Privacy, Safety, and Responsible Use

Protect data before it becomes model input

Do not paste information into an AI product until you understand what the information contains, whether you are authorized to use it, what the product retains, who can access it, and what organizational policy permits.

Sensitive information may include personal data, health or financial records, credentials, private source code, customer material, confidential business plans, regulated data, or combinations of ordinary facts that identify a person.

Minimize input, redact or substitute where possible, use approved products and accounts, and never enter secrets into prompts or examples.

Risk follows consequence, exposure, and reversibility

A private brainstorming draft and an automated eligibility decision may use similar language technology but have radically different consequences. Consider who is affected, how widely the output spreads, whether errors can be corrected, and whether the system can take action.

Increase evidence, testing, human review, access controls, monitoring, and recovery as consequences rise. Some uses should remain prohibited when harm cannot be controlled to an acceptable level.

Responsible use has several dimensions

Privacy asks whether data is collected, used, retained, shared, exported, and deleted appropriately. Security addresses unauthorized access, prompt injection, data leakage, malicious tools, and system abuse. Fairness examines whether performance and impact differ unacceptably across people or situations.

Transparency means people understand when AI is involved, what its role is, what important limitations apply, and how to question or correct an outcome. Accountability assigns owners who can investigate and respond.

Build controls around the use case

A responsible workflow documents its purpose, approved data, users, expected behavior, evaluation method, permission boundaries, human approvals, monitoring, incident response, and retirement conditions.

Safeguards can include data filtering, scoped credentials, tool allowlists, output validation, citations, test suites, content policies, rate limits, audit logs, user reporting, manual approval, and rollback. No single safeguard replaces a complete risk-management process.