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AI can generate a convincing Amsterdam street that never existed, then fail to recognize its own fiction when asked to geolocate it. This experiment shows why visual plausibility is no longer proof. For OSINT practitioners, the lesson is clear: seeing is not believing. Only provenance, source tracing, corroboration, and disciplined geolocation tradecraft can separate synthetic evidence, from reality in an internet where images can lie with confidence and false places can feel

I’ve been thinking a lot about why conversations around OSINT feel increasingly shallow, even though the tools we use have never been more powerful. We have more data, automation, dashboards and AI-generated summaries than at any point in the history of intelligence. And yet, when I look at how conclusions are drawn, how confidence is expressed, and how often assumptions go unchallenged, I can’t escape a deeply uncomfortable feeling: We are collecting more than ever and thin

Open-source intelligence (OSINT) is entering its most disruptive era yet. What was once a clear, open landscape is splintering into disappearing data, platform restrictions, AI-driven deception, and geopolitical pressure that reshapes information in real time. OSINT is no longer just about collecting data, it’s about surviving a shifting environment that will redefine how investigators work in the next five years.

OSINT isn’t about collecting public data, AI already does that better than humans. OSINT is about pressure-testing reality. In digital investigations, organisational security, and business risk, OSINT turns fragmented public signals into defensible understanding. As AI increases visibility but not judgment, OSINT remains a human discipline: questioning sources, timelines, intent, and absence. This article breaks down what OSINT really is, how it’s used, why businesses rely on