FAT&GCR

The solution will integrate Meta Llama, Ollama, Open WebUI, Qdrant Vector Database, Python, Docker, and NVIDIA GPUs** to create an intelligent cybersecurity assistant capable of analyzing security documentation, processing threat intelligence, and supporting Security Operations Center (SOC) activities. The platform will operate on a self-hosted Ubuntu Linux infrastructure, providing greater control over sensitive enterprise security information without requiring external cloud-based AI inference. What the Project Will Do - AI-Powered Cybersecurity Analysis: Using Meta Llama models to analyze vulnerabilities, security incidents, attack patterns, and potential threats. - Intelligent Knowledge Retrieval: Implement RAG using Qdrant to retrieve information from cybersecurity documents, technical manuals, security policies, and threat intelligence repositories. - Security Telemetry Integration: Ingest and process security events from firewalls, SIEM platforms, endpoint security tools, intrusion detection systems, and Windows/Linux system logs. - Automated Incident Investigation: Correlate relevant security information, summarize suspicious activities, and assist analysts with incident triage and response recommendations. - Natural Language Security Assistant: Enable security professionals to ask questions about vulnerabilities, security events, compliance requirements, and infrastructure configurations through Open WebUI. - Automated Data Processing: Develop Python-based ingestion pipelines for document extraction, text chunking, embedding generation, vector indexing, and scheduled knowledge-base updates. - GPU-Accelerated AI Inference: Optimize local Meta Llama execution using NVIDIA RTX 3090 and Tesla V100 GPUs, subject to model compatibility and available GPU memory. - Private AI Infrastructure: Maintain control of confidential data, AI processing, knowledge repositories, and security telemetry within the organization's infrastructure. Technologies and Techniques Technologies: Meta Llama, Ollama, Open WebUI, Qdrant, Python, Bash, Docker, Ubuntu Linux, NVIDIA CUDA, and REST APs. AI Techniques: Retrieval-Augmented Generation (RAG), semantic search, vector embeddings, prompt engineering, document chunking, LLM inference, security event correlation, and workflow automation. Expected Outcome Deliver a scalable, self-hosted AI cybersecurity platform that transforms enterprise security documentation and operational telemetry into actionable intelligence. The solution will help **accelerate threat investigations, reduce manual analysis, improve access to institutional knowledge, and enhance SOC analyst productivity**, establishing a foundation for more advanced AI-assisted security operations .