[FEATURE] Optimization of Decision-Making through Security Alert Analysis and Artificial Intelligence

### What's needed and why?

Hello,

Would it be possible to implement artificial intelligence via an API that could switch from a learning mode to a detection mode? This would allow for processing data using a prompt that includes all relevant logs to make precise adjustments.

Currently, this task is done manually, but we are encountering numerous issues with various protections, without clearly knowing which configuration is most appropriate. We frequently have to proceed by trial and error to adjust the burst, identify the causes of problems with Mod Security, as well as alerts blocking the rules. Due to the high number of false positives, we adapt the rules progressively. A learning mode gathering the different cases would be beneficial to better understand, after learning, the necessary adjustments to Mod Security's burst configurations. Integrating this into a prompt providing details and explanations on the different Mod Security cases would be ideal.

Furthermore, providing a dedicated space during events to gather the various alerts encountered would also be advantageous. This would allow for making appropriate decisions before switching to protection mode.

### Implementations ideas (optional)

I suggest compiling statistics on all learning mode alerts to identify the most frequent occurrences and base decisions on this data (false positive or not). Then, an AI that could provide examples of strategies to follow or advice on the different alerts received by Mod Security would represent real support for decision-making and understanding of the issues.

### Code of Conduct

- [x] I agree to follow this project's Code of Conduct

The issue names no files, tests, or entry points. Start by clarifying the learning and detection workflows, alert collection requirements, and API boundaries with maintainers; done would require an agreed implementation scope and acceptance criteria for analyzing ModSecurity alerts and supporting configuration decisions.

Written by the indexing model from the issue text.