A practical framework for data classification, access control, provider review, logging, retention, and incident response.
Separate public, internal, confidential, personal, financial, medical, and regulated data. Each category needs explicit rules for processing, storage, and access.
Send only the data required for the task. Remove direct identifiers, secrets, irrelevant document sections, and hidden metadata.
Review provider terms, data residency, model-training settings, subprocessors, deletion controls, encryption, and audit capabilities. Apply least privilege to both people and services.
Logs should show what data was used, which model and prompt version ran, and what action followed. Define procedures for revocation, notification, investigation, and recovery.
You do not need a detailed brief to begin.
These safeguards are especially relevant to AI assistants and governed AI agents that work with company data.
Learn how we assess the process, business case, data security, and quality before development begins.
Explore the methodology →Determine whether the task calls for AI, rule-based automation, or a process redesign.
Assess a process →Send the current process, constraints, and expected outcome, and we will suggest a practical first step.
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