AI Safety and Quality
A system can be secure and still produce inaccurate, biased, opaque, or unsuitable results. AI safety combines technical protection with quality evaluation and human oversight.
Safety Areas
| Area | Practical control |
|---|---|
| Transparency | Tell users when they interact with AI and when content is AI-generated where required |
| Accuracy | Ground important answers in approved sources and show source context where possible |
| Reliability | Test representative, boundary, adversarial, and failure cases before launch |
| Human oversight | Require review for high-impact outputs and decisions |
| Fairness | Review use cases involving employment, access, scoring, or people-related decisions |
| Content safety | Configure categories and thresholds for the intended audience |
| Privacy | Minimize personal data in prompts, context, logs, and evaluation datasets |
| Continuous improvement | Use feedback, analytics, incident findings, and controlled configuration changes |
Evaluation Set
Create a versioned test set for every production agent. Include:
- expected everyday questions,
- questions that should be refused or escalated,
- ambiguous and incomplete requests,
- stale or conflicting sources,
- attempts to retrieve inaccessible data,
- direct and indirect prompt injection,
- tool failures and approval rejection,
- harmful-content boundaries relevant to the audience.
Record the agent configuration, model deployment, date, expected behavior, actual result, reviewer, and release decision. Re-run the set after changing prompts, models, tools, data, memory, safety settings, or public exposure.
Product filters are configured in Organization Security. They complement, but do not replace, use-case evaluation.