When Do You Trust AI

I was sitting with a friend this weekend, and we started talking about AI and when you actually trust what it tells you. His stance was pretty firm: “I don’t trust anything it says until I verify it.”

I followed up with the obvious question: “Well then, when are there efficiency gains in using AI?” If I have to fact-check every single answer, is it really saving me time, or just giving me more to verify?

That conversation prompted this post.

When you’re using AI to help with code, there’s at least a built-in safety net. If the AI generates something that doesn’t work, the compiler or runtime tells you right away. But when you’re asking AI about real-life questions around application security or AI security no equivalent “compiler error” to catch mistakes.

So even in programming, we don’t really stop at “the code runs.” We verify with tests, peer reviews, and experience. With AI, verification is harder because the errors don’t follow predictable patterns like syntax mistakes or common logic bugs.

The hardest thing about working with AI isn’t that it’s sometimes wrong, it’s that it’s inconsistently wrong in ways that don’t follow learnable patterns.

With human experts, you develop a sense of their reliability over time. Sarah is great with technical architecture but terrible with timelines. Mike knows the regulatory landscape cold but gets lost in implementation details. You learn to weight their input accordingly.

AI systems don’t give you that luxury. The same model that brilliantly explains secure coding practices might confidently cite a non-existent NIST guideline in the next breath, or suggest a coding pattern that doesn’t make sense with the application you are working on. There’s no consistent “personality” to calibrate against.

This creates what I call “the trust thermometer problem”: we need to adjust our skepticism in real time, case by case, without reliable patterns to guide us.

So how do we develop good judgment? I think it comes down to building verification habits around three key questions:

  1. Can I quickly triangulate this?
    If I’m asking about common SQL injection patterns, I can check against OWASP, run static analysis, or validate with dynamic testing. If I’m asking about whether a new AI regulation has actually passed, verification might take hours, time to go straight to the primary source instead.

  2. What’s my error tolerance here?
    Getting the general approach right for a brainstorming session? Low stakes, high tolerance. Deciding whether to deploy a new security control in production? Very low tolerance, requiring multiple verification layers.

  3. Am I asking the right type of question?
    AI excels at synthesis, pattern recognition, and generating starting points. It’s weak at real-time information, nuanced judgment calls, and anything requiring recent domain expertise. Matching the tool to the task matters more than generic trust levels.

Take a concrete example: say I’m asking AI to help identify SQL injection vulnerabilities in a code review.

  • Triangulate? Yes, I can check against OWASP guidelines, run static analysis tools, and compare with known vulnerability databases.
  • Error tolerance? Very low, missing an injection flaw could mean a data breach.
  • Right type of question? Partly, AI is good at spotting common injection patterns and suggesting mitigations, but weak at understanding business-specific logic and custom frameworks.

This tells me to use AI for pattern detection and remediation suggestions, but require manual code review and dynamic testing before marking anything as secure.

The meta-skill isn’t learning to trust or distrust AI, it’s learning to rapidly assess these three factors and adjust your verification approach accordingly. Like developing a good poker sense, it improves with practice and conscious attention to your own decision-making patterns.

And yes, this is cognitively demanding. We’re naturally inclined to trust confident-sounding responses, and constantly adjusting skepticism levels creates mental overhead that many people find exhausting.

But the goal isn’t perfect judgment it’s better judgment that gets more efficient over time.

I’m curious what you think

  • How do you decide when to trust AI?
  • Have you found tricks that save verification effort without sacrificing accuracy?
  • Or do you lean toward my friend’s stance: trust nothing until proven?

I’d love to hear your thoughts, drop them in the comments or share how you’ve built your own “trust thermometer.”