputs, or notifying humans.
4. Verification: Updated content is re-evaluated by AISentinel to confirm compliance.
5. Audit: Results are logged with auditId references for compliance tracking.

Reference Architecture

Sample Remediation Worker (Python)

import os
from aisentinel import Client, ApiError
from redaction import redact_pii

client = Client(api_key=os.environ["AISENTINEL_API_KEY"], tenant_id=os.environ["AISENTINEL_TENANT_ID"])

def handle_job(job: dict) -> None:
    decision = job["decision"]
    if decision["decision"] != "deny":
        return

    content = decision["payload"]["content"]
    redacted = redact_pii(content)

    try:
        reevaluation = client.policies.evaluate(
            input=redacted,
            rulepack=decision["rulepack"],
            context={"sourceAuditId": decision["auditId"]},
        )
    except ApiError as exc:
        if exc.status_code == 429:
            raise RuntimeError("Rate limit hit during remediation") from exc
        raise

    if reevaluation.decision == "approve":
        publish_fix(redacted, reevaluation.audit_id)
    else:
        escalate(decision, reevaluation)

• redact_pii represents your custom sanitizer leveraging NLP or deterministic masking.
• publish_fix can push updates to data warehouses, vector stores, or CRM records.
• escalate sends context to incident management (PagerDuty, ServiceNow).

Automated PII Redaction Workflow (n8n)

1. Trigger: Webhook receives denial event.
2. Split: IF node checks decision.reasonCode == "pii_detected".
3. Redact: Execute Python node using the script above.
4. Re-evaluate: HTTP node calls AISentinel to confirm fix.
5. Notify: Slack and Jira nodes provide updates with new auditId.

Error Handling and Retries

• Transient Errors (5xx): Implement exponential backoff with jitter.
• Permanent Errors (403 policy_denied): Escalate to security analysts for manual review.
• Queue Poison Messages: After configurable retries, move to dead-letter queue and create a ticket.

Security Considerations

• Store remediation credentials in secret managers and rotate via Key Rotation.
• Enforce role-based access per Team Management; automation accounts need limited scopes.
• Log every automation step and forward to your SIEM per Auditing & Compliance.

Monitoring and Metrics

• Track mean_time_to_remediate (MTTR) and automation success rates.
• Use Usage Analytics to correlate denial rates with remediation workload.
• Create dashboards for recurring root causes (rulepack, agent, dataset).

Extending the Playbook

• Integrate with Research Automation to sanitize research outputs before publication.
• Use BYOK-protected storage (see Encryption BYOK) for sensitive remediation artifacts.
• Generate deletion proofs for sanitized records using Cryptographic Proofs.

Automated remediation closes the loop between detection and resolution, ensuring AISentinel guardrails lead to rapid, auditable fixes.