# Air-Gapped AI for Financial Services — On Infrastructure You Own

> Source: https://ibl.ai/resources/alternatives/air-gapped-ai-finance-alternative


*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.

## About Cloud AI Assistants

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.

**Strengths:**
- Capable frontier models with strong reasoning and drafting quality
- Polished, familiar interfaces that drive fast employee adoption
- SOC 2 attestation and enterprise admin controls including SSO
- No-training options that exclude business data from model improvement
- Minimal IT overhead — provision and roll out in days
- Continuous vendor-managed model updates with no upgrade work

**Limitations:**
- Client data, financial records, and MNPI transit and are processed on the vendor's cloud — you rely on vendor controls, not your own perimeter
- No fully air-gapped or on-premise-owned deployment option
- Model lock-in — limited or no ability to swap in open-source or alternative models for sensitive workloads
- Typically per-seat pricing that compounds as adoption grows across the firm
- Audit logs and telemetry are controlled by the vendor, not owned by you
- Limited data-residency control — a concern under regulator expectations on third-party data handling, supervision, and recordkeeping

## Comparison

### Data Residency & MNPI

| 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 |

### Deployment

| 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 |

### Ownership & Model Choice

| 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 |

### Compliance & Recordkeeping

| 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 |

### Cost at Scale

| 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 |

## Why ibl.ai

### Air-Gapped and On-Premise by Design

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.

### Complete Source Code Ownership

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.

### Model-Agnostic, Including Open-Source

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.

### Autonomous Agents for Finance

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.

### Owned Audit Trail for Recordkeeping

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.

### Flat-Fee Licensing

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.

### Proven at Scale

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.

## Migration Path

1. **Discovery and Compliance Mapping** (Week 1–2): 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.
2. **Infrastructure Provisioning and Deployment** (Week 2–4): 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.
3. **Agent and Integration Configuration** (Week 3–6): 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.
4. **Pilot Rollout and Validation** (Week 5–8): 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.
5. **Production Cutover and Governance** (Week 8–12): 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.

## FAQ

**Q: Does our data ever leave our infrastructure?**

No. ibl.ai is deployed entirely within your perimeter — air-gapped, on-premise, or in a private cloud you control. Client data, financial records, and MNPI are processed inside your environment. There is no telemetry, no metadata egress, and no external API calls unless you explicitly choose to route a workload to an external provider.

**Q: Can it run air-gapped?**

Yes. ibl.ai is purpose-built to run fully disconnected, with no internet connectivity required. The platform, the models you select, and the audit trail all operate inside your air-gapped environment. This lets you bring AI to restricted trading desks and segregated networks where cloud-hosted assistants are categorically prohibited.

**Q: How does it support SEC/FINRA/SOX recordkeeping?**

Every agent action is logged at the infrastructure level and stored in your own environment, giving you a complete, owned, retainable record. Because you control retention and access, the audit trail supports SEC and FINRA recordkeeping and supervision, SOX controls, and GLBA and PCI-DSS data-handling obligations — without depending on a vendor's logging or retention policy.

**Q: Is pricing per-seat?**

No. ibl.ai uses flat-fee licensing — one price regardless of how many employees use it. Cloud AI assistants are typically priced per seat, so cost compounds as adoption spreads across desks, branches, and back-office teams. At firm scale, the flat-fee model typically delivers roughly 85% lower cost than per-seat SaaS pricing.

**Q: Are cloud AI assistants ever the right choice for a financial institution?**

Sometimes, yes. For non-sensitive, low-risk drafting and research, cloud assistants are capable, fast to adopt, and familiar — and many offer SOC 2 attestation and no-training options. The structural concern is sensitive workloads: client data and MNPI processed on a vendor's cloud rely on vendor controls rather than your own perimeter. Many firms use both, reserving ibl.ai for sovereign and regulated workloads.

**Q: Which models can we run, and can sensitive workloads stay on owned models?**

ibl.ai is model-agnostic. You can route general drafting to frontier models like Claude, GPT, or Gemini, and run open-source models such as Llama or Mistral on-premise for sensitive compliance, risk, advisory, and KYC/AML workloads. Sensitive analysis can run entirely on owned models with no external calls.

**Q: What can the autonomous agents actually do for a financial institution?**

ibl.ai deploys agents for compliance, risk, advisory, KYC/AML, and operations. They reason over context, integrate with core banking, CRM, surveillance, and recordkeeping systems via MCP and APIs, take actions, and complete multi-step workflows — not just generate text. Every action is captured in an audit trail you own for supervision and review.

**Q: Is ibl.ai production-ready at the scale a financial institution needs?**

Yes. ibl.ai serves 1.6M+ users across 400+ organizations, including learn.nvidia.com, Kaplan, and Syracuse University — delivered with full source code ownership and roughly 85% lower cost than per-seat SaaS. The platform is model-agnostic, deployable air-gapped, and production-grade from day one.