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AI is being touted as the future of threat detection, but its limitations are often overlooked. A useful benchmark is weather forecasting — one of the most mature and well-funded uses of AI. Despite decades of investment, petabytes of high-quality data, and physics-based models validated against reality, weather AI still gets predictions wrong.

Now compare that to cybersecurity AI:

• Data quality. Weather benefits from standardized, structured, physics-grounded data going back to the 1940s. Cybersecurity data is fragmented, inconsistently labeled, and often lacks a common taxonomy.
• Feedback loops. Weather models are constantly validated against observable outcomes. In cybersecurity, ground truth is ambiguous, feedback is weak, and outcomes are hard to measure.
• Adversaries. Weather doesn’t lie. Cyber attackers actively manipulate signals and attempt to deceive detection systems.

The takeaway: the challenge isn’t the algorithms. It’s the foundation. Without normalized telemetry, shared taxonomies, and validated outcomes, cybersecurity AI will remain brittle, error-prone, and far less trustworthy than many vendors would like us to believe.
AI has a role to play in security, but it can’t replace the fundamentals we haven’t built yet.