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A focused guide series for regulated and GovCon teams who need enterprise AI over sensitive data without losing control of the model, retrieval path, cost, observability, or evidence. Start with what a private LLM is, compare it with RAG, then move into deployment, security, cost, retrieval, monitoring, and leakage controls. When you are ready to build, review GS Consulting's private LLM and secure RAG implementation service or the broader secure AI automation service.
Private is not a product you buy. It is a set of controls and operating costs you can prove. This series helps leaders connect the model environment, RAG choice, deployment options, data path security, cost, observability, retrieval design, and leakage prevention into one defensible approach for CUI and other high value data.
Start with the definition, then use the supporting guides to compare private LLM and RAG, choose a deployment option, secure the full data path, estimate operating cost, design observability, and keep controlled information out of the wrong lanes.
Read the Main GuideUnderstand what makes an LLM private, then control identity, data admission, retention, logs, response, and evidence.
Match on prem, self operated, dedicated, or managed lanes to the data duty, operating capacity, and full cost.
Build permission aware RAG so the model never sees content a user is not allowed to see.
Connect identity, sources, quality, cost, incidents, and recovery while keeping controlled information out of unapproved paths.
Featured Guides
Pillar Guide | Secure AI Automation
A private LLM is not a product you switch on. It is a control decision. This guide explains what a private LLM is, when a private LLM is worth the effort, and the controls that make one defensible for regulated and GovCon work.
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Supporting Guide | Private LLM & Secure RAG
Open source does not automatically mean safer or cheaper. This guide compares five LLM operating patterns by control discretion, operator burden, evidence, cost, and exit.
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Supporting Guide | Secure AI Automation
There is no single private LLM you can buy. There are deployment options, each trading control against cost. This guide compares on prem, self hosted, dedicated tenant, and zero retention lanes, and shows how to match the option to your data.
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Supporting Guide | Private LLM & Secure RAG
Private hosting is not a security control. This guide shows how to secure the full private LLM data path, rank the controls that matter first, and retain evidence that survives review.
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Supporting Guide | Private LLM & Secure RAG
Private LLM cost is not the model price. This guide builds a full operating model across infrastructure, people, controls, quality, change, risk, and exit.
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Supporting Guide | Private LLM & Secure RAG
Private LLM vs RAG is not an either or decision. A private LLM sets the operating boundary. RAG supplies current source knowledge. This guide shows when to use either one or both.
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Supporting Guide | Private LLM & Secure RAG
Private LLM observability is not a dashboard. It is the ability to replay why the system saw a source, produced an answer, took an action, cost money, and passed or failed review.
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Supporting Guide | Private LLM & Secure RAG
Private LLM model selection is not a leaderboard exercise. Regulated teams need to prove task fit, data boundaries, security, operating duty, change control, and exit before a model reaches production.
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Supporting Guide | Private LLM & Secure RAG
Private LLM disaster recovery is not a restored endpoint. It is the controlled return of authority, data, model behavior, runtime, tools, and evidence to a verified production state.
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Supporting Guide | Secure AI Automation
A practical guide to secure RAG architecture for GovCon teams that need enterprise AI over internal knowledge without breaking permissions, CUI boundaries, audit trails, or data controls.
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Supporting Guide | Secure AI Automation
Secure RAG is an access decision, not a vector database feature. This guide compares six enterprise patterns, shows where each one fits, and defines the evidence needed to prove retrieval stayed inside the user's authority.
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Supporting Guide | Secure AI Automation
A practical GovCon guide to keeping CUI out of unapproved LLM paths by controlling prompts, uploads, RAG, vector databases, logs, vendors, and downstream workflow tools.
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Supporting Guide | Secure AI Automation
A practical guide for regulated organizations choosing between public AI, private AI, and hybrid AI automation based on data sensitivity, workflow risk, AI authority, and evidence requirements.
Read articleGS Consulting helps regulated and GovCon teams classify data, choose deployment options, design secure RAG, prevent CUI leakage, and build the evidence packet assessors will ask for.
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