The Tranquil Data™️ Enterprise Edition

The Tranquil Data™️ Enterprise Edition is software that our users deploy into their platforms. Accessed via APIs and/or acting as an intermediary for databases, the software captures context about users and data, and uses the policies that our users write to validate or enforce requirements. Records or fields are redacted as-required by policy, and the results are audited to provide details about why each decision was made.

Data Context Materializes as a Graph Data-Set

The Context Graph connects knowledge about data (at the record-version) level, to anyone associated with that data, the actors and services allowed to work with that data, and the policies and schemas that dictate valid interaction. This does not replace existing databases, nor is it a general knowledge graph. It’s the minimal knowledge needed to answer questions about how to use and share data correctly, stored in its own database. It tracks version and evolution, origin, and lineage across services. APIs let our users define how data from different sources forms context (identity, tags, sensitivity, categorization, etc.), and define the attributes and relationships for anyone associated with data.

A Core Engine Drives Context and Evaluation

The Tranquil Data™️ Context Engine (a container image) builds and optimizes the context graph in real-time, either via API or as a transparent database intermediary. Consensus is used to change any of the “building block” components of the system, like policy or schema, and the history of those components is captured in the ledger. The full Context Graph is streamed out, while a pre-computed version is cached, stored, and coordinated in a Key-Value model. Policies are expressed in a powerful enterprise standard or in one of several layered syntaxes, each of which support querying context in real-time. When an action is taken on a record, the engine efficiently provides the related context, decides whether the action is permitted, audits (with deep detail) why that decision was made, and adds the resulting knowledge back to the context graph.

At-Scale Services Enforce Real-Time Compliance

As multiple engines are provisioned and connected, a logical service forms that shares context to scale throughput and offer resiliency. Via APIs users can export datastore access across services and locales, and set different properties on each engine instance within a service, so that context forms under specific policies and with localized properties. This addresses Segmentation requirements ranging from the need to provide different views of the same data to different users to tracking and enforcing data residence and sovereignty requirements.