OptikalAI – Cybersecurity Expert Large Language Model
Overview
OptikalAI is a conceptual large language model (LLM) designed to assist security analysts, threat hunters and compliance teams. It is built by fine‑tuning an open‑source base model (e.g., LLaMA 2, Mistral, Falcon) on carefully curated cybersecurity corpora drawn from threat‑intelligence feeds, vulnerability databases and security standards. The goal is to provide accurate, actionable and safe responses about vulnerabilities, adversary techniques, defensive strategies and regulatory compliance while avoiding the generation of harmful content.
This repository contains example files and scripts that demonstrate how one could fine‑tune and package a cybersecurity‑focused LLM for deployment on the Hugging Face Hub. No actual model weights are included – users must supply a base model and their own domain‑specific data.
Training Approach
Data Collection and Cleaning – Aggregate structured and unstructured threat‑intelligence sources such as MITRE ATT&CK, CVE/NVD entries, CERT advisories, vendor security blogs and open‑source research papers. Remove sensitive or proprietary information and normalize terminology (e.g., CVE identifiers, ATT&CK techniques). See the high‑level guidelines in /home/oai/share/optikalai_guidelines.md for a summary of recommended sources and practices【694563648843875†L11-L27】.
Base Model Selection – Choose a permissively licensed base model (e.g., meta-llama/Llama-2-7b-hf or mistralai/Mistral-7B-v0.1). The selected model should balance capability with computational feasibility【694563648843875†L31-L43】.
Parameter‑Efficient Fine‑Tuning – Use Low‑Rank Adaptation (LoRA) or another adapter‑based technique to fine‑tune the base model on your security corpus【694563648843875†L41-L43】. This approach trains only a small number of parameters, reducing compute requirements while preserving base model weights.
Safety and Alignment – Integrate RLHF using security‑expert feedback to discourage unsafe completions. Implement content filtering to block requests for exploit code or malicious actions【694563648843875†L47-L54】. Consider a retrieval‑augmented pipeline that queries up‑to‑date vulnerability databases at inference time【694563648843875†L51-L54】.
Evaluation – Measure performance on tasks like vulnerability classification, ATT&CK mapping and mitigation suggestion. Use cybersecurity‑focused benchmarks (e.g., CyberMetric or CyberSec‑Eval) to quantify the model’s utility【694563648843875†L55-L59】.
Packaging – Export the fine‑tuned model weights (e.g., pytorch_model.bin or LoRA adapters) along with configuration files (config.json, tokenizer.json) and this README. Use huggingface-cli to create a repository and upload the files【694563648843875†L76-L89】.
File Structure
optikalai_model_repo/
├── README.md # This model card
├── train_optikalai.py # Example script to fine‑tune a base LLM with LoRA
├── config.json # Placeholder model config (to be generated after training)
├── adapter_config.json # Placeholder for LoRA adapter config
├── adapter_model.bin # Placeholder for LoRA adapter weights (empty)
Usage
1. Prepare Your Dataset
Organize your training data in a JSON or CSV format where each record contains an instruction (query) and an expected response. Ensure that your data adheres to privacy and licensing requirements【694563648843875†L18-L22】.
2. Fine‑Tune the Model
The script train_optikalai.py demonstrates how to fine‑tune a base model using Hugging Face’s transformers and peft libraries. Install the required dependencies:
After training, the LoRA adapter weights will be saved in the specified output directory. Copy adapter_model.bin and adapter_config.json into this repository and update config.json to reflect the base model architecture.
3. Push to Hugging Face
Authenticate with your Hugging Face account and create a new model repository:
Optionally, enable gated access for the repository if your model contains dual‑use content【694563648843875†L86-L90】.
Limitations
No Weights Provided – This repository does not contain pre‑trained or fine‑tuned model weights. You must perform training with your own data.
Compute Requirements – Fine‑tuning large models requires significant GPU resources. Consider using parameter‑efficient methods (LoRA) or smaller base models to reduce costs【694563648843875†L41-L43】.
Safety Measures – Despite filtering and RLHF, the model may still produce inaccurate or unsafe advice. Always include a human analyst in the loop【694563648843875†L47-L54】.
License
This repository is provided for educational purposes. Ensure that your use of training data and downstream model complies with all applicable licenses and regulations.
