Traditional cybersecurity operations face a critical scalability problem in autonomous threat detection. Collecting 10–20 GB/s per endpoint across 350,000 endpoints would generate 15 petabytes daily, making centralized data collection impossible. This creates dangerous gaps between alert generation and investigation, increasing the risk of missed threats and prolonged incident response times. Solution: Allianz developed a hypothesis-based AI analysis system that reimagined threat investigation. Rather than collecting all endpoint data centrally, the AI generates hypotheses when alerts are triggered, identifies the data points needed to validate them and retrieves them on demand using forensic application programming interfaces (APIs). The system iteratively refines its analysis, mirroring the on-demand data-processing approach used in autonomous vehicle technology. This enables targeted dynamic data collection, avoiding infrastructure overload.