Self Hosted AI for Financial Services
Self hosted AI for financial services is a private LLM deployment running inside your own environment: no client data transits third-party APIs. This is the only architecture that satisfies SEC Regulation S-P's customer information safeguarding requirements and FINRA Rule 3110's supervisory controls by design. Tools like Ollama, vLLM, and Open WebUI run on your infrastructure, under your control, with full audit logging that produces the supervisory records FINRA examiners will ask for.
This page is for RIAs, financial planners, accounting firms, and fintech compliance officers who need AI for document review, client research, and workflow automation, but cannot route client financial data through external cloud APIs without triggering Regulation S-P, FINRA supervisory obligations, or PCI-DSS cardholder data scope.
why cloud AI is a compliance problem for financial services#
The financial services industry operates under regulatory requirements that make "use a cloud AI tool with a good data processing agreement" an insufficient answer. The obligations are structural, not contractual.
SEC Regulation S-P: the customer information safeguarding obligation#
SEC Regulation S-P requires broker-dealers, investment advisers, and other covered entities to protect the security and confidentiality of customer records and information. The 2025 amendments, effective December 3, 2025 for larger covered institutions and June 3, 2026 for smaller ones, strengthened those requirements materially (SEC/FINRA, 2025). Covered institutions must now maintain a documented service provider oversight program, implement written policies for third-party access to customer information, and notify customers of breaches within 30 days of discovery.
Using a cloud AI tool to process client financial data creates a service provider relationship. That relationship must be documented, supervised, and controlled under the amended rule. If you cannot produce documentation of your oversight program for that AI vendor, you are out of compliance, regardless of what the vendor's terms of service say about security.
FINRA Rule 3110: supervision requirements extend to every AI tool your firm uses#
FINRA's 2026 Annual Regulatory Oversight Report identified GenAI supervision under Rule 3110 as a primary examination focus (FINRA, December 2025). Rule 3110 requires member firms to establish and maintain a supervisory system reasonably designed to achieve compliance with applicable securities laws and FINRA rules. FINRA examiners are now asking firms to produce documentation of their supervisory controls for AI tools that touch client data.
The specific questions they ask: what data does the AI access, who approved it, how is it monitored, what safeguards prevent inappropriate use. If your firm uses a commercial AI tool and cannot answer those questions on paper, you have a Rule 3110 gap. A self hosted deployment makes that documentation tractable, because every component is inside your environment and every access decision is logged.
PCI-DSS v4.0.1: cardholder data scope doesn't stop at your payment processor#
As of March 31, 2025, all PCI DSS v4.0.1 requirements apply to AI-based payment systems with no exemptions (PCI Security Standards Council, 2025). Requirement 3 (stored cardholder data), Requirement 7 (access control), and Requirement 10 (audit logging) all extend to AI inference systems that process or have access to cardholder data. For fintech companies and payment-adjacent firms, the AI inference layer is now explicitly in PCI scope. Running inference inside your own environment, with the access controls and audit logging that PCI requires, is the only architecture that satisfies this without creating a new entry point into your cardholder data environment.
the 30-day breach notification clock that most firms aren't ready for#
The amended Regulation S-P requires firms to notify affected customers within 30 days of discovering a breach. That clock requires you to know, within 30 days, exactly what customer data was exposed and how. If your AI tool is a cloud service, your ability to meet that window depends entirely on how quickly your vendor can characterize the incident and what data they were retaining on your behalf.
Firms that run AI inference inside their own environment can characterize an incident from their own logs. They are not waiting on a vendor's timeline. That is a meaningful operational difference when the 30-day clock is running.
Regulatory penalties for global financial institutions skyrocketed 417% in H1 2025 versus H1 2024, totaling $1.23 billion across 139 enforcement actions (Fenergo, 2025).
how self hosted AI satisfies the regulatory architecture#
what "private" actually means at the infrastructure layer#
"Private AI" is used loosely in the market. Some vendors use it to mean an enterprise tier with data processing agreements. Others use it to mean a hosted single-tenant environment on their infrastructure. Neither of those is what we mean.
In a self hosted deployment, the LLM inference engine runs on hardware inside your environment: your data center, your private cloud, your on-premise server room. Client financial data is sent to your inference server, processed by your model, and returned to your application. No data leaves your network. No API call is made to an external service. There is no third-party infrastructure in the inference path.
That architectural distinction is what actually matters for Regulation S-P, FINRA Rule 3110, and PCI-DSS. It is also the distinction that 44% of enterprises cite as the top barrier to LLM adoption. In financial services, that barrier is a compliance mandate, not an operational preference (Kong Enterprise AI Report, 2025).
data residency: where the model runs, where the data stays#
Data residency is deterministic with self hosted AI. The model runs in a specific physical or virtual location that you control. Client data processed by the model stays in that location. Your data governance policies, retention schedules, and access controls apply to the AI inference layer exactly as they apply to any other system in your environment. There is no ambiguity about where data is stored or who can access it.
audit logging and supervisory controls that satisfy FINRA examiners#
Every deployment we build includes comprehensive audit logging: every query, every response, every user, every access event, timestamped, attributed, and retained in a format your compliance team can export for examiner review. This is not optional. FINRA Rule 3110 examinations will ask for it, and the audit log is how you answer.
It demonstrates what the AI was used for, who used it, what data it accessed, and that no anomalous usage occurred. Firms that can hand this documentation to an examiner are in a materially different position than firms that have to contact their AI vendor and wait for records.
service provider oversight: why on-premise eliminates the vendor risk#
Amended Regulation S-P requires a documented service provider oversight program. When the AI runs on your own infrastructure, administered by your team or by us under a services agreement you control, that program is tractable: you are the service provider, or you have a direct contractual relationship with the party that manages your infrastructure. There is no third-party AI vendor with its own retention policies, security posture, and government subpoena exposure inserted into your data flow.
what we deploy for financial services firms#
client document analysis and research automation#
AI-assisted analysis of client financial documents, including statements, tax filings, contracts, and offering memoranda, runs inside your environment. The model extracts key information, flags anomalies, and produces structured summaries for advisor review. Document content never leaves your network.
compliance workflow automation: SAR drafting, AML flag review, audit prep#
Compliance workflows carry two burdens at once: they are high-volume and they are sensitive. SAR drafting, AML transaction flag review, and audit preparation all involve client data that cannot leave your environment. We build these workflows inside your perimeter: the AI handles document generation and initial analysis, the compliance officer reviews and approves, and the full workflow is logged. Nothing routes through an external API.
internal knowledge bases for policy and procedure Q&A#
Your compliance manuals, regulatory guidance interpretations, and internal policies can be indexed into a retrieval-augmented generation system that answers staff questions and cites the source document. New staff get accurate answers from your actual documentation, not from a commercial AI trained on generic financial content. The knowledge base runs on your hardware and is updated by your compliance team on your schedule.
advisor copilots for portfolio research and client reporting#
Advisors using AI for portfolio research and client report generation work with a copilot that runs inside your environment and has access only to the data you authorize. Research queries, client data references, and generated content are all logged. The advisor's workflow speeds up; the compliance trail is maintained.
tech stack#
Every component runs inside your environment. None of these make outbound calls to cloud services.
inference layer: Ollama (RIA and advisory firm scale), vLLM (high-throughput fintech)#
Ollama is our default for registered investment advisers, advisory firms, and accounting practices. It handles document-centric workloads well, runs on existing server hardware without a dedicated GPU cluster, and is straightforward for an IT team to operate and maintain. For fintech companies and broker-dealers with high document volume or many concurrent users, vLLM handles the throughput that Ollama is not designed for. Both run entirely on your infrastructure.
interface: Open WebUI with LDAP/AD auth and role-based access controls#
Open WebUI is the staff-facing interface. It integrates with LDAP/Active Directory for authentication, supports role-based access controls, and gives your IT or compliance team full control over user management. Access is granted by role: advisors, compliance staff, and administrators see different capabilities and different data. Everything is logged.
RAG and retrieval: LangChain, ChromaDB, pgvector, on your documents#
The retrieval layer indexes your firm's actual documents: client files, compliance manuals, regulatory guidance, research memos. We build retrieval-augmented generation pipelines using LangChain for orchestration and either ChromaDB or pgvector for vector storage, depending on your database environment. The choice between them usually comes down to whether your firm already runs PostgreSQL. Either way, the index runs on your hardware and is updated by your team.
orchestration: n8n self hosted for workflow automation inside your network#
For compliance workflow automation, including SAR drafting, AML review, and audit prep pipelines, we use n8n deployed as a self hosted instance inside your network. n8n orchestrates multi-step workflows between your AI system, your document management platform, and your compliance tools. No data leaves your environment at any step.
security: network segmentation, full audit logging, encrypted storage at rest#
The inference layer is network-segmented from general office traffic. Storage is encrypted at rest. All system activity is captured in an audit log formatted for FINRA examination and Regulation S-P documentation. There is no external telemetry and no data pathway to any system outside your perimeter.
who this is built for#
registered investment advisers under SEC examination scrutiny#
RIAs under active SEC examination programs face direct scrutiny of their AI usage and data handling practices. A self hosted deployment gives your CCO a clear, documentable answer when examiners ask how client data is protected in your AI workflows, because the answer is in your own audit logs, not in a vendor's security whitepaper.
accounting and CPA firms handling tax and financial data#
Accounting firms handle client tax returns, financial statements, and personal financial data under obligations that vary by state and engagement type. AI for document review, data extraction, and report preparation is useful. It is only viable if the architecture keeps client data inside the firm's environment, which cloud AI tools do not.
fintech companies in PCI-DSS scope#
Fintech companies with payment card data in scope face explicit PCI-DSS requirements that now extend to AI inference systems. Self hosted AI keeps your inference layer inside your cardholder data environment, where your existing PCI controls apply to it directly.
broker-dealers with FINRA supervisory obligations#
Broker-dealers need a documented supervisory control framework for any AI tool that touches client data. A self hosted deployment produces that documentation by design: every component is inside your environment, the access log is yours, and the supervisory record is generated automatically.
FAQ#
Can financial advisors use AI without violating SEC Regulation S-P?
Yes, if the AI runs inside the firm's own infrastructure. When the model inference runs on the firm's hardware with no third-party API in the data path, the safeguarding obligation is satisfied by the firm's own controls. When the AI routes client data through a cloud API, the firm has created a new service provider relationship that must be separately documented and supervised.
Does using cloud AI expose RIAs to Regulation S-P liability?
Yes, materially. A commercial cloud AI tool that processes client data is a service provider under the amended rule. If you cannot produce documentation of your oversight program for that vendor, you are non-compliant. The 30-day breach notification requirement makes this worse: your ability to meet that window depends on your vendor's incident response timeline, not your own.
How does self hosted AI infrastructure satisfy FINRA Rule 3110 supervisory requirements?
FINRA examiners are asking firms to document what data the AI accesses, who authorized it, how it is monitored, and what safeguards are in place. A self hosted deployment produces those answers from your own logs: every access is recorded, the audit trail is under your records management policy, and there is no vendor to contact when an examiner has questions.
What is the cost of deploying self hosted AI for a financial services firm?
Deployments typically range from $10,000 to $45,000 for initial build-out, depending on firm size, workflow scope, and infrastructure requirements. RIAs and advisory firms with 10 to 50 staff and two to four workflows tend toward the lower end. Fintech companies and broker-dealers with high-throughput requirements and multiple workflow automations sit at the higher end. Every engagement starts with a scoped assessment.
What AI tools are safe to use with client financial data?
Any tool where the model inference runs entirely inside your own infrastructure, with no client data transiting a third-party server, satisfies the architectural requirements of Regulation S-P, FINRA Rule 3110, and PCI-DSS. The tools themselves are the same ones used in cloud deployments: LLMs, retrieval systems, workflow automation. The difference is where they run and who controls the access logs.
start with a compliance and infrastructure assessment#
If your firm uses any AI tool that processes client financial data, or if your CCO or outside counsel has flagged your current AI setup as a compliance risk, the practical first step is to map what you actually have.
We will document your current AI usage and data flows, identify where client financial data leaves your controlled environment, and scope what a self hosted deployment requires for your specific workflows and regulatory obligations.
Request an assessment, or contact us directly to discuss your regulatory environment and timeline.
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