Building a Secure Internal AI Chatbot for CyerSecurity Company
Client Overview
The client required an internal AI-powered chatbot to serve multiple clients, each with unique requirements. The chatbot needed to handle sensitive client data securely while providing accurate, context-aware responses.
Challenges
– Each client had different types of data sources (websites, PDFs, system artifacts).
– The system had to parse, normalize, and embed data for efficient retrieval.
– Strong security and guardrails were critical to ensure the chatbot only responded to queries relevant to the client’s data, avoiding irrelevant or unsafe answers.
– Needed to support dynamic checking techniques for ongoing validation.
Solution
We developed a chatbot architecture that:
- Ingests client-specific data (websites, PDFs, and artifacts)
- Processes and normalizes documents before creating embeddings.
- Stores embeddings in ChromaDB and FAISS for efficient vector search.
- Uses LangChain for orchestration of parsing, retrieval, and query handling.
- Fine-tunes the model on client-specific data using OpenAI’s API (ChatGPT).
- Deploys securely on a remote Ubuntu server via SSH, ensuring access controls and isolation.
- Implements strict guardrails (mono-guardrails) to prevent the chatbot from answering out-of-scope or irrelevant questions.
Technologies Used
ChromaDB
FAISS
LangChain, custom document parsing & normalization
ChatGPT (OpenAI API)
Remote Ubuntu server with SSH access
Impact Of The Project
The chatbot now enables its software to provide secure, tailored, and efficient AI-driven interactions for their clients, while maintaining strict data security and compliance with cybersecurity standards.
Conclusion
The successful development and deployment of the secure internal AI chatbot enabled the client to modernize their service delivery while maintaining the highest standards of cybersecurity. By combining advanced data processing, vector search technologies, and strict guardrails, the solution ensures that each client receives accurate, context-appropriate responses based solely on their approved data.
This architecture not only enhances operational efficiency but also reinforces trust by providing a controlled, compliant, and scalable AI ecosystem. The project demonstrates how AI and cybersecurity can work hand-in-hand to create intelligent, secure, and future-ready digital solutions tailored to enterprise needs.