Understanding Glean's compliance features compared to ChatGPT Enterprise

Glean's compliance features differ from ChatGPT Enterprise in three material areas: deployment flexibility (cloud, single-tenant, and on-premises versus cloud-only), permission enforcement (per-query access checks across 100+ sources versus sandboxed isolation with limited native connectors), and model governance (multi-LLM selection via Model Hub versus a single proprietary model).

Those differences matter because compliance in enterprise AI goes beyond encryption and access controls. It spans where data physically resides, how permissions propagate across every connected system, and whether your organization can switch models without rebuilding its deployment. The scale of the challenge is stark: while 60% of enterprises are actively scaling AI, only 4% are governing it at scale — making the architectural choices behind compliance enforcement a decisive factor in whether AI adoption succeeds or stalls.

This article breaks down the specific compliance and deployment differences between a Work AI platform and a general-purpose enterprise chat tool — covering data security architecture, on-premises options, knowledge graph governance, and vendor lock-in.

What compliance features does a Work AI platform offer that a general-purpose chat tool does not?

A Work AI platform provides per-query permission checks across 100+ source systems, on-premises and single-tenant deployment, model selection through a Model Hub, and overshared-data scanning — none of which a general-purpose enterprise chat tool offers natively. These capabilities are architectural foundations, not features added on top of a cloud-only interface. Permission-aware indexing syncs access controls in real time across 100+ native connectors, so every search result and generated answer respects the same permissions an employee has in the source system.

Contractual zero-day data retention with LLM providers means no customer data persists in any third-party model environment.

A general-purpose enterprise chat tool approaches compliance differently. It inherits permissions from a smaller set of connected applications and processes queries inside a secure sandbox, with data purged within 30 days of account deletion. That model works for organizations with limited integration needs, but it does not scale when your data lives across dozens of systems with distinct access policies. The risk is not theoretical — 97% of AI-related breaches have been linked to a lack of proper access controls, and per-query permission checks across a handful of integrations leave gaps when a financial services team needs enforcement across their HRIS, CRM, document management, ticketing, and code repositories simultaneously.

The core difference comes down to architecture. Glean enforces compliance as a platform capability that scales uniformly across every connected data source — with deployment options spanning cloud, single-tenant cloud, and fully on-premises environments for organizations that require data to stay within their own infrastructure.

A general-purpose enterprise chat tool treats compliance as a feature layer on top of a cloud-only chat interface with a narrower set of integrations. For regulated industries or organizations with strict data residency requirements, that architectural gap determines whether an AI deployment passes procurement review or stalls indefinitely.

How does permission-aware search differ from sandboxed data isolation?

Permission-aware search inherits access controls from every connected source system and enforces them at the moment a query runs — a capability that depends on a properly built permissions structure. If a hospital administrator revokes a contractor's access to patient records at 2:00 PM, the platform reflects that change on the next query.

The system mirrors the permission model of each upstream application, so HIPAA-regulated health data, SOX-governed financial records, and classified government documents all follow the same enforcement pattern without separate configuration.

A general-purpose enterprise chat tool takes a different approach: it processes data inside a sandboxed environment and relies on its own set of native connectors for permission mapping. Organizations with data sources outside those native integrations need to build custom connectors to enforce granular access — adding engineering effort and introducing drift between the source system's permissions and what the chat tool actually enforces. That gap widens as an organization connects more systems.

The risk surface also differs based on how data enters the AI system. When employees manually upload or paste content into a chat interface, sensitive data moves outside its governed source — creating exposure that no downstream sandbox can fully remediate. A platform that indexes content upstream of the language model and checks permissions per query keeps data within its original governance boundary. For a pharmaceutical company managing clinical trial data across 40+ internal systems, per-query permission checks across all of those sources are the difference between passing an FDA audit and explaining why a research associate accessed trial results they shouldn't have seen. Learn more about how AI security features enforce these controls across connected systems.

What deployment options does a Work AI platform provide beyond cloud-only delivery?

A Work AI platform supports three deployment models: multi-tenant cloud with configurable data residency, single-tenant cloud with dedicated infrastructure, and fully on-premises — where all data, models, and processing stay inside the organization's network.

All three options include Glean Search, Glean Assistant, and Glean Agents with full functionality. A general-purpose enterprise chat tool offers cloud-only SaaS delivery with no on-premises option, which disqualifies it for organizations that cannot allow data to leave their network boundary.

Cloud controls for regulated environments

Cloud deployment includes configurable data residency, so organizations choose the geographic region where their index and processing occur. This matters because on-premises deployment now commands 53.8% of the AI governance market, demonstrating that data sovereignty concerns outweigh cloud convenience for most enterprises. Encryption covers data in transit and at rest, and identity federation through providers like Okta and Azure AD enforces authentication without duplicating user management.

Over 100 native connectors pull data from SaaS applications, on-premises file shares, and custom databases while preserving source-system permissions. A financial services firm operating under EU data residency requirements, for example, can run its entire AI deployment within a European data center while maintaining the same connector coverage and feature set available in any other region.

On-premises and air-gapped environments

Single-tenant cloud provides dedicated infrastructure — no shared resources with other customers, full control over the update cadence, and the ability to select a specific data residency region. On-premises deployment goes further: the full platform runs on organization-controlled hardware with no external connectivity required.

Defense contractors subject to ITAR restrictions, intelligence agencies operating in classified environments, and critical infrastructure operators with air-gapped networks all require this model. Glean's on-premises deployment does not sacrifice capability — the same Glean Search, Glean Assistant, and Glean Agents functionality available in the cloud runs within the organization's own data center, including all 100+ connectors and the full governance toolset.

How does a knowledge graph approach strengthen compliance compared to a language model alone?

A comprehensive knowledge graph maps the structural relationships between people, documents, teams, and interactions across an organization — making compliance enforcement structural rather than rule-based.

The Enterprise Graph captures who created each document, which team owns it, who has accessed it, and how it connects to related content across every integrated system. A Personal Graph layers individual context on top, surfacing results weighted by a person's role, recent activity, and collaboration patterns — capabilities that depend on multi-hop reasoning across the full organizational context.

A general-purpose enterprise chat tool queries against files that users upload or connect through its available integrations. It processes those inputs with large context windows — up to 128,000 tokens — but does not maintain a persistent, organization-wide map of data relationships and access patterns. The practical difference: when a compliance officer asks about a sensitive project, the knowledge graph approach returns answers drawn from a governed, permission-checked index of the entire organization's knowledge. The chat tool returns answers drawn only from whatever files were uploaded or connected to that specific conversation.

Consider a 10,000-person manufacturing company managing ISO 27001 compliance across engineering, procurement, and quality assurance. Each department stores documents in different systems with distinct access policies. Glean indexes all of those systems into a unified knowledge graph, enforcing department-level and role-level permissions on every query. An engineer searching for quality specifications sees only the documents their role permits — not because a rule was manually written, but because the graph inherited and enforces those boundaries automatically.

How does model flexibility reduce vendor lock-in and support compliance requirements?

A Model Hub approach gives organizations access to multiple large language models from different providers, selected per use case based on accuracy, latency, cost, and compliance requirements.

A legal team processing contract analysis might choose a model optimized for precision on structured documents, while an internal communications team might prioritize speed and cost efficiency. The ability to swap models without re-architecting workflows means an organization is not locked into a single provider's pricing changes, capability roadmap, or data handling practices.

A general-purpose enterprise chat tool runs on its provider's proprietary models with no option to substitute alternatives. That single dependency means an organization's AI capabilities are tied to one vendor's decisions about model updates, deprecation timelines, and regional availability. The concern is widespread: 44% of enterprises now cite vendor lock-in as a top risk factor in AI deployment. When that vendor changes its data processing terms or discontinues a model version, every workflow built on top of it is affected simultaneously.

Different regulatory environments impose different data handling requirements. Some mandate that no data leave a specific geographic region during processing, which requires models hosted within that region or deployed on local infrastructure. Gartner predicts that over 75% of European and Middle Eastern enterprises will move AI workloads into sovereign solutions by 2030, up from less than 5% in 2025 — underscoring why model flexibility and deployment optionality are converging compliance requirements.

Others require open-weight models running on organization-controlled hardware so compliance teams can independently audit the model's behavior. Regardless of which model an organization selects through the Model Hub, zero-day data retention with LLM providers applies uniformly — no customer data persists in any third-party model environment after the query completes.

How do audit trails and governance controls compare between the two approaches?

Audit logging in a Work AI platform covers every Glean Search query, Glean Assistant interaction, and Glean Agents action across all connected systems — producing a complete record of who queried what, when, and which sources informed the response.

Overshared-data scanning adds a proactive layer: the platform's active data governance identifies documents with permissions broader than intended — a quarterly board deck shared to "anyone with the link," or an HR policy document accessible to contractors — and flags them before they surface in AI-generated answers.

A general-purpose enterprise chat tool provides admin controls and activity logging scoped to the chat interface itself. Administrators can see chat interactions and manage user access within the tool, but that visibility does not extend to permission gaps across the broader SaaS stack. If a confidential sales forecast is over-permissioned in the source document management system, the chat tool has no mechanism to detect or flag that exposure.

For organizations preparing for ISO 27001 certification, HIPAA audits, or SOX compliance reviews, the depth of governance tooling directly affects audit readiness. A CISO at a publicly traded company preparing for a SOX audit needs to demonstrate governance of financial data across every source feeding the AI system, including the AI interface itself.

What should you evaluate when comparing enterprise AI platforms for compliance?

Start with data residency — determine whether your regulatory environment requires on-premises deployment, single-tenant cloud, or multi-tenant cloud with data residency controls.

Confirm that whichever deployment model you select delivers the full feature set. Glean's on-premises deployment, for example, runs the complete platform including Glean Agents without restricting functionality.

Next, map your permission model. Count the number of SaaS tools, on-premises systems, and custom databases that contain sensitive data. Verify that the AI platform enforces per-query access checks across all of them, not a subset of common applications. Organizations frequently discover during procurement that a platform supports permission enforcement only for its native connectors — leaving custom-built integrations outside the governed perimeter. Use concrete evaluation criteria to differentiate real enterprise AI systems from those that only cover a fraction of your data landscape.

Assess vendor lock-in risk by asking whether you can switch language models without rebuilding integrations or retraining workflows. Review the depth of audit and governance tooling against your compliance framework: does the audit trail cover all AI interactions across connected systems, or only activity within the AI interface?

Frequently asked questions

Can a Work AI platform be deployed in an air-gapped environment?

Yes. The full platform — including Glean Search, Glean Assistant, and Glean Agents — runs on organization-controlled hardware with no external connectivity required. Government agencies, defense contractors, and critical infrastructure operators use this deployment model for classified and sensitive environments.

How does zero-day data retention work with third-party LLMs?

Contractual agreements with LLM providers guarantee that customer data is processed in memory and discarded immediately after the query completes. No prompts, responses, or enterprise data persist in any third-party model environment.

Does on-premises deployment limit connected data sources?

No. The same 100+ native connectors available in cloud deployment are available on-premises. Organizations running air-gapped or on-premises configurations maintain full connector coverage without sacrificing integration breadth.

How does model flexibility help with compliance?

Different regulatory frameworks impose different requirements for data handling, model auditability, and processing location. The Model Hub lets compliance teams select models that meet specific regulatory requirements — including open-weight models for independent auditing or regionally hosted models for data residency obligations — without changing the underlying platform configuration.

What happens to permissions when an employee changes roles?

Permission changes propagate in real time. When an employee moves from engineering to sales, the platform immediately reflects their updated access rights from every connected source system. There is no sync delay or manual reconfiguration required.

The differences in deployment flexibility, permission enforcement, model selection, and audit depth determine whether your organization can scale AI adoption within its regulatory requirements. Glean's architecture handles compliance enforcement at the platform level, so AI adoption scales without creating governance gaps. Request a demo to explore how Glean and AI can transform your workplace and see how these compliance capabilities work with your existing infrastructure.