AI Security: Managing Overconfidence is one of six short AI courses I bought from ISC2 in May 2026, and it should have been the standout. The topic – automation bias and over-trust in AI output – is the one practitioners actually fall into, and the underlying principle is sound. What dragged it down to three stars was the opening case study: an Activision cybersecurity incident the course implies was caused by AI.

The actual incident, in December 2022, was an SMS phishing attack against an HR employee – not an AI failure – which is what I remembered from the public coverage and what the public record still says. The course never explains whether its version is a fictionalized rendering or rests on details not in the public record, and a good portion of the rest of the material builds on that same case. Once I noticed that, the rest of the course felt awkward.

The Title Promises a Different Course

Reading the title, I expected this to be about managing the overconfidence that LLMs themselves demonstrate. LLMs express certainty in things they are not sure of. They hallucinate. They cheerfully produce links that look real and do not work, with no hint that anything is amiss. The problem compounds with agentic AI in software development: development agents will cheerfully go off and implement the wrong thing, very confidently, and then it breaks and you have to figure out why. Or worse, it doesn’t break; it’s just wrong and no one notices.

Getting useful work out of those agents means writing prompts that explicitly instruct the model to check its work – error handling, unit tests, look for existing code to reuse – and then running multiple review passes over what it produced. There is no shortage of public examples. In 2025, a developer working with Replit’s AI agent watched it delete his entire production database during a code freeze. The AI was confident enough to propose the destructive operation. The human was confident enough to accept the suggestion. The result was exactly what the title of this course warns about – on both sides, the AI’s and the operator’s.

That is the kind of example I expected here. The course is instead about organizational overconfidence in adopting AI: policies, blind spots, oversight. Both are worth covering. Only one is in the box.

What It Covers

The general principle is the worth of the course: too many people treat AI as a magic box that is smarter than they are and trustworthy with everything. From my own experience using these tools, that is plainly not the case. More usefully than the principle itself, the course works through the specific blind spots AI tends to have and how to mitigate them at the organizational level – which is where it earns its place, if the case study had not soured the opening and the title had not promised something else.

Two real gaps. The first is that the course does not go into much detail on agentic AI, which is unfortunate – and given the mismatch between the title and the content, doubly so. The second is more about the world than the course: many of its recommendations are sound policy that is genuinely hard to put into practice, particularly for a smaller organization that is not already an AI shop doing its own training on its own data. Without that capability, knowledge, or hardware, a lot of the guidance hits a brick wall. That is not a flaw in the course; it is the current reality of AI use under real constraints of cost, hardware, and skill.

Verdict

Three stars. The principle is the right one, the blind-spot guidance is the kind of thing a working professional should walk through deliberately rather than absorb on the fly, and the difficulty of implementing the recommendations in a small organization is honestly the world’s problem more than the course’s. Two things did the damage. The course is about organizational overconfidence in AI, not the LLM overconfidence the title implies, and that mismatch is a missed opportunity given the public examples sitting right there. And the opening case study was wrong enough, on a public incident I happened to remember, that the rest of the material sat under a cloud. If you can read past the opening and reset your expectations on the title, the substance is real. I would have preferred a different example and a different framing.

Part of The ISC2 AI Course Bundle . More from the bundle: AI for Cybersecurity · The Evolving Cybersecurity Workforce · Foundations of AI · Aligning with Global AI Regulations · Planning for Secure by Design AI