Artificial intelligence has moved from optional “nice-to-have” to indispensable tool. From support in business workflows to lifesaving applications in health diagnostics and autonomous systems, AI is already deeply embedded in decisions that shape outcomes for individuals, companies, and societies alike. The question many are now asking, as it was during a recent ReadSetCyber discussion, is not if AI is making critical decisions, but whether we can trust it to do so safely and responsibly.

Trust Isn’t Binary. It’s Calibrated

When we talk about trusting AI, it helps to unpack what trust really means. It’s not that AI must be infallible. No technology ever is. For AI to be trusted, it must operate in ways we understand, can audit, and can oversee. Across industries, research on human–AI interaction suggests that the term “trust” is often misused; instead, what we should seek is appropriate reliance on AI systems. This means aligning capabilities, transparency, and accountability with the level of risk at stake. 

AI systems are essentially sophisticated pattern recognizers. Their outputs depend heavily on the quality of inputs, training data, and how problems are framed mathematically. Confidence in AI decisions is always contextual, not absolute.

Automation Bias: The Hidden Risk in “Trusting Too Much”

A well-documented phenomenon called automation bias shows that humans often defer to automated systems even when they are flawed or when evidence suggests otherwise. Over-reliance on AI — assuming it’s ‘correct’ simply because it’s automated — is one of the biggest subtle threats to safe decision-making, especially in high-stakes environments like healthcare, finance, or crisis response.

This is why AI should be an advisor, not an autonomous decision-maker, unless appropriate safeguards and human oversight are in place. Particularly in areas with ethical or life-impact consequences, the best outcomes come when humans and AI contribute complementary strengths. 

Trust, Transparency & Explainability

One major barrier to trusting AI is the infamous “black box” problem. If no one can explain how a system reached a conclusion, it’s hard to justify critical actions based on that output. This lack of interpretability erodes trust. And in domains like regulated industries or safety-critical systems, it can be a show-stopper. 

Meaningful trust requires:

  • Explainability: People and teams must understand why a decision was made.
  • Governance: Decision pathways must be monitored and reviewed.
  • Accountability: Humans remain responsible for outcomes.

Without these, “trust” becomes a placeholder for unwarranted optimism.

When AI Meets Cybersecurity: Trust Under Threat

The intersection of AI and cybersecurity illustrates how fragile trust can be if systems aren’t secured and governed properly.

  • Cybersecurity leaders now talk about AI not just as a tool but as an attack surface. If governance and controls are weak, adversaries can exploit AI systems to influence outputs, manipulate data, or undermine decision-making.
  • Emerging risks like prompt injection demonstrate how malicious actors can subtly manipulate decision prompts or data inputs, turning AI into a vector for cyber exploitation. 
  • And while AI can be an incredible force for faster threat detection and response, over-reliance without human oversight can reduce human vigilance and decision agility – ironically increasing risk, not lowering it. 

This is why cybersecurity isn’t an add-on to AI trust. Rather, it’s central to it. We can’t meaningfully trust AI to make critical decisions unless we also trust the systems and processes that secure the AI itself.

Conclusion: Trust Is Earned, Not Given

So, can we trust AI to make critical decisions? The honest answer is: sometimes. But only when designed, governed, and overseen with intention and rigor. And this means including Humans.

AI shouldn’t replace human judgment where consequences are profound. Instead, it should augment human thinking, offering insights that humans interpret, contextualize, and validate.

Trust in AI isn’t about surrendering control. It’s about sharing decision-making responsibly, with transparency, safeguards, and people firmly in the loop.