We present Cybersecurity LLM Indic, a large language model fine-tuned specifically for cybersecurity purposes. This model has been trained on a curated dataset containing cybersecurity data, tips, and guidelines from various Indian government sources. The fine-tuning process involved approximately 3,000 rows of data, ensuring that the model is well-versed in the nuances of cybersecurity within the Indian context.
Base Model
The base model used for this fine-tuning process is Navarasa 2.0 2B Gemma Instruct. This base model is renowned for its versatility and robustness, making it an excellent foundation for building a specialized cybersecurity model.
Training Data
The training dataset comprises a diverse collection of cybersecurity-related information, including:
Guidelines and advisories from Indian government agencies
Best practices for securing information systems and networks
Tips for individuals and organizations to safeguard against cyber threats
Case studies and real-world examples of cybersecurity incidents and responses
Training Procedure
The model was fine-tuned using the following procedure:
Data Preparation: The raw data was cleaned and preprocessed to ensure high-quality input for training. This involved removing duplicates, correcting formatting issues, and standardizing terminology.
Fine-Tuning: The fine-tuning process involved training the model on the prepared dataset for several epochs, optimizing for performance on cybersecurity-related tasks.
Evaluation: The model was evaluated on a separate validation set to ensure its accuracy and relevance in providing cybersecurity advice and guidelines.
Use Cases
Cybersecurity LLM Indic can be utilized in various scenarios, including:
Education and Training: Providing comprehensive and accurate cybersecurity training materials.
Advisory Services: Offering real-time cybersecurity advice and best practices.
Policy Development: Assisting policymakers in drafting effective cybersecurity policies.
Incident Response: Guiding organizations in responding to cybersecurity incidents.
Limitations
While Cybersecurity LLM Indic is a powerful tool for cybersecurity applications, it has certain limitations:
Domain-Specific Knowledge: The model is specialized for cybersecurity within the Indian context and may not perform as well on general or international cybersecurity issues.
Data Limitations: The training data consists of approximately 3,000 rows, which, while substantial, may not cover every possible cybersecurity scenario.
Continuous Learning: Cybersecurity is a rapidly evolving field, and the model may need periodic updates to stay current with new threats and best practices.
Ethical Considerations
The model was developed with a strong emphasis on ethical considerations, including:
Privacy: Ensuring that the training data does not contain sensitive or personally identifiable information.
Bias Mitigation: Efforts were made to minimize biases in the training data to ensure fair and unbiased advice.
