AI Behind the Firewall
Why Local LLMs Are Finally Worth Your Attention
We previously explained in a recent Deep Finance Dispatch article why generative AI platforms like ChatGPT Enterprise, Claude, and Gemini can be deployed with the same security confidence as your ERP or CRM system. When configured correctly with zero-retention settings, SOC 2 compliance, and administrative controls, these tools pose no greater risk than any other enterprise SaaS platform.
However, not every organization has the flexibility to use cloud-based tools, even if they meet those standards.
Some industries operate under strict data governance or regulatory mandates that completely prohibit uploading sensitive information to third-party systems regardless of encryption, compliance certifications, or usage limits. In these cases, even the most secure cloud configuration is not an option.
Who Cannot Use Cloud-Based AI?
The list of organizations blocked from using cloud-based AI tools, even when they are secure, is larger than many people realize:
Defense and aerospace contractors working with ITAR-restricted data (such as Lockheed Martin or Raytheon)
National security agencies handling classified or compartmentalized information
Systemically important financial institutions required to store customer data in-country
Audit and tax teams managing sensitive client data before signoff
Healthcare payers and providers governed by HIPAA, GDPR, or similar privacy regulations
These organizations often operate within closed environments, where all data must remain inside internal systems, with no exposure to cloud APIs or third-party inference engines.
A New Option: GPT-Class AI Without the Cloud
Until recently, this was a hard limitation. The most advanced AI models, such as GPT-4o or Claude Opus, were only available through cloud APIs. That created a trade-off: organizations could choose performance or compliance, but not both.
That is now starting to change.
This week, OpenAI released GPT-OSS, a fully open-weight model available under an Apache 2.0 license. The model is available in 20B and 120B parameter sizes and is designed for on-premise use. It can run entirely within your infrastructure, with no data leaving your environment.
It is not alone. Meta's Llama 4 and Mistral's Mixtral 8x22B also offer ways to run powerful AI without exposing sensitive data to the public cloud. These models are open-weight and can be deployed locally, under full enterprise control.
These models are not as powerful as the very best frontier models. But for many use cases, they are more than capable. And more importantly, they can be deployed entirely under your own security, access, and logging policies.
What This Means for Finance Teams
For finance professionals working in highly regulated or data-sensitive environments, the ability to run GPT-level models locally is a breakthrough. It makes it possible to automate or accelerate tasks that were previously blocked due to data restrictions.
Some examples include:
Variance analysis: Draft commentary based on ledger and budget data without moving it outside the firewall
Audit support: Summarize internal memos or workpapers without exposing pre-signoff data to any external platform
Contract review: Extract terms and identify risks in vendor agreements using models that never leave your servers
Finance chatbot: Build secure Q&A tools that stay entirely within your data perimeter
With GPT-OSS and Llama 4, these kinds of workflows can be handled with AI that respects the same controls your other enterprise systems follow.
Why This Moment Matters
The performance of open models has improved significantly in the past year. In 2023, open-source models fell well behind GPT-4, Claude, and Gemini in terms of reasoning and language quality. Today, GPT-OSS-120B, Mixtral 8x22B, and Llama 4 all offer performance that comes close to GPT-3.5 and in some cases approaches GPT-4 mini.
That changes the equation for teams that need both control and capability.
For the first time, organizations with strict security policies can deploy generative AI that is fast, useful, and capable of real reasoning—all without moving data outside their infrastructure.
In the Pro Edition:
We’ll compare the top local models available today, walk through what it takes to deploy GPT-OSS inside your environment, and provide templates for secure prompt logging, RAG pipelines, and finance-specific workflows.
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