Private AI & Local LLM Deployment

Overview

A practical programme for learners who want to deploy and secure private AI assistants while controlling data exposure, cost and governance boundaries. PentestHint Academy positions Private AI & Local LLM Deployment as a practical supervised learning path, not a generic AI course. The delivery model is 3 Months, Remote, 20 Classes, 1 Hour per Class, 5 Days per Week, 40 Supervised Working Days, and programme fee INR 6,999. The first month includes 20 live practical classes, conducted one hour a day, five days a week. The following two months focus on supervised remote internship and live-project work, documentation, mentor review and final project demonstration. Certificate model: Learners who meet attendance, assignment, project, documentation and review requirements receive a Three-Month Training Certificate and a Remote Internship / Live Project Certificate from PentestHint Academy. Target audience: Cybersecurity professionals, IT administrators, Developers, System and cloud engineers, Teams interested in controlling AI cost and sensitive data exposure. Prerequisites: Basic IT or cybersecurity familiarity, Comfort with operating systems and command-line concepts, Interest in private AI, local models and data governance, No machine-learning research background required. Month 1 roadmap: Week 1 Private AI foundations: LLM and private AI fundamentals, Cloud AI versus local/private AI, Hardware, storage and model-sizing concepts, Model selection and quantisation, Cost and performance considerations. Week 2 Local-model runtime and access concepts: Local-model runtime concepts, Ollama/local model deployment concepts, Docker/container fundamentals, Internal AI interface concepts, User access and role controls. Week 3 RAG and private knowledge assistants: RAG and private knowledge assistants, Document ingestion concepts, Vector database and retrieval fundamentals, Source grounding, Permission-aware access and data boundaries. Week 4 Private AI security and handover: Private AI security controls, Prompt injection and data-leakage risks, Logging and audit trails, Monitoring and governance, Deployment documentation and handover. Supervised live project examples: Local/private knowledge assistant, Controlled RAG workflow, Internal document-assistant prototype, Local AI deployment architecture, AI security-control checklist, Deployment guide and final demonstration. Required project outputs: Private AI architecture diagram, Deployment guide, RAG access-control notes, Security-control checklist, Monitoring and audit recommendations, Final demonstration. Tools and technologies: Ollama, local LLMs, Open WebUI-style interfaces, Docker, vector-database concepts, RAG pipelines, document processing, access control, audit logging and AI security testing techniques. Learning outcomes: Understand cloud AI versus local/private AI tradeoffs, Plan a local model and private knowledge assistant architecture, Review RAG access boundaries and source grounding, Document private AI security controls and handover steps. Is this about public chatbot prompting? No. The focus is private AI and local LLM deployment, controlled knowledge sources, access boundaries, RAG, logging and governance. Do I need a GPU? The course explains sizing, GPU, storage and performance concepts. Exact hardware depends on model size, use case and deployment approach. Will this cover RAG security? Yes. It covers document ingestion, source grounding, permission-aware access, data boundaries and private AI security risks. Will I receive certificates? Learners who meet attendance, assignment, project, documentation and review requirements receive a Three-Month Training Certificate and a Remote Internship / Live Project Certificate from PentestHint Academy. Does this promise a production deployment? No. Learners build supervised prototypes, architecture documents and approved project outputs, not unsupported production deployments.

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