Agentic OSINT: The New Frontier of Vulnerability Hunting

Traditional OSINT methodologies are failing. The sheer volume of digital exhaust produced by modern enterprises exceeds human processing capacity. Security teams are drowning in data while missing critical signals.

Manual reconnaissance is slow, inconsistent, and often stops at surface-level enumeration. Meanwhile, threat actors are already automating their discovery pipelines, leaving defenders strictly reactive.

This research introduces a novel framework for Agentic OSINT. By decoupling reconnaissance logic from execution, we demonstrate how autonomous agents can recursively discover, validate, and correlate asset data at scale.

We detail the architecture for self-correcting discovery loops that mimic human intuition but operate at machine speed.