Data Prism MCP server

Data Prism

<!-- mcp-name: io.github.AindriuB/data-prism -->

A privacy layer between MCP clients and enterprise APIs.

Status: the walking skeleton and every slice through S9a are built, with 19 Maven submodules (pom.xml:24-42; 20 Maven projects in the reactor counting the root pom-packaged aggregator itself) and a passing test suite. The privacy engine, correlation and consistency findings, parallel mTLS connectors, embedded Hazelcast identity cache and read budget, an OAuth2 resource server with session-derived PrivacyContext, audit and metrics are all real and exercised end to end. The standalone server is the primary deployment surface; the Spring Boot starter is the embedded option. A one-command local Compose quickstart also exists: see "Try it" below. Not built: the re-identification operator surface (deferred past V1 by decision, see docs/architecture.md#decisions-worth-knowing), the Elasticsearch connector and its search tools, and the append-only audit sink with hash-chain verifier (a file/SLF4J sink exists; the append-only sink is deliberately deferred). Two MCP tools ship today, getentitycontext and compareentitysources — the other two named in the design review, searchentitydata and describeentitymodel, are not yet built. See docs/plan/PLAN.md for what is open.

The problem

An organisation wants an LLM to investigate live business data spread across several systems. Giving the model direct API access is not acceptable: those APIs carry personal and confidential data, each system represents the same entity differently, and raw identifiers let anything downstream correlate across sessions.

The obvious fix — redact everything sensitive — destroys the investigation. Once three systems' names for one person are all [REDACTED], the model cannot tell whether it is looking at one person or three.

What Data Prism does

It sits between the two and does two things that are easy to confuse:

It makes identity consistent. One subject gets one synthetic identity across every source, derived deterministically from (scope, subject, namespace, algorithm version, key) — never random, never stored in plaintext, and reproducible without the cache. The same person in three systems reads as one person to the model.

It leaves the data inconsistent, and says so. If those three systems disagree about a name, the answer carries a finding that says they disagree. The platform never makes enterprise data look cleaner than it is. That distinction is the point of the project:

Identity representation becomes consistent. Underlying data inconsistencies

become more visible, not less.

Pseudonyms are scoped. The same person in two different investigations gets two different synthetic identities, so nothing correlates across cases by accident.

What it is not

Not an API gateway, not an ETL platform, not a master-data system, not an identity provider, and not an entity-resolution engine — correlation requires a key the sources already share, behind a documented SPI. It carries no business domain: no Customer, Taxpayer or Employee type exists outside the example application.

It is not anonymisation. Under GDPR Art. 4(5), pseudonymised data is still personal data. Sending Data Prism output to a third-party model is still processing, and still needs a lawful basis, a DPIA, and a transfer mechanism where the provider is outside the EU. The platform reduces exposure; it does not remove the obligation.

Try it

The fastest way to see a real MCP call answered by the real privacy engine — no local JDK, no Maven install, one command:

docker compose up --build

Shortened. The full README is on GitHub.