Every prompt an analyst sends to a cloud AI service hands non-public customer data to a third party, and GLBA, the SEC, and FINRA all put that data under your control, not a vendor's. We'll build the whole system on your own hardware instead: the agents, the orchestration, and the models that run your firm's real work, and client financial data never leaves your network.
Every deployment starts on-site, with the people who do the work.
Air-gapped AI banking infrastructure eliminates the structural conflict cloud AI creates with the Gramm-Leach-Bliley Act's safeguards for non-public personal information.
The GLBA Safeguards Rule (16 CFR Part 314) requires financial institutions to develop, implement, and maintain a comprehensive information security program. The core mandate: protect customer non-public personal information from unauthorized access and disclosure - making financial data privacy AI architecture a core compliance decision. When a bank employee pastes loan documents, account details, or customer communications into a cloud AI service, that NPI travels across the internet to infrastructure controlled by a third party. Financial institutions are one of eleven regulated industries where this structural conflict between cloud AI and data confidentiality is most acute.
PCI DSS v4.0 adds a second layer of compliance requirements for any institution handling cardholder data. The standard mandates strict controls over cardholder data environments, including network segmentation, access controls, and monitoring. Cloud AI processing introduces third-party infrastructure into the cardholder data flow, complicating scope assessments and creating additional compliance documentation requirements.
SEC Regulation S-P requires broker-dealers and investment advisers to protect customer records and information. Insurance carriers face parallel requirements under state GLBA implementations and NAIC Model Laws. The common thread across GLBA, PCI DSS, and SEC oversight: these frameworks require institutional control over data handling infrastructure. Cloud AI processing creates a dependency on vendor infrastructure that complicates compliance across all three frameworks. The vendor's privacy policy - not your compliance program - controls what happens to that data once transmitted. Banking AI without cloud dependency is not a preference - it is the structural requirement these frameworks impose.
"No data leaves your building" is a description of network architecture, not marketing language.
NPI never leaves your network. Prompts travel from workstation to server over your internal network only. No internet connection is required for inference. No data packets leave your building.
This is a physical server in your data center, running on your power and connected to your network, not a hosted service. You own it outright.
After initial setup and model installation, the system can operate entirely disconnected from the internet. For the books that carry your most sensitive customers, that means complete network isolation, no external connections of any kind.
A general model off the shelf knows finance in the abstract and nothing about your firm. It's never read your product shelf, doesn't know your surveillance rules, can't tell a private-wealth mandate from a checking account. We build for the firm you run, not the one in a textbook.
Island Mountain builds the agents and the orchestration that carry the multi-step work start to finish: KYC and AML review with the SAR drafted for a compliance officer to sign, disclosure and regulatory-filing drafting, audit prep, a compliance read on client-facing communications, research pulled and summarized across a stack of sources. It runs on current open-weight models like Kimi K3, ZAI GLM 5.2, Qwen 3.8+, and Nous Hermes 4, served through Ollama or vLLM behind an OpenWebUI front end, or whatever open weights fit the task. All of it governed by our own agentic orchestration and the Woven Security Fabric, so every action carries an identity, an approval, and a receipt, and the credentials expire when the task is done. Client financial data never leaves your network to make any of it happen.
When the work needs a model that speaks your book, we'll build one. Reasoning models and small language models from scratch, trained to orbit your firm's ontology: your product set, your compliance framework, your client book. It learns your terminology and your cadence, the shorthand your desk trades in that no manual ever wrote down.
It air-gaps completely for the books and the deals that demand it, and reaches the cloud only when a task genuinely needs to. That's your call, workflow by workflow.
Then we hand it back. On-site onboarding, SOPs written for your people, the keys dropped into your team's hands so they run and rework the workflows without calling us. Want the upkeep off your IT desk's plate? We offer tiered support sized to the firm, and we plug in wherever that's easiest. This is your firm's legacy. We build it alongside you, and we won't call it done until your people can run it without us.
These aren't chat prompts. They're agentic workflows the orchestration runs start to finish, each step carrying its own identity and receipt, and no client financial data leaves your network.
An agent reads the loan file, flags the risk factors, and pulls the key terms and dates against your lending criteria. Mortgage packages, underwriting memos, credit assessments: the whole application gets processed in the building, never handed to a cloud API.
Run Know Your Customer documentation and Anti-Money Laundering screening as one orchestrated workflow. An agent works the customer file, reads the transaction patterns, flags what looks off, and drafts the SAR narrative for a compliance officer to review. Customer data stays on your hardware, start to finish.
Draft the regulatory filings, the compliance reports, and the audit responses. An agent summarizes a dense rule and maps it against how your institution runs, with examination data processed on-premises and nowhere else.
Read transaction patterns, surface the anomalies, and build fraud investigation summaries an analyst can work from. Sensitive account data never leaves your network to get there.
Draft customer correspondence, disclosure documents, and advisory letters that hold the regulatory language steady across every client touchpoint. An agent writes the first pass; your team signs off.
Summarize market research, read portfolio documentation, and draft investment memoranda. Proprietary strategy and client positions stay in-house, on hardware you own.
Kimi K3, ZAI GLM 5.2, Qwen 3.8+, Nous Hermes 4, and whichever open weight is strongest the month we deploy, served through Ollama or vLLM behind OpenWebUI. This field moves every quarter, so we don't marry a model. We run the one that clears your task today and swap it the day something sharper lands.
When nothing off the shelf understands your book, we build from scratch. Reasoning models and small language models trained on your ontology: your product set, your compliance framework, your client book, the way money and mandates move through your desks. Compact, precise, and nobody else's.
Open weight or custom, every model answers to the same governance: our agentic orchestration and the Woven Security Fabric. Access is scoped, credentials expire with the task, and each action leaves a receipt. The book that demands an air gap gets one; a task reaches the cloud only when it earns it.
The cloud costs every month and transmits NPI every session. The hardware costs once and keeps everything in-house.
| Cloud AI | Island Mountain build | |
|---|---|---|
| Year 1 Cost | $30,000 - $120,000 (50 users) | One-time purchase (quoted to the deployment) |
| Year 3 Cumulative | $90,000 - $360,000 | Electricity only (~$1,200 - $2,400/yr) |
| Year 5 Cumulative | $150,000 - $600,000 | Electricity only |
| Customer Data Location | Cloud provider servers | Your data center. Period. |
| Compliance Risk | NPI transmitted to third party | Zero transmission. Zero risk. |
| Per-Token Fees | $15 - $60 per million tokens | None. Unlimited use. |
| Model Control | Provider decides models and updates | You choose which models to run |
| Banking System Integration | Some platforms offer integrations | Not included. General-purpose AI. |
| Vendor Lock-In | Complete | None. Permissive open-source licenses. |
Knowing the boundaries matters more than knowing the features.
The open-weight models are general-purpose large language models, not finance-specific AI. They're strong at reasoning, analysis, and prose generation, but out of the box they're general tools rather than a vendor's finance-tuned platform, with no fine-tuning on financial datasets, no Bloomberg integration, and no real-time market feeds. The finance-specific model is the one we build for you.
Island Mountain hardware does not connect to core banking platforms, loan origination systems, or CRM tools out of the box. The AI runs through OpenWebUI - a browser-based chat interface. Moving data between your banking systems and the AI is a manual process.
The system does not submit to EDGAR, FFIEC, or state regulatory portals. The AI assists with drafting regulatory filings and compliance documentation - but filing is a manual process through your existing regulatory submission systems.
After the 30-day included support period, your institution is responsible for OS security updates, model updates, and general system maintenance. This is the same maintenance profile as any Linux server in a professional environment. Most managed service providers can handle it.
A GLBA compliant AI server keeps NPI processing under institutional control from day one.
The GLBA Safeguards Rule (16 CFR Part 314) requires financial institutions to develop, implement, and maintain a comprehensive information security program. The FTC's 2023 amendments strengthened these requirements, mandating encryption of customer information in transit and at rest, access controls, and continuous monitoring. Cloud AI processing introduces external infrastructure into the NPI handling chain, creating additional compliance documentation requirements and vendor management obligations.
PCI DSS v4.0 mandates strict controls over cardholder data environments. Any system that stores, processes, or transmits cardholder data falls within PCI scope. When cloud AI processes prompts containing card numbers, transaction details, or customer payment information, that cloud infrastructure enters PCI scope - making PCI DSS AI compliance dependent on the vendor's own certification and creating shared responsibility models that complicate audit responses.
SEC Regulation S-P requires broker-dealers and investment advisers to adopt policies and procedures that address administrative, technical, and physical safeguards for customer records and information. The common thread across all three frameworks: institutional control over data handling infrastructure. Local deployment returns that control entirely to the institution - providing secure AI for SOX compliance, GLBA obligations, and PCI audit requirements simultaneously. No vendor dependency. No shared responsibility models. No third-party data transmission to document and justify.
Disclaimer: This section describes the general regulatory environment regarding AI and financial data protection. It is not legal or compliance advice and should not be relied upon for compliance decisions. Consult qualified compliance counsel or your institution's regulatory advisors for guidance specific to your charter type, jurisdiction, and operational context.
Power & Installation: All Island Mountain systems require a dedicated 208V/30A power circuit (NEMA L6-30R). This is standard in server rooms and data closets. Most financial institutions with an existing server closet already have this infrastructure or can add it for $500-$2,000 through a licensed electrician. The system fits in a standard 4U rack space. Average power draw under typical inference loads is 1.5-2.5 kW.
Yes. Cloud AI transmits non-public personal information (NPI) to third-party infrastructure, creating structural GLBA Safeguards Rule (16 CFR Part 314) compliance risk. The rule requires financial institutions to maintain comprehensive security programs protecting customer NPI - cloud processing introduces vendor dependency that undermines this control. On-premises AI hardware from Island Mountain eliminates third-party transmission entirely.
Island Mountain builds workflows for loan document review, KYC and AML analysis, regulatory reporting, fraud detection support, customer correspondence drafting, and investment analysis. The system runs current open-weight models, and custom models we build for your firm, for analysis and drafting. All processing occurs on your own hardware inside your facility.
Cloud AI subscriptions for financial services platforms typically cost $50 to $200 per user per month. For 50 users, that totals $30,000 to $120,000 per year. Over three years: $90,000 to $360,000 cumulative with no ownership and continued NPI exposure. An Island Mountain build is a one-time purchase, sized and quoted to your workload. Cost parity typically reached within year one.
No. The system is configured and air-gapped before it lands through a web browser. Setup requires racking the server, connecting power and network, and opening a browser. 30 days of hands-on support are included. Ongoing maintenance is standard Linux server administration.
Island Mountain is an engineering practice, not a compliance authority. References to GLBA, PCI DSS, SEC regulations, or related financial compliance frameworks on this page reflect factual descriptions of data handling mechanics - not legal, regulatory, or compliance advice. Consult qualified counsel for compliance determinations specific to your organization and jurisdiction.
Regional bank processing 500 loan applications per month. Every document stays on our servers. GLBA compliance is no longer a question mark.
Scenario: Community BankAir-gapped inference credit union serving 40,000 members. Our members' financial data never touches a cloud API. That's the standard our board demanded.
Scenario: Credit UnionInvestment advisory firm handling $2B AUM. Proprietary research and client portfolio data stay in-house. Cloud AI was never on the table.
Scenario: Investment Advisory FirmNo sales pitch. Tell us about your compliance environment and we will spec the right system for GLBA and SEC requirements.
One conversation. No sales pitch. Tell us about your institution's AI needs and we will spec the right system.
Or call directly: 1-341-441-8740
See all eleven industries we serve or explore: Insurance · Energy & Utilities