Blog

Evidence poisoning is the contamination of the open web with synthetic, manipulated or amplified material that later appears useful as investigative evidence. In an age of AI slop, fake personas, recycled footage and algorithmic summaries, OSINT practitioners must ask not only whether a source can be found, but whether it was manufactured to be found.

AI has made it easy for OSINT practitioners to build and install investigative tools. It has not made it safe to trust them. This blog examines the real risk behind vibe coding, viral GitHub apps, and useful-looking platforms that may expose workflows, distort analysis, or reveal investigative intent. In OSINT, the danger is not only bad code. It is trusting software you do not fully understand.

This is not just random extremism. It is a pipeline. From open platforms to closed chats, vulnerable people can be spotted, shaped, tested, and pushed toward real-world violence as disposable proxies. This piece examines how that works, why Jewish sites and dissidents are attractive targets, and how OSINT investigators can spot the runway before the fire starts.

AI may not replace analysts first. It may break them first. This blog explores how cognitive overload, synthetic media, AI dependence, and algorithmic pressure may erode the human judgment behind OSINT, threat intelligence, and digital investigations. The real danger is not just AI-generated content, but what prolonged exposure to machine-shaped workflows does to attention, skepticism, and critical thinking in a polluted information environment.

AI isn’t just speeding up OSINT - it’s reshaping what investigators and leaders believe is true. As hallucinations, data poisoning, and AI-generated narratives flood the information space, critical thinking is collapsing inside intelligence cycles. This blog exposes how AI-shaped distortions already influence investigations, threat intelligence, and decision-making and what must be done to stop the next intelligence failure.

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