Automation limitations
Automated output can be incomplete, incorrect, outdated, or inappropriate for a specific environment. Findings should be reviewed according to impact and discipline.
Human responsibility
Customers remain responsible for authorization, implementation, production changes, legal obligations, and verification. High-impact remediation should use review, testing, rollback, and approval controls.
Data handling
Production use of external AI providers should be disclosed in privacy and subprocessor materials, including data sent, purpose, retention, and controls.
Operational implementation
- Assign an owner for implementing and reviewing the obligations described in AI and Automation Disclosure.
- Map the policy to product settings, contracts, support procedures, data flows, records, and staff responsibilities.
- Keep the public language aligned with actual production behaviour and contracted commitments.
- Retain approval, effective-date, change-history, and customer-notice records.
Review triggers
- A material product, pricing, data-processing, security, vendor, or support change.
- A new jurisdiction, customer class, contract requirement, or regulatory obligation.
- An incident, complaint, audit finding, or operational exception that shows the published process is incomplete.
- A change that could create separating client language from the technical evidence until the two conflict.
Decision and verification record
Scope
Name the website, environment, URLs, entities, templates, or user journeys included in the ai and automation disclosure decision.
Decision
Record the chosen action, owner, priority, dependencies, approval, and the evidence that justified it.
Verification
Confirm the report matches the current source records, scope, approvals, and verification status before distribution.
Turn ai and automation disclosure into an accountable record.
An implementation checklist showing where AI and Automation Disclosure is reflected in product behaviour and business procedure.