Foundational Principles (Not AI-Specific)
- Plan for failure early & continuously
From the moment you start designing a system, keep asking: What can go wrong? Build safeguards accordingly. - Think beyond the tech
The “system” includes not just the AI, but also humans, business processes, and how the system is used in real life. - Don’t silo problems
Privacy, security, or ethical risks are everyone’s concern. Avoid passing the buck between teams—solve holistically. - Make a written safety plan
Always document what the system is, what risks you foresee, and how you plan to handle them. This is your accountability trail.
AI-Specific Guidelines
- AI ≠ Perfect logic
AI will make mistakes. Treat it like a well-meaning but inexperienced intern—capable, but needs oversight. - Common failure types in AI
- Bad inputs (GIGO)
- Misinterpreted data
- Hallucinations (false positives)
- Omissions (false negatives)
- Unexpected preferences
- Design like you would for humans
Add layers of review and checks for key decisions—just like you’d do for a new hire handling sensitive tasks. - Prioritize testing over coding
You’ll spend more time testing AI than building it. Expect to iterate with real-world data, edge cases, and even threat scenarios.
Safety in Decision-Making
- Use test cases to validate AI behavior
Define clear criteria, test across varied examples, and involve multiple reviewers to ensure alignment. - Cross-check decisions
Randomly audit outputs using both humans and AI. Flag disagreements or high-risk decisions for expert review. - Be intentional with information flow
Whether it’s AI → human or team → team, design clear interfaces (UX or API) to avoid miscommunication and blame.
Final Insight
AI doesn’t demand a new safety philosophy—it pushes us to rigorously apply the basic principles of good system design.
Source: Microsoft Security Blog – How to Deploy AI Safely (May 29, 2025)
