Comparison
Gateco vs Microsoft Purview
Purview secures Copilot. Gateco secures the AI you're building. They protect different surfaces and rarely compete directly.
Bottom line
Purview is a Microsoft 365 governance suite: it applies sensitivity labels and controls Copilot oversharing across SharePoint, Teams and Exchange, which Gateco does not do. Gateco is a permission-aware retrieval layer for the AI your own engineers build, with 12 native vector database connectors, IDP-agnostic principal sync, deny-by-default enforcement at retrieval time, and no Microsoft 365 E5 dependency. If your AI story is Copilot, use Purview. If it is custom code calling a vector database, that surface is out of Purview scope.
| Capability | Gateco | Microsoft Purview |
|---|---|---|
| Custom RAG pipelines (your code) Purview protects Microsoft's Copilot experience, not custom RAG | Yes | No |
| M365 Copilot oversharing protection Purview's core strength. Gateco does not address this. | No | Yes |
| Cross-cloud vector DBs (Pinecone, Qdrant, etc.) Purview covers Microsoft 365 and, in preview, several non-Microsoft SaaS repositories. No vector databases. | Yes | No |
| 12 vector DB connectors | Yes | No |
| Works without Microsoft 365 / E5 license Purview requires E5 + Copilot SKUs for most features | Yes | No |
| Deny-by-default retrieval enforcement Purview applies sensitivity labels at document level; Gateco enforces at retrieval time | Yes | Partial |
| AI Act audit evidence (retrieval-level logs) Purview has compliance logs; not retrieval-specific for custom RAG | Yes | Partial |
| Vendor-neutral (AWS, GCP, Azure, on-prem) Purview is Microsoft-centred. Preview DLP reaches Box, Dropbox, Google Workspace and Salesforce; preview auto-labeling reaches Box and Google Workspace. | Yes | No |
| Python / TypeScript SDK | Yes | No |
| IDP-agnostic principal sync Purview principals = Azure AD only | 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. Purview doesn't govern custom code. Gateco does.
The tell: if your engineers are writing Python or TypeScript to call a vector DB, Purview is not in scope for that surface. Gateco is.
The hyperscaler bundling question
Microsoft, AWS, and GCP are each building retrieval security capabilities into their stacks:
- Microsoft: Purview + Microsoft Search/Loop for M365 Copilot-specific scenarios.
- AWS: S3 Access Grants + Bedrock Guardrails, strong for AWS-native Bedrock RAG.
- GCP: Sensitive Data Protection + Vertex AI RAG Engine access controls, strong for Vertex-native scenarios.
The pattern: each hyperscaler's solution is excellent within their own stack and stops at the cloud boundary. If your vector data lives across AWS and GCP, or your IDP is Okta, or your team uses Pinecone and Qdrant, the hyperscaler solution leaves gaps. Gateco is vendor-neutral and cross-cloud by design. That's the wedge that stays open even as the hyperscalers invest.
Protect the AI you're shipping
Gateco works alongside your existing cloud security stack, including Purview for the M365 layer.