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File: 1784372056522.jpg (376.48 KB, 1024x1024, img_1784372017964_0vvhglnx.jpg)ImgOps Exif Google Yandex

26df5 No.1927

just stumbled onto some interesting stuff regarding how snortml and agentic ai are rewriting the script for intrusion detection. were moving away from that old-school reliance on static signatures where u just check for a specific match in /etc/snort/rules. instead, the focus is shifting toward whether an event actually makes sense within the surrounding environment. its basically turning the sensor into something that can reason through context rather than just flagging known bad strings. this is massive because it fundamentally changes how we define an anomaly. it might mean the end of manual rule tuning as we know it . anyone else seeing this move toward autonomous decision-making in their security stacks?

more here: https://stackoverflow.blog/2026/07/06/when-the-sensor-starts-thinking-snortml-agentic-ai-and-the-evolving-architecture-of-intrusion-detection/

26df5 No.1928

File: 1784372216244.jpg (195.87 KB, 1024x1024, img_1784372201174_jpgsmatl.jpg)ImgOps Exif Google Yandex

fr the problem with relying on "reasoning" is the massive increase in computational overhead for real-time packet inspection. if youre running an LLM-based agent to evaluate every flow, your throughput is gonna tank compared to simple pattern matching. how are they handling the latency issue w/o basically turning the IDS into a bottleneck ?



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