Self-Hosted AI with Security-Focused RAG

Why Self-Hosted AI with Security-Focused RAG Is a Game-Changer for Security & Life Safety Research

In physical security, life safety, and critical-infrastructure protection, information quality matters as much as information access. Decisions about code compliance, system design, equipment selection, and operational response are often made under time pressure and legal scrutiny. In these environments, relying on generic cloud-hosted AI tools—trained on unknown data, constrained by safety filters, and constantly transmitting queries to third parties—is often difficult or impossible to justify.

A self-hosted AI model, paired with a carefully curated Retrieval-Augmented Generation (RAG) knowledge base, offers a fundamentally different approach. It transforms AI from a general-purpose chatbot into a private, domain-specific research and decision-support system tailored to security and life safety work.

This article explores the benefits of that approach, using a concrete example: a self-hosted Ollama instance running OpenAI’s open weight public model called “gpt-oss-120b”, fine-tuned to be uncensored and connected exclusively to private RAG content—no cloud data, no external APIs, and no data leakage.   Adding uncensored fine tuning to a private model for security makes sense because often some of the questions asked would be refused or rejected by a model for “safety” reasons.  We ask pointed questions in this industry, and removing those guardrails helps improve the response content quality and experience.  In this context, uncensored does not mean uncontrolled or unethical. It means that responsibility for governance, acceptable use, and risk management remains with the organization—not a third-party AI provider.

Note: The hardware requirements for this type of configuration are significant, requiring an enterprise grade server platform and GPU.   Using a 120b model means it was originally trained on 120 billion parameters, and the resultant model is huge, over 65Gb.  This means that the GPU used must have at least 65Gb VRAM, but often more, to comfortably use the model.  The fine-tuning process also takes significant resources and time to do properly.

The Problem with Generic AI in Security-Critical Domains

Most public AI tools are optimized for breadth, not depth. They excel at summarizing general concepts but struggle when asked to reason across:

• Local building codes and jurisdictional amendments
• Manufacturer-specific installation constraints
• Past incident reports and internal post-mortems
• Lessons learned from failed deployments
• Sensitive vulnerabilities that cannot be discussed publicly

Even worse, cloud-based AI introduces three risks that are unacceptable in many security environments:

1. Data exposure risk – Queries, prompts, or uploaded documents may be logged, retained, or used for training.
2. Censorship and safety filters – Overly broad restrictions often block legitimate discussions about weapons detection, attack vectors, or system failures.
3. Loss of institutional knowledge – Internal reports and historical lessons remain siloed in PDFs and file servers instead of being actively used.

A self-hosted AI with private RAG directly addresses all three.

What “Security-Focused RAG” Really Means

Retrieval-Augmented Generation is not just “search plus AI.” In a security context, it becomes a structured knowledge fabric that combines:

• Code and standards (e.g., fire, electrical, access control, and life safety codes)
• Best-practice guides from integrators and manufacturers
• Equipment specifications and field notes
• Project profiles from past installations
• Legal and Regulatory (CFR, NERC, FERC, ADA, etc)
• After-action reports and post-mortems

Unlike a public LLM that guesses based on training data, a RAG-enabled system grounds its answers in documents you explicitly control.

For example, a private RAG corpus might include:

• Local amendments to national fire and building codes
• Card-access and CCTV design standards used internally
• Lessons learned from failed perimeter detection deployments
• Commissioning checklists for substations or hospitals
• Vendor datasheets annotated with real-world limitations

The AI does not invent answers—it synthesizes them from your material.

Architecture: Self-Hosted, Private, and Air-Gap Friendly

In our example environment:

• Model runtime: Ollama
• Model: gpt-oss-120b (running entirely on-prem GPU hardware)
• Fine-tuning: Configured to be uncensored and domain-focused
• RAG store: Local vector database (FAISS, Qdrant, or similar)
• Data sources: Internal PDFs, DOCX, CAD notes, incident reports
• Network posture: No outbound internet access, APIs, or cloud data shared

Every query stays local. Every document remains under your control. This architecture is particularly attractive for:

• Law firms
• Hospitals
• Utilities and Energy Sector
• Critical manufacturing
• Government and defense contractors

Practical Use Cases in Security and Life Safety

Code Compliance Interpretation (Not Just Quoting)

Instead of asking, “What does the fire code say about egress?” you can ask:

“Based on our past hospital projects and current code references, is delayed egress allowed on psychiatric units, and what operational safeguards are required?”

The AI can respond by correlating internal project notes, AHJ feedback from prior inspections, and applicable code excerpts—highlighting conditions, exceptions, and pitfalls.

This goes far beyond static compliance checklists.

Equipment Recommendations with Context

Generic AI might say, “Use an IP camera with analytics.”
A security-focused RAG system can say:

“In outdoor substations with high EMI and temperature swings, our past deployments show radar-based intrusion detection paired with thermal cameras reduced nuisance alarms by 63%. Optical-only analytics failed during snow events.”

That answer is rooted in your own deployment history—not marketing copy.

Lessons Learned and Post-Mortem Analysis

One of the most powerful uses of private AI is surfacing uncomfortable truths.

Example query:

“What recurring mistakes have we made in perimeter detection projects, and how can they be avoided?”

Because the model is uncensored and private, it can summarize failures honestly:

• Poor ground-truth calibration
• Inadequate lighting assumptions
• Over-reliance on vendor defaults
• Underestimating maintenance overhead

This turns institutional memory into an active design asset.

Threat and Risk Assessment Without Oversharing

Security professionals often need to analyze attack patterns or failure modes that public AI tools avoid.

A self-hosted model can safely reason about:

• Tailgating risks at turnstiles
• Credential cloning threats
• Sensor evasion techniques
• Alarm fatigue scenarios

All without violating policy, leaking data, or triggering moderation blocks.

Why “Uncensored” Matters in a Professional Context

“Uncensored” does not mean reckless. It means professionally honest.

In security and life safety work, avoiding difficult topics leads to bad outcomes. A fine-tuned, uncensored model allows:

• Realistic threat modeling
• Open discussion of system weaknesses
• Accurate failure analysis
• Candid design tradeoffs

Because the model operates in a closed environment, ethical and legal responsibility stays with the organization—not a third-party provider.

Long-Term Value: Institutional Knowledge, Preserved

Over time, a private RAG system becomes a living archive:

• New projects feed back into the knowledge base
• Lessons learned become instantly searchable
• Junior engineers gain access to senior-level insight
• Decision-making becomes more consistent and defensible

Instead of losing expertise when people leave, organizations retain and amplify it.

Final Thoughts

A self-hosted AI model running locally—paired with security-focused RAG content—represents a fundamental shift in how security and life safety professionals research, design, and assess risk.

By using an on-prem LLM instance, fine-tuned for uncensored reasoning and connected only to private data, organizations gain:

• Total data sovereignty
• Honest, domain-specific analysis
• Faster and better-informed decisions
• A durable institutional memory

In a field where mistakes can cost lives, lawsuits, or reputations, that advantage is not theoretical—it’s strategic. If you treat AI as infrastructure instead of a novelty, self-hosted security-focused RAG is one of the most powerful tools you can deploy.

If you are interested in how we deployed and configured self-hosted LLMs to augment for our clients, give us a call to discuss your needs and we will be glad to help you.

 Hardware Recommendations