Every data source. One governed door. Any AI.
Connect Snowflake, S3, Azure, SharePoint, Salesforce and your databases once. Clarista applies access control, PII masking and lineage on top, then serves the data to AI tools and people through a single governed MCP connection.
The result: your data teams govern in one place, your security team audits one door instead of a hundred credentials, and your AI tools get the context they need without ever holding a raw connection string.
Three layers, in the order your CISO would draw them:
Warehouses, lakes, databases, document stores and SaaS apps plug into the fabric with scoped, vault-held credentials. No credentials are ever handed to AI tools.
Role-based data access control, column-level PII masking, row filters, and query logging with lineage. Defined once, enforced everywhere.
AI tools and users query governed views through the Clarista MCP server. Every query is logged, masked and mapped to the person or app that asked.
Data access control by role and purpose, not by credential sharing. PII masking at the column level, so an analyst tool sees j***@a****.com while the payroll app that needs the real value gets it. Lineage on every query, so when an auditor asks "what data did this AI app read in March", the answer is a report, not an investigation.
Evidence maps to the same controls as the rest of Clarista: SOC 2, HIPAA, and NIST, exportable in one click from the compliance monitoring layer.
Every AI tool that touches your data directly is another credential to rotate, another egress path to monitor, another audit conversation. A governed MCP server inverts that: the tools authenticate to Clarista, Clarista authenticates to your data, and the security team reviews one integration. Our guide to running Claude MCP in the enterprise covers the setup end to end, and the agent governance white paper covers what happens when the thing on the other side of the door is an agent rather than a person.
They overlap and vendors blur them. Short version: data virtualization gives you a unified query layer without moving data. A data mesh is an organisational model where domains own their data as products. A data fabric is the connective layer that makes sources usable and governed in one place, and it is the only one of the three that answers the question AI forces: "who let the model read that?" Our older comparison of data fabric vs data mesh goes deeper on the strategy choice.
No. The fabric runs in your environment, credentials stay in your vault, and AI tools receive governed query results, never raw connections or bulk exports.
Column-level masking by policy. Sensitive fields are masked, tokenised or blocked per role. The masking is applied inside the fabric, before any AI tool or user sees the data.
Anything that speaks MCP: Claude, Claude Code, and other MCP-capable clients, plus your own apps through the same governed interface. Non-MCP tools can use governed APIs.
It includes a virtualized access layer, but adds what virtualization alone lacks: enforced access control, PII masking, lineage, and an AI-ready MCP interface with per-query logging.
Standard connectors (Snowflake, Postgres, S3, SharePoint, Salesforce) are same-day. Governance policies are defined once and apply to every new source you add.
Thirty minutes. Bring your messiest data landscape and your CISO.
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