AI-enabled security tools are creating noise instead of actionable insight from security logs. Security professionals struggle with how to prioritize their time fixing the most urgent issues. Tools based on large-scale LLMs are expensive to create and expensive to use. And you want to share your company’s data with a big tech player? If not, keep reading and discover how PyLog turns raw telemetry into actionable insight while keeping data private and under your control.

This week I read an interview with Yann LeCun in the New York Times. LeCun is one of the so-called ‘godfathers of AI’. He argues that the AI industry is ‘LLM-pilled’ and that the herd mentality is taking us into a dead end. The AI boom is hitting a dead‑end, warns Yann LeCun, but the next breakthrough isn’t bigger models, it’s focused, local‑first AI that actually understands your security logs. Being fixated on a single approach has become a proxy for progress. Ever-larger language models are being trained on everything at enormous consumption of energy and memory chips. And with diminishing returns. 

The main problem with “bigger is smarter” is that large language models are extraordinary statistical machines. They compress vast portions of human language into probabilistic representations that can reason, summarize, and generate at impressive levels. But they also have fundamental constraints:

1. They do not understand the physical or operational world they operate in

2. They do not plan or reason over causal systems

3. Errors compound as tasks become more complex

You end up with a generic model when trained on ‘everything’, and specificity is lost. PyLog has a design philosophy built on different assumptions. Real-world security problems don’t need general intelligence, but domain-specific context explained well by IBM. Instead of training massive models on the entire Internet, PyLog focuses on:

• One domain: cybersecurity and infrastructure telemetry

• One problem: understanding what security logs actually mean

• One constraint: privacy, locality, and operator control

PyLog ingests firewall logs, network events, and system telemetry; normalizes them, and applies small, open-source language models trained specifically on security semantics, not internet prose. The result is not a chatbot that ‘sounds smart,’ but a system that can:

1. Explain why a firewall event matters
2. Classify risk based on real infrastructure context
3. Reduce alert fatigue instead of amplifying it
4. Run locally, without exporting sensitive data to cloud LLMs

This is applied AI, not aspirational AGI. This is key to AI outside of Silicon Valley. The EU talks extensively about AI sovereignty, yet often equates sovereignty with regulation rather than architecture. Local-first, domain-specific AI systems change that equation by:

1. Reduce dependency on hyperscale cloud providers
2. Enable compliance by design, not by policy
3. Allow organizations to retain control over data, models, and inference
4. Scale economically where trillion-parameter models do not

In cybersecurity, especially, exporting raw logs to external AI services is often a non-starter. PyLog was designed for that reality. And, we believe that open systems are safer systems. PyLog is a solution where:

1. Open models can be inspected
2. Training data can be constrained and audited
3. Behavior can be tested, reproduced, and corrected
4. No single vendor controls the intelligence layer

In security, opacity isn’t a feature. It’s a liability. The next wave of useful AI may not come from models that know everything, but from systems that know one thing extremely well. PyLog is not trying to build superintelligence. We make security teams less blind, and less overwhelmed.

If the industry really is approaching a dead end, as LeCun suggests, then the way forward may not be bigger models, but better questions asked and answered.