Comparison
Gateco vs Microsoft Purview
Purview classifies and protects data across Microsoft 365 and beyond. Gateco decides what the AI you're building may retrieve, for each user or agent, at retrieval time. They work at different layers and rarely compete directly.
Bottom line
Purview is Microsoft's data security, governance and compliance suite. It applies sensitivity labels, prevents data loss across Microsoft 365, devices and a growing set of other apps, and controls Copilot oversharing across SharePoint, Teams and Exchange. Gateco does not do DLP or Copilot oversharing control, and it does not write sensitivity labels to your files. Gateco is a permission-aware retrieval layer for the AI your own engineers build: it supports 12 vector and search backends, syncs principals from the identity provider you already have, enforces deny-by-default at retrieval time, and has no Microsoft tenant requirement. If your AI story is Copilot, use Purview. If it is custom code calling a vector database, Microsoft's documented option is Purview label enforcement inside Azure AI Search, in preview and within one Entra tenant. Gateco enforces in front of the vector stores you already run.
| Capability | Gateco | Microsoft Purview |
|---|---|---|
| Custom RAG pipelines (your code) Azure AI Search can enforce Purview sensitivity labels at query time for RAG apps (preview, one Entra tenant). Foundry IQ knowledge bases run on Azure AI Search; for indexed content they use the same index-stored permission and label metadata with query-time enforcement, not a separate Purview path. Gateco enforces in front of the vector stores you already run. | Yes | Partial |
| M365 Copilot oversharing protection Purview's core strength. Gateco does not address this. | No | Yes |
| Classification engine and automatic sensitivity labeling Purview's core competency: a mature classification engine with sensitive information types and trainable classifiers. Auto-labeling with the same sensitive information types and classifiers is in preview for Box and Google Workspace. Gateco does not infer labels from content. Its classification suggestions are rule-based and take effect only when an administrator accepts them; a source-sync pipeline can apply an administrator-set default classification to the documents it imports. We decide access, not sensitivity. | No | Yes |
| Data loss prevention across the estate Purview DLP covers data at rest, in use and in motion across Microsoft 365, Windows and macOS devices, on-premises file shares and non-Microsoft cloud apps, with Box, Dropbox, Google Workspace and Salesforce in preview. Available actions vary by app. Inline, it can audit or block sensitive prompts and files sent to AI apps such as ChatGPT and Gemini, through Edge for Business or a network integration. Gateco does not inspect or block data leaving your organisation; it decides what retrieval may return. | No | Yes |
| Compliance suite (eDiscovery, insider risk, records, audit) Purview is a governance estate; these are whole product areas Gateco does not enter. Gateco's audit surface is its own control plane: policy changes, provisioning, connector configuration and a record of every retrieval decision. It is not an eDiscovery or records-management product. | No | Yes |
| Cross-cloud vector DBs (Pinecone, Qdrant, etc.) Purview's query-time label enforcement runs inside Azure AI Search (preview), which ingests labels from Azure Blob Storage, ADLS Gen2, SharePoint and OneLake. | Yes | No |
| 12 vector and search backends | Yes | No |
| Works without a Microsoft Entra tenant Microsoft places Purview accounts under a Microsoft Entra tenant, and query-time Purview label enforcement requires the search service and the user to be in the same Entra tenant. | Yes | No |
| Deny-by-default retrieval enforcement Azure AI Search enforces Purview labels at query time, in preview and within one Entra tenant, and returns an error rather than partial or unfiltered results when labels can't be evaluated. Gateco enforces deny-by-default at retrieval time in front of the vector stores you already run. | Yes | Partial |
| Per-retrieval allow/deny audit trail Purview Audit records DLP activity, and in preview Azure AI Search writes a Purview audit entry for each document returned by an elevated-read query. Gateco records every retrieval decision, allow and deny, with its inputs. | Yes | Partial |
| Vendor-neutral retrieval enforcement (AWS, GCP, Azure) Purview Data Map connects to sources in AWS, Google Cloud and on-premises, such as Amazon S3, Google BigQuery and SQL Server. Its documented query-time label enforcement runs inside Azure AI Search, in preview. | Yes | No |
| IDP-agnostic principal sync Purview label enforcement at query time takes Microsoft Entra user tokens from one tenant; guest accounts and cross-tenant queries aren't supported. | Yes | No |
Different lanes
If your AI story is M365 Copilot, Purview is the right tool for controlling what Copilot can see in your SharePoint, Teams, and Exchange data. It's built into the Microsoft stack and optimised for that surface area.
If you're shipping an AI product (a customer-facing assistant, an internal knowledge base chatbot, a compliance research tool), you're building your own RAG pipeline.
If you run Microsoft 365 and your problem is knowing where sensitive data lives, stopping it leaking, or meeting discovery obligations, Purview is the right tool and we will not pretend otherwise. Microsoft is extending that reach beyond Microsoft 365: DLP for Box, Dropbox, Google Workspace and Salesforce is in preview, auto-labeling is in preview for Box and Google Workspace, and network data security can audit or block sensitive prompts and files sent to AI apps such as ChatGPT and Gemini. Gateco answers a narrower question: may this user or agent, named by your application on each request, retrieve this specific content now? Gateco enforces the user's access at retrieval time, deny by default, in front of the vector stores you already run. The two work at different layers: Purview classifies, labels and protects data; Gateco decides what each requester may retrieve into the model's context.
The hyperscaler bundling question
Cloud platforms are building retrieval security into their own stacks. On Microsoft's, Azure AI Search offers document-level access control: security filters (generally available) and, in preview, Microsoft Entra ACLs and RBAC scopes, SharePoint ACLs and Purview sensitivity labels. For SharePoint content, a remote SharePoint knowledge source keeps permissions and labels in SharePoint and retrieves through the Copilot Retrieval API. In Microsoft's own words, "Query-time enforcement evaluates the caller's Microsoft Entra claims against the permission metadata that's already stored in the index," and permission changes in the source system reach search results only after that metadata is synchronized to the index. Evaluation happens at query time; the metadata it evaluates is synced.
The pattern on Microsoft's side: enforcement is strongest inside its own index and identity system. Purview label enforcement in Azure AI Search, for example, requires the search service and the user to share one Entra tenant and does not support guest or cross-tenant queries. If your retrieval runs outside a single vendor's index and identity boundary, you need enforcement that travels with the stack you built. Gateco is vendor-neutral and cross-cloud by design, and enforces at retrieval time in front of the vector stores you already run.
Protect the AI you're shipping
Gateco works alongside your existing cloud security stack, including Purview for the M365 layer.