Zylon and Microsoft Copilot represent two distinct enterprise AI deployment philosophies.

Zylon positions itself as a private, on-prem enterprise AI platform built for regulated environments, emphasizing full infrastructure ownership, air-gapped capability, and governance-first control. Zylon’s stack can run in cloud VPC, on-prem servers, or fully air-gapped environments, with “zero external dependencies” for runtime and—with full-airgap installation—offline lifecycle management including updates.

Microsoft Copilot, in contrast, is a cloud-based AI assistant suite delivered through the Microsoft ecosystem. For enterprise productivity, the flagship is Microsoft 365 Copilot, which Microsoft documents as operating within the Microsoft 365 service boundary, using Microsoft Graph grounding and Azure OpenAI for processing (not OpenAI’s public services). Microsoft states that prompts/responses and Microsoft Graph-accessed data aren’t used to train foundation models for Microsoft 365 Copilot.

At a practical decision level:

• If your primary objective is broad knowledge productivity across Word/Excel/Outlook/Teams inside a Microsoft-centric environment, Microsoft 365 Copilot is often the most direct path—paired with Microsoft Purview and the Copilot Control System for governance.

• If your primary objective is private AI for regulated industries where data sovereignty and air-gapped feasibility are non-negotiable, Zylon’s on-prem model is structurally aligned—because the organization owns the infrastructure boundary and can operate offline.

What Is Zylon?

Zylon is an on-premise private AI platform for regulated industries, designed so enterprises can deploy generative AI inside their own infrastructure without external cloud dependencies.

Private, on-prem enterprise AI platform built for regulated industries

Across its platform and industry pages, Zylon emphasizes three core constraints common in highly regulated environments:

1. Runs on customer infrastructure (on-prem, private cloud, or air-gapped).

2. Air-gapped capability, including full offline installation and update workflows in its operator documentation.

3. Governance-first design, including audit trails, role-based permissions, and policy-controlled API access.

Key Zylon platform components (as documented)

Zylon’s platform overview describes a modular stack:

• Zylon AI Core: described as the “foundation,” including local LLMs, vector databases, and GPU orchestration, deployable on-prem or air-gapped.

• Zylon API Gateway: described as OpenAI-compatible endpoints with authentication, logging, rate limiting, observability, and governance controls per request.

• Zylon Workspace: a user-facing interface for chat/knowledge base/projects; Zylon documentation describes audit logs as admin-only and exportable.

Zylon’s AI Core page also describes a cryptographically signed, self-contained bundle with “no public repositories” and “no runtime downloads from external model hubs,” which is a meaningful operational detail for environments that prohibit dependency drift or external artifact pulling.

What Is Microsoft Copilot?

“Microsoft Copilot” is an umbrella brand for multiple copilots across products. Enterprise buyers should treat these as separate solutions with different data planes, controls, and compliance overlays—especially when you’re asking “Microsoft Copilot vs private AI” or “on-prem AI vs Microsoft Copilot.”

Key Copilot variants to distinguish

• Microsoft 365 Copilot (primary focus here): AI assistance in Microsoft 365 apps, grounded in Microsoft Graph and governed within the Microsoft 365 service boundary.

• Azure Copilot: an AI assistant for working with Azure services, leveraging the Azure control plane and insights about your Azure environment.

• GitHub Copilot: developer-focused coding assistance delivered through GitHub tooling (important, but out of scope for this post’s primary comparison).

• Security Copilot: security-operations-oriented assistance with Purview audit integrations and security workflows (relevant to governance, but not the core “enterprise productivity copilot”).

Microsoft 365 Copilot’s documented operating model

Microsoft describes Microsoft 365 Copilot as a processing/orchestration engine coordinating:

• LLMs,

• Microsoft Graph content (emails, chats, documents) the user has permission to access,

• Microsoft 365 productivity apps (Word, PowerPoint, etc.).

Microsoft further states:

• User prompts and Copilot responses remain within the Microsoft 365 service boundary.

• It uses Azure OpenAI services for processing (not OpenAI’s publicly available services).

• Prompts, responses, and data accessed through Microsoft Graph aren’t used to train foundation LLMs for Microsoft 365 Copilot.

For enterprise governance, Microsoft documents the Copilot Control System as an integrated framework to secure, manage, and measure Copilot and agent use.

Deployment Model Comparison: On-Prem vs Cloud AI

Enterprise architects evaluating enterprise AI deployment models should start with a simple question:

Where does inference happen, and who owns the infrastructure boundary that encloses prompts, retrieved context, and generated outputs?

On-premise AI deployment (Zylon)

Zylon’s documented deployment stance is that AI Core can be installed and run on:

• on-prem servers,

• private cloud VPC,

• fully air-gapped environments.

Its operator manual details installation modes (online, semi-airgap, full-airgap), including the mechanics of moving large bundles (70–100 GB) to offline environments and operating without internet connectivity on the target machine.

Architecturally, this creates a deployment where:

• The organization owns the compute layer (GPUs/CPUs), storage, and network segmentation.

• The AI system can be placed inside an existing “high side” enclave (including disconnected networks) depending on security posture.

Cloud-native SaaS AI (Microsoft 365 Copilot)

Microsoft 365 Copilot is inherently an online service operating within Microsoft 365. Microsoft describes your tenant sitting inside the Microsoft 365 service boundary and Copilot as a shared service in that boundary.

It also documents a typical prompt flow:

• user prompt → Copilot preprocessing/grounding via Microsoft Graph → grounded prompt sent to the LLM → response returned.

It means:

• You don’t run GPUs.

• You inherit (and must correctly configure) Microsoft 365 identity, permissions, and compliance controls.

• You accept that the platform is delivered as SaaS and changes over time under Microsoft’s release cadence.

Azure-hosted enterprise AI (custom AI in Azure)

A third model sits between SaaS copilots and on-prem private AI: building custom enterprise AI in Azure using services such as Azure OpenAI.

Microsoft’s Azure OpenAI pricing documentation describes token-based pay-as-you-go and provisioned throughput options, plus deployment types including global, data zone (EU/US), and regional deployments.

This is “enterprise AI in the cloud” where:

• You get more architectural control than Microsoft 365 Copilot (networking, isolation patterns, app architecture).

• You still depend on Microsoft’s cloud infrastructure boundary (and its legal/commercial wrapper).

• Sovereignty controls can improve, but do not become identical to “fully on-prem, customer-owned infrastructure.”

Air-gapped feasibility and infrastructure ownership

• Zylon documents full-airgap installation and offline operation on the target machine, enabling zero internet access environments.

• Microsoft 365 Copilot, by design, is part of Microsoft 365 online services; the controls are inside the Microsoft cloud service boundary rather than your physical datacenter boundary.

This is the central architectural implication: on-prem private AI makes your infrastructure the security boundary; SaaS AI assistants make the vendor’s service boundary the operational reality.

Data Privacy & Sovereignty Analysis

For regulated industries, privacy and sovereignty questions often decide the architecture before features do.

Data residency and processing locations

Microsoft documents that Microsoft 365 Copilot’s LLM calls are routed to nearby datacenters and can call into other regions during high utilization. It also states that for EU users it applies “additional safeguards” so EU traffic stays within the EU Data Boundary, while worldwide traffic can be processed in other regions.

Microsoft’s EU Data Boundary documentation describes a commitment to store and process customer data and personal data within the EU Data Boundary for Microsoft enterprise online services including Microsoft 365.

Zylon’s positioning is more direct: it describes deployments where “data never leaves your environment” and “everything stays on your servers,” particularly in government and defense/critical infrastructure contexts.

Cloud dependency and sovereign options

Microsoft’s sovereignty posture includes multiple layers:

• EU Data Boundary as a residency/processing commitment for Microsoft enterprise online services including Microsoft 365.

• “National clouds” and sovereign offerings for certain public sector requirements; Microsoft documentation describes national clouds as physically isolated instances designed for sovereignty needs.

• Microsoft Sovereign Cloud messaging highlights in-country processing and hybrid/disconnected offerings via Azure Local and Microsoft 365 Local, but these remain part of Microsoft’s broader cloud strategy and control plane.

Zylon’s sovereignty stance is operationally simpler: the deployment runs in infrastructure you operate, and Zylon documentation includes fully air-gapped installation workflows and the ability to disable telemetry/metrics.

Model training, prompt handling, and telemetry

Microsoft states in its Microsoft 365 Copilot privacy documentation:

• prompts/responses and Graph-accessed data are not used to train foundation models used by Microsoft 365 Copilot,

• prompts/responses are stored as interaction history,

• admins can manage this data using Microsoft Purview tooling and retention policies.

It also notes that Anthropic models are out of scope for the EU Data Boundary and in-country processing commitments (where applicable), which matters for EU sovereignty-oriented programs.

Zylon’s documentation explicitly discusses observability tooling (crash reporting and anonymous/aggregated usage metrics) and provides a configuration mechanism to opt out.

Regulated-industry implications (finance, healthcare, public sector)

• Financial institutions: Privacy/regulatory pressure includes safeguarding customer information and third-party oversight requirements. The FTC describes GLBA as requiring financial institutions to safeguard sensitive data, and the Safeguards Rule requires an information security program.

• Healthcare: HIPAA’s Privacy Rule restricts how protected health information can be used or disclosed; cloud processing locations and access controls become central.

• Public sector: Procurement often embeds residency, access, and operational independence requirements—driving interest in air-gapped designs or sovereign cloud programs. Microsoft’s EU Data Boundary and sovereign cloud options address parts of this, while Zylon’s model can be deployed without cloud exposure where required.

Compliance & Governance

Compliance outcomes depend on how you implement and operate the system—not just what the platform claims. Still, the platform determines what’s possible.

Regulatory landscape (what matters most for enterprise AI deployment models)

• GDPR is the EU’s core data protection legal framework, as summarized by the European Commission.

• HIPAA governs protected health information use/disclosure in U.S. healthcare contexts.

• GLBA and the Safeguards Rule require safeguarding customer information in financial institutions.

• EU AI Act (Regulation (EU) 2024/1689) establishes a risk-based AI regulatory framework for the EU, including obligations that may affect enterprise deployment and governance programs.

• DORA establishes an EU-wide oversight framework for critical ICT third-party providers and strengthens resilience expectations in the financial sector.

• NIS2 establishes cybersecurity requirements across critical sectors in the EU.

Governance controls: audit logs, RBAC, data isolation, and oversight

Zylon governance controls (documented):

• Audit logs are admin-only comprehensive record and exportable via API; Zylon also documents that audit logging can be disabled depending on policy.

• Knowledge base access is governed by project roles (Viewer/Editor/Admin/Owner).

• Token-based API access exists, with operator/API references for token management.

• Zylon’s industry pages for financial services reference complete audit logs and role-based access control as architectural elements and claim alignment with SOC 2, GLBA, FINRA, and NCUA requirements.

Microsoft 365 Copilot governance controls (documented):

• Microsoft documents the Copilot Control System as a framework of integrated controls for Copilot and agents, covering management controls, security/governance, and measurement/reporting.

• Microsoft states Copilot honors Microsoft Purview sensitivity labels and encryption, and describes how audit/eDiscovery/retention can be applied to Copilot interaction data.

• Microsoft Purview provides audit logs for Copilot and AI applications if auditing is enabled.

• Microsoft Purview retention policies can retain and delete data from Copilot and other AI apps (with Copilot-related message copies stored via Exchange mailbox mechanisms for compliance search).

• Microsoft’s Copilot privacy documentation states that Microsoft 365 Copilot is compliant with existing Microsoft 365 commercial privacy/security commitments, and lists broad compliance offerings including GDPR, ISO 27001, HIPAA, and ISO 42001 as supporting the compliance journey.

Compliance & governance comparison table

The table below summarizes governance capabilities as they relate to AI for the enterprise, especially in regulated contexts. It is based on official vendor documentation and highlights where the control plane naturally lives.

Microsoft governance references come from Microsoft 365 Copilot documentation and Microsoft Purview/Copilot Control System materials.

Zylon governance references come from Zylon platform pages and documentation.

Cost Model Comparison

Enterprise AI cost models differ radically depending on whether cost is driven by per-user subscription, consumption/tokens, or owned infrastructure.

Microsoft 365 Copilot subscription pricing

Microsoft’s enterprise pricing page lists Microsoft 365 Copilot at $30.00 user/month, paid yearly (with a qualifying Microsoft 365 plan).

For many CIO/CTO stakeholders, this is attractive because:

• forecasting is straightforward (seats × $30/month),

• procurement aligns with existing Microsoft licensing motions,

• deployment is largely administrative (identity, app enablement, governance).

But it can become costly at scale if AI is enabled broadly (e.g., thousands of licensed users), regardless of individual usage intensity.

Azure consumption-based pricing for enterprise AI

Azure OpenAI pricing documentation describes:

• standard on-demand pay-as-you-go for input/output tokens,

• provisioned throughput units (PTUs) as a predictable-cost option,

• deployment types such as global, data zone (EU/US), and regional deployments.

For organizations building custom AI in Azure, cost often behaves like:

• usage-driven variable cost (tokens, throughput, associated services),

• plus operational cost of owning the app architecture (monitoring, security, testing, incident response).

Zylon on-prem infrastructure economics

Zylon frames its on-prem model as avoiding per-user fees in some contexts

Zylon’s operator and platform documentation also makes clear that you provision the infrastructure and perform installation/operations workflows (including offline bundle transfer for airgapped installations).

In economic terms, on-prem private AI tends to look like:

• upfront (or amortized) GPU/server investment,

• fixed platform licensing/support (vendor-specific),

• internal operational staffing (SRE/infra/security),

• and then marginal cost per additional user may be lower than SaaS per-user pricing—if concurrency and hardware capacity are sized correctly.

Cost comparison table (subscription vs infrastructure)

This table focuses on the cost mechanics enterprises typically model. Microsoft pricing and Azure pricing model descriptions are from official pages; Zylon “no per-user fees/fixed cost” positioning is from Zylon’s public sector material.

Security Posture & Threat Model Differences

Security posture for enterprise AI is less about “which vendor is secure” (both invest heavily) and more about how the deployment boundary reshapes threats.

External attack surface and cloud exposure risk

Microsoft 365 Copilot is accessed through Microsoft 365 apps and services; Microsoft recommends leveraging existing controls such as Conditional Access and MFA, and documents that Copilot honors those controls.

Because it’s a cloud service, the attack surface includes:

• identity compromise pathways (phishing, token theft),

• misconfiguration and oversharing risks in SharePoint/OneDrive permissions,

• agent/connector supply chain governance (what’s allowed, where it can retrieve data).

Zylon shifts much of this into your infrastructure:

• the external surface is your published endpoint(s),

• segmentation is your network architecture,

• patching and monitoring become your operational responsibility.

Multi-tenant services vs isolated deployments

Microsoft describes logical isolation of customer content within each tenant through authorization and role-based access control mechanisms and cloud encryption controls.

Zylon’s defense/critical infrastructure positioning stresses no cloud exposure, project-level segregation, and “no vendor access,” implying an isolated deployment model with stronger internal control over who can access the system.

In risk terms:

• Multi-tenant cloud designs must prove tenant isolation is effective (and Microsoft documents that as part of its control model).

• Isolated deployments reduce cross-tenant concerns but concentrate responsibility on the enterprise operator (your security program becomes the primary control plane).

Data exfiltration vectors and lateral movement

For Microsoft 365 Copilot, the largest practical risk is often not “Copilot leaking across tenants”—it’s Copilot revealing data that users already have access to because permissions were too broad (a classic oversharing problem). Microsoft explicitly documents SharePoint oversharing controls and Purview DLP approaches to limit Copilot exposure to labeled content.

For Zylon, the risk pattern is different:

• if the system is deployed inside sensitive enclaves, controls must prevent lateral movement between projects/teams, and logs must be strong enough for audits.

• Zylon emphasizes project role permissions and audit logs as operational controls.

Performance & Customizability

Selecting private AI versus SaaS AI often comes down to the combined question:

How much do you need to control the model layer, latency, and system behavior—and how much operational burden will you accept to get that control?

Model flexibility and multi-model orchestration

Zylon documents:

• GPU resource management and orchestration in AI Core, including distributed workload management and concurrency support.

• Platform evolution supporting multiple models in parallel and “use your own AI models” (including fine-tuned models) per its changelog.

Microsoft 365 Copilot is not positioned as “bring any model.” It is positioned as a managed service that orchestrates the model layer inside Microsoft’s boundary, with Microsoft managing the underlying LLMs and stating that they’re hosted in an Azure OpenAI instance exclusively for Microsoft 365 and within the Microsoft 365 service boundary.

For enterprises that want custom models, the Microsoft pattern typically shifts to Azure-hosted AI apps (Azure OpenAI / Foundry tooling), where Azure documents privacy protections for prompts/completions and different deployment types (regional/data zone/global).

Latency: cloud inference vs local inference

• Microsoft 365 Copilot involves a cloud roundtrip to grounding services and LLM processing.

• Zylon, deployed on-prem, can keep retrieval + inference inside local networks, which can reduce latency and eliminate reliance on wide-area network performance—particularly valuable in high-security environments where outbound connectivity is constrained.

Deep customization vs fixed productivity experiences

Microsoft 365 Copilot excels when the target is standardized productivity improvements in Microsoft 365 apps—summarization, drafting, meeting assistance, and work-grounded chat—under a centralized governance framework (Purview/Copilot Control System).

Zylon is designed for deeper customization through its API Gateway and on-prem integration model, including OpenAI-compatible endpoints and policy controls.

Integration & Extensibility

Integration matters because regulated enterprises rarely have “all data in one place.” Core systems (core banking, claims systems, EHRs, case management, document repositories) drive AI value.

Integration with internal systems

Zylon explicitly positions itself as connecting to enterprise systems “without moving data to the cloud,” listing connectors and regulated-industry systems such as banking cores and enterprise platforms.

Its API Gateway is described as OpenAI-compatible endpoints with built-in governance controls and logging, intended for building custom agents and internal applications.

Microsoft 365 Copilot’s integration approach relies on:

• Microsoft Graph grounding (first-party Microsoft 365 data),

• Graph connectors / Copilot connectors to bring external data into Microsoft Graph for grounding,

• agents (including those built with Copilot Studio) governed by admin controls.

Extensibility beyond productivity tools

If your target use case is “AI inside Microsoft 365 apps,” Copilot provides the most native workflow.

If your target use case is “AI inside the enterprise stack,” including custom workflows and industry systems, the choice is:

• extend Microsoft 365 Copilot via connectors/agents where feasible, or

• build custom AI apps on Azure, or

• use an on-prem platform like Zylon where integrations and retrieval happen inside your infrastructure boundary.

Enterprise Use Cases in Regulated Industries

Banking & Financial Services

In banking and credit unions, the dominant constraints are:

• GLBA Safeguards requirements for protecting customer information,

• operational resilience and third-party risk management expectations (DORA in EU contexts),

• auditability for compliance and investigations.

Zylon’s financial services page emphasizes audit logs, RBAC, data residency on your servers, and air-gap capability, and claims alignment with SOC 2/GLBA/FINRA/NCUA requirements.

Microsoft 365 Copilot can be compelling in finance for productivity workflows (summarizing policies, drafting communications, meeting notes), while relying on Purview controls (sensitivity labels, DLP, audit/eDiscovery) to manage exposure.

Where cloud-native AI may be limited: workloads involving highly sensitive datasets that policies forbid from leaving controlled infrastructure boundaries (or where data localization is insufficient on its own).

Healthcare

Healthcare organizations must manage PHI under HIPAA’s Privacy Rule (in U.S. contexts), which limits how PHI can be used or disclosed.

Microsoft documents that Microsoft 365 Copilot supports HIPAA compliance for properly configured implementations (with noted exceptions such as web search queries not covered by the BAA).

Zylon’s positioning is “secure document processing and knowledge retrieval” inside enterprise infrastructure, which can reduce exposure when PHI-heavy datasets must stay in-house.

Where on-prem private AI provides strategic advantage: clinical environments or research enclaves with strict network segmentation, or where institutional policy requires isolated operations.

Public Sector & Government

Public sector constraints often include:

• strong sovereignty expectations,

• transparency and auditability requirements,

• procurement constraints on vendors/supply chain.

Zylon’s government page states “no cloud exposure,” “air-gap capable,” “data sovereignty,” and “every AI interaction logged,” plus “no per-user fees.”

Microsoft’s EU Data Boundary and Sovereign Cloud initiatives can address many residency and operational-control requirements for EU public sector contexts, but they still represent Microsoft-managed cloud/service boundary designs rather than customer-owned on-prem infrastructure.

Critical Infrastructure

NIS2 covers critical sectors and establishes cybersecurity expectations across the EU.

Zylon’s critical infrastructure/defense materials emphasize air-gapped operation, project-level segregation, and audit trails—directly aligned with high-sensitivity engineering and operational contexts.

Microsoft can still be viable for many critical infrastructure firms—especially where Microsoft 365 is already foundational—but risk posture and sovereignty requirements may limit what data can be used in cloud-based copilots.

Strengths & Limitations of Each Platform

A fair enterprise-grade comparison should treat this as a trade-space, not a winner-takes-all.

Microsoft 365 Copilot strengths (enterprise cloud assistant)

Microsoft 365 Copilot’s strengths are most visible when:

• You want AI embedded inside Microsoft 365 workflows.

• You can leverage Microsoft’s governance stack (Purview, Copilot Control System, sensitivity labels, audit/eDiscovery/retention).

• You accept the Microsoft 365 service boundary as your operational boundary and can align residency needs through Multi-Geo/ADR and EU Data Boundary commitments where applicable.

Limitations to plan for:

• It is not “fully on-prem.” It is delivered as an online service.

• Data residency and sovereignty are constrained by Microsoft’s documented processing model (including load routing and specific exclusions such as Anthropic model scope limitations for EU Data Boundary).

• Extensibility into non-Microsoft systems depends on connectors/agents and governance configuration.

Zylon strengths (private AI for regulated industries)

Zylon’s strengths are structurally aligned with private AI for banking, private AI for the public sector, and other high-sensitivity contexts because the deployment can be fully within customer-controlled infrastructure, including air-gapped.

Additional differentiators in documented materials include:

• OpenAI-compatible governed API endpoints (API Gateway) and built-in controls (auth/logging/guardrails).

• Auditing features and project role-based permissions for knowledge bases.

• Installation models supporting offline setups and signed bundles with no public repo dependencies at runtime.

Limitations to plan for:

• You operate infrastructure: GPUs, patching, backups, monitoring, and capacity planning.

• Availability and resilience are your architecture problem (or your integrator’s), not the vendor’s SaaS default.

• Feature breadth for “office productivity assistant inside every app” is not the same category as Microsoft 365 Copilot.

When Microsoft Copilot Makes Sense

Microsoft Copilot (specifically Microsoft 365 Copilot) is often the pragmatic choice when:

Your enterprise is already committed to Microsoft 365, and the highest ROI comes from improving:

• knowledge work drafting and summarization,

• meeting productivity,

• document/search workflows grounded in Microsoft 365 content.

Your regulatory burden is manageable within a cloud-first approach, and you can operationalize governance through Purview and Copilot Control System controls (audit logging, retention policies, sensitivity labels, DLP for Copilot).

Your organization can absorb a per-user subscription model, and the simplicity of seat-based procurement is a feature (not a problem), with Microsoft listing $30/user/month (enterprise annual).

When Zylon Is the Strategic Choice

Zylon is strategically aligned when the environment demands private AI for regulated industries in a literal sense—meaning the AI system runs entirely inside customer-controlled infrastructure.

Common scenarios include:

• Air-gapped environments requiring offline installation and operation, which Zylon documents in its operator manual (full-airgap installation).

• Strict sovereignty requirements where the security boundary must be the organization’s own infrastructure rather than a provider’s cloud service boundary.

• Defense and critical infrastructure contexts emphasizing no cloud exposure and high-assurance auditability.

• Enterprises explicitly avoiding dependency on cloud vendor infrastructure for AI inference and retrieval (including third-party/subprocessor concerns).

Final Recommendation for Enterprise Decision-Makers

Decision-makers (CTOs, CISOs, compliance leaders, enterprise architects) can make this decision more deterministic by aligning three variables:

Risk tolerance and boundary philosophy

If your operating model demands that the infrastructure boundary (compute, storage, network) is owned and controlled by the enterprise—especially for high-sensitivity data—then an on-prem private AI platform is structurally more aligned than SaaS copilots. Zylon is explicitly built around that model with air-gapped capability and on-prem deployment patterns.

If your operating model accepts the vendor’s cloud service boundary and focuses on productivity uplift inside Microsoft 365, Microsoft 365 Copilot is the direct fit—supported by Microsoft’s Purview and Copilot Control System governance narrative.

Regulatory burden and audit posture

In high-audit regimes (financial services, healthcare, public sector), the best platform is the one where you can:

• prove who accessed what,

• retain or delete interaction data on policy,

• control data residency expectations,

• and manage third-party risk.

Microsoft provides a mature compliance toolchain around audit, retention, and eDiscovery for Copilot interactions through Purview.

Zylon provides audit logs and role-based permissions inside a customer-controlled infrastructure boundary.

Cost predictability and scaling economics

• If you want seat-based predictability, Microsoft 365 Copilot’s $30/user/month enterprise pricing is straightforward.

• If you want usage-based cost alignment and custom AI, Azure token/throughput models (including PTUs) are designed for that, but require active FinOps discipline.

• If you want full infrastructure ownership and can amortize GPU investments across high utilization, an on-prem platform can be economically favorable—especially when per-user fees become prohibitive at scale (which Zylon explicitly targets in some vertical messaging).

Regulated-industry suitability table

This table is a decision aid, not a compliance certification. It reflects the architectural fit implied by vendor documentation and mainstream regulatory constraints discussed above.

FAQ

Is Microsoft Copilot suitable for regulated industries?

Microsoft 365 Copilot is designed to operate within Microsoft 365’s privacy, security, and compliance commitments, and Microsoft documents governance tooling through Purview and the Copilot Control System. Suitability depends on whether your regulators and internal policies accept the Microsoft 365 service boundary and your configuration maturity (permissions, labels, DLP, audit/retention).

Can Microsoft Copilot be deployed fully on-premise?

Microsoft 365 Copilot is documented as an online service operating within the Microsoft 365 service boundary and using Azure OpenAI services for processing. That is not the same as a “fully on-prem” deployment where the customer runs inference inside its own datacenter without cloud dependency.

What is the difference between private AI and SaaS AI?

In enterprise terms:

• Private AI means the organization controls the infrastructure boundary (compute/storage/network), often enabling on-prem or air-gapped operation and hard sovereignty. Zylon documents full-airgap installation and on-prem operation modes.

• SaaS AI means the AI runs inside the vendor’s cloud service boundary; governance is achieved through the vendor’s controls and your tenant configuration. Microsoft 365 Copilot is described as operating within the Microsoft 365 service boundary.

Is Microsoft Copilot compliant with GDPR and HIPAA?

Microsoft states Microsoft 365 Copilot is compliant with existing Microsoft 365 commercial privacy/security/compliance commitments, including GDPR and EU Data Boundary, and describes HIPAA as part of its broad compliance offerings (with documented caveats for web search not covered by the BAA).

For HIPAA at the organizational level, compliance still depends on correct configuration and contractual arrangements (e.g., BAA), plus operational controls.

What are the risks of cloud-based AI in banking?

In banking, key risks typically include:

• third-party dependency and concentration risk (addressed in part by DORA’s oversight focus on critical ICT third-party providers),

• data governance failures from oversharing/misconfiguration (a practical risk in any large content estate),

• and policy conflicts where regulators or internal controls require stronger sovereignty than cloud boundaries can provide (organization-specific).

What is the safest AI deployment model for financial institutions?

There is no universal “safest” model. The safest for your institution is the one that matches your:

• data classification policy,

• regulator expectations,

• audit requirements,

• and operational maturity.

If your policy requires full control of infrastructure and strict sovereignty (including air gapping), an on-prem private AI model is structurally aligned. Zylon explicitly targets that model for financial services and sensitive environments.

If your institution can operate safely within Microsoft 365 boundaries, Microsoft 365 Copilot combined with Purview controls can provide a strong governance and auditing story.

How does AI compliance work under the EU AI Act?

The EU AI Act (Regulation (EU) 2024/1689) establishes a risk-based framework with stricter requirements for higher-risk AI systems and obligations for certain AI providers/operators. For enterprises, compliance typically becomes a program combining risk classification, documentation, governance controls, and supplier management—especially when deploying AI in regulated workflows.

Author: Cristina Traba Deza, Product Designer at Zylon
Published: February 2026
Last updated: February 2026

Cristina designs secure, on-premise AI platforms for regulated industries, specializing in enterprise AI deployments for financial services, healthcare, and public sector organizations requiring full data control, governance, and compliance.