White Paper
Privacy and Compliance in AI Chatbots
A field guide for teams deploying customer-facing AI with clear data ownership boundaries, auditable controls, and realistic compliance operations.
Executive Summary
Production chatbot deployments fail trust reviews when policies are generic but controls are implicit. This white paper defines the minimum control surface expected by legal, security, and operations: data classification, retention windows, training opt-out, subprocessor transparency, and incident playbooks.
Control Domains
- Data collection boundaries and purpose limitation
- Retention schedule by data type and lifecycle event
- Model training usage policy and tenant-level opt-out controls
- PII redaction, audit logging, and access governance
- Subprocessor inventory, DPA workflow, and review cadence
Recommended Retention Schedule
| Data Type | Default | Reason |
|---|---|---|
| Conversation transcripts | 30 days | Quality review and support traceability |
| Operational event logs | 12 months | Security audit and forensic analysis |
| Lead capture fields | Per CRM policy | Commercial workflow continuity |
Operational Checklist
- Document model provider responsibilities and data flow boundaries
- Publish opt-out behavior for model improvement usage
- Add legal and security contact channels with response targets
- Run quarterly control and policy alignment review
Need the implementation walkthrough?
Use this white paper with the integration guides and case studies in the resources hub to operationalize compliance quickly.