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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