Cloud AI assistants process your client data, financial records, and MNPI on a vendor's servers. ibl.ai deploys air-gapped or on-premise on infrastructure you own — model-agnostic, with autonomous compliance and risk agents and an audit trail you control.
Cloud-hosted SaaS AI assistants are genuinely capable. Tools like ChatGPT Enterprise, Microsoft Copilot, and Gemini offer frontier models, polished interfaces, and fast adoption — and most now offer SOC 2 attestation and no-training options for business data.
But for a financial institution, where client data, financial records, and material non-public information are involved, the structural fact remains: that data transits and is processed on the vendor's cloud. You rely on the vendor's controls and perimeter, not your own.
ibl.ai is built for institutions that need to own the stack. Deploy air-gapped or on-premise so data never leaves your perimeter, run any model, and operate autonomous compliance, risk, and KYC/AML agents with an audit trail you own. 1.6M+ users across 400+ organizations.
Cloud AI assistants are the category of cloud-hosted SaaS AI products — ChatGPT Enterprise, Microsoft Copilot, Gemini, and similar — that financial institutions adopt for drafting, research, summarization, and analysis. They are delivered as managed services on the vendor's infrastructure, typically with SOC 2 attestation, enterprise admin controls, SSO, and options to exclude business data from model training. They are easy to adopt, broadly familiar to employees, and backed by frontier models.
| Criteria | Cloud AI Assistants | ibl.ai | Verdict |
|---|---|---|---|
| Where Client Data Is Processed | On the vendor's cloud — prompts and data transit and are processed on vendor infrastructure | Entirely within your perimeter — data and MNPI never leave infrastructure you own | ibl.ai |
| MNPI & Confidential Handling | Reliance on vendor controls and contractual no-training terms rather than your own perimeter | MNPI and confidential client data stay sovereign inside your controlled environment | ibl.ai |
| Telemetry & Metadata Egress | Vendor receives usage telemetry and metadata even with training opt-out | Zero telemetry — no data or metadata leaves your environment | ibl.ai |
| Criteria | Cloud AI Assistants | ibl.ai | Verdict |
|---|---|---|---|
| Air-Gapped Deployment | Not available — requires connectivity to vendor cloud endpoints | Fully supported — runs disconnected with no external API calls | ibl.ai |
| On-Premise / Any Cloud | Cloud-hosted on the vendor's infrastructure only | On-premise, private cloud, or any public cloud — your choice | ibl.ai |
| Time to Deploy | Fast — provision and roll out in days with minimal IT work | Structured onboarding; production deployment typically within 4–8 weeks | competitor |
| Criteria | Cloud AI Assistants | ibl.ai | Verdict |
|---|---|---|---|
| Source Code Ownership | None — managed SaaS; the vendor owns and controls the platform | Full source code delivered to your institution; you own it permanently | ibl.ai |
| Model Flexibility | Tied to the vendor's model family — limited or no swap for sensitive workloads | Model-agnostic — Claude, GPT, Gemini, Llama, Mistral, or open-source on-prem | ibl.ai |
| Model Quality for General Drafting | Excellent — direct access to the latest frontier models | Excellent — route to the same frontier models, plus open-source for sensitive tasks | Tie |
| Criteria | Cloud AI Assistants | ibl.ai | Verdict |
|---|---|---|---|
| Audit Trail Ownership | Logs and telemetry controlled and stored by the vendor | Complete, owned audit trail on every agent action, stored in your environment | ibl.ai |
| SEC / FINRA / SOX Recordkeeping | Application-level logs; recordkeeping depends on vendor-controlled retention | Owned, immutable records supporting SEC, FINRA, SOX, GLBA, PCI-DSS supervision | ibl.ai |
| Compliance Attestations | SOC 2 attestation and enterprise certifications from the vendor | Inherits your infrastructure's compliance posture; SOC 2, GLBA, PCI-DSS-aligned | Tie |
| Criteria | Cloud AI Assistants | ibl.ai | Verdict |
|---|---|---|---|
| Pricing Model | Typically per-seat subscription — cost scales with every user added | Flat-fee licensing — one price regardless of headcount | ibl.ai |
| Cost Across a Large Workforce | Per-seat pricing compounds significantly across thousands of employees | Flat-fee model holds cost flat as adoption grows firm-wide | ibl.ai |
| Long-Term TCO | Perpetual subscription subject to vendor price changes | Code ownership means no perpetual per-seat fees after the initial license | ibl.ai |
Cloud AI assistants process prompts on the vendor's infrastructure. For a financial institution handling client records and material non-public information, that means relying on vendor controls rather than your own. ibl.ai runs air-gapped or on-premise so data never leaves your perimeter.
Many trading floors, restricted networks, and regulated workloads cannot use cloud-hosted AI. The cloud assistant category has no fully air-gapped or on-premise-owned option. ibl.ai is built for disconnected and on-premise deployment without architectural compromise.
Cloud assistants control the audit logs and retention. Under SEC, FINRA, and SOX supervision and recordkeeping expectations, control over the record matters. ibl.ai gives you a complete, owned audit trail on every agent action, stored in your own environment.
Cloud assistants tie you to one vendor's model family. ibl.ai is model-agnostic — route general drafting to frontier models and run open-source models on-premise for the most sensitive compliance, risk, and advisory workloads.
Per-seat pricing compounds when AI rolls out to thousands of employees across the firm. ibl.ai uses flat-fee licensing, so your AI cost does not climb every time a new desk or branch adopts it.
Cloud assistants are primarily chat interfaces. ibl.ai deploys autonomous agents for compliance, risk, advisory, KYC/AML, and operations — agents that reason, plan, and execute multi-step workflows against your systems with a logged, owned audit trail.
Deploy ibl.ai fully disconnected — air-gapped data centers, restricted networks, on-premise, or any cloud you control. No internet connectivity required and no external API calls. Client data, financial records, and MNPI stay inside your perimeter at all times.
ibl.ai delivers the full platform codebase to your institution. You own it, inspect it, modify it, and run it forever — with or without an ongoing vendor relationship. Your AI platform becomes an owned asset, not a per-seat subscription you rent.
Run any model — Claude, GPT, Gemini, Llama, Mistral, or fine-tuned open-source models on-premise. Route general drafting to frontier models while keeping compliance, risk, and advisory workloads on owned models that never call out to a vendor.
ibl.ai is agentic, not chat-first. Deploy autonomous agents for compliance, risk, advisory, KYC/AML, and operations that reason over context, integrate via MCP and APIs, take actions, and complete multi-step workflows — every action logged in an audit trail you own.
Every agent action is logged at the infrastructure level, stored in your environment, and owned by you. This supports SEC, FINRA, SOX, GLBA, and PCI-DSS recordkeeping and supervision — records under your control, not a vendor's retention policy.
One price, unlimited users. ibl.ai's flat-fee model keeps AI cost predictable as adoption grows across desks, branches, and back-office teams. At firm scale this delivers roughly 85% lower cost than per-seat cloud-assistant pricing.
ibl.ai serves 1.6M+ users across 400+ organizations, including learn.nvidia.com, Kaplan, and Syracuse University — delivered with full code ownership and roughly 85% lower cost than per-seat SaaS. Production-grade from day one.
Inventory current cloud-assistant usage across desks and functions — drafting, research, compliance review, KYC/AML. Map use cases to ibl.ai's agent architecture and define your target environment (air-gapped, on-premise, or private cloud) with compliance, supervision, and recordkeeping requirements.
Provision your target environment and deploy the ibl.ai codebase inside your perimeter. Configure your chosen models — frontier providers for general work, open-source on-premise for sensitive workloads. Establish SSO, RBAC, and data isolation aligned to your org and regulatory structure.
Build priority use cases as autonomous agents — compliance, risk, advisory, KYC/AML, operations — rather than chat prompts. Configure MCP and API integrations with your core banking, CRM, surveillance, and recordkeeping systems. Enable the owned audit trail on every agent action.
Deploy to a defined pilot group. Validate agent behavior, integration reliability, and audit-trail completeness against SEC, FINRA, and SOX recordkeeping needs. Run compliance and risk review on the logged records, then iterate on agent configurations before full rollout.
Execute firm-wide rollout with change management. Decommission redundant cloud-assistant seats. Operationalize governance using ibl.ai's owned audit trail and admin controls, and establish ongoing supervision and retention processes under your own infrastructure.
Customer financial data and core banking records cannot be exposed to third-party cloud infrastructure under GLBA and examiner expectations on data handling and third-party risk. Cloud assistants process prompts on the vendor's servers, which conflicts with strict data-control mandates.
ibl.ai runs air-gapped or on-premise so customer data never leaves your perimeter, with an owned audit trail supporting GLBA, SOX, and examiner supervision requirements.
Advisors handle client PII, portfolio data, and MNPI that fall under SEC and FINRA recordkeeping and supervision rules. Cloud assistants put that data on vendor infrastructure with vendor-controlled retention, complicating supervisory recordkeeping.
Autonomous advisory agents run inside your perimeter with a complete, owned record of every interaction — supporting SEC and FINRA recordkeeping and books-and-records obligations.
Underwriting, claims, and policyholder data are highly regulated and often subject to state data-handling rules. Reliance on a cloud vendor's controls introduces third-party risk for sensitive policyholder information.
On-premise deployment keeps policyholder and claims data sovereign, while autonomous agents automate underwriting and claims review with an audit trail you own and retain.
Trading desks handle MNPI and operate under FINRA supervision and SEC recordkeeping obligations, often on restricted networks. Cloud assistants cannot run air-gapped and place sensitive data and audit logs in vendor control.
Air-gapped deployment lets ibl.ai operate on restricted desks with MNPI staying inside your perimeter and an owned, supervision-ready audit trail for FINRA and SEC.
Fast-scaling fintechs face per-seat AI costs that compound across engineering and operations, plus PCI-DSS and partner-bank data-handling obligations that limit what can sit on third-party cloud AI.
Flat-fee licensing controls cost as headcount grows, while owned, model-agnostic deployment keeps cardholder and partner-bank data PCI-DSS-aligned and inside your perimeter.
Compliance and risk teams need defensible, owned records and the ability to run sensitive analysis on models that don't call out to a vendor. Cloud assistants control the logs and tie analysis to one model family.
Autonomous compliance and risk agents run on owned models inside your perimeter, producing a complete audit trail you control for SEC, FINRA, SOX, and internal supervision.
Schedule an assessment to see how ibl.ai can replace your current platform with a solution you fully own and control.