CyberSecurity Morning Rundown

Heroes, late breaking critical news. Here's a detailed look at the current cybersecurity landscape for September 17, 2025.

COMMENTARY: Why AI projects fail
Beyond the usual "AI isn't ready" excuses, two real reasons matter.

AI is being asked to boil the ocean This one is probably easy enough to digest. At first glance, it looks like AI is extremely capable, but when it comes to orchestration of business processes, you still need an architect to oversee it all. Until the coding community themselves up-skill to having managerial and architecture skills commensurate with complex projects, you'll have the same outcome as hiring dozens of coders but having no competent leadership.

Some problems are simply beyond the scope of being 'solvable' The short story is that AI does not overcome math, and some problems can't be solved in a reasonable time. The classic case is the 'traveling salesman problem' (you have 1 salesmen but must visit 50 customers. What is the optimal path? If you look at a map, the options seem infinite to calculate). In these cases, we apply heuristics (shortcuts) that get us to a 'good enough' answer, but we don't really know for sure we chose the most optimal one. But it works and is profitable, and we move on.

If you're looking for AI to optimize supply chain costs, it's gonna be a slog. Too many moving parts outside of your control. Technologies like AGI still won't overcome math.

But AI can be an amazing tool to sort through the noise and give stakeholders options for high-level decisions, and solving smaller problems quite well (for example, how to load a truck optimally, etc).

The takeaway: AI amplifies good decisions. It doesn't make them for you.

A green flag when selecting an AI solution is they emphasize empowerment of the human-in-the-loop. These people are still the ones that can adjust for dirty or outdated data, new requirements, and corner cases.

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