Record-Level Redaction Starts with Data Context
Static engines only pattern-match. Tranquil Data provides you with rich APIs and UIs to automate complex redaction tasks in real-time.
Context Unlocks Fine-Grained Redaction
Most redaction systems use pattern-matching to identify specific fields, column-definitions that apply to all entries, or AI in an attempt to surface sensitivity. None of these addresses the fact that redaction should be applied differently based on the context of data. Tranquil Data applies redaction record-by-record, understanding that even within a schema or document, data represents different conditions that may not be clear from the data itself, and uses context to apply the right rules even when data has been tokenized or anonymized. This means that one row may be dropped based on a user’s age, another may be redacted based on a B2B contract, and a third may be allowed based on a consent.
Seamless Support for Data Transform
For real-time data transform flows, Tranquil Data supplies a single API call to ask whether a given record is valid for an asserted purpose. For instance, if a transform flow is taking data from several internal systems and making that data available for Advertising to run a campaign, you embed in that flow a single call to Tranquil Data for each record asking “can I use this record for the stated campaign?” The caller gets back a yes or no, an explanation of why that decision was made, and (if the answer is no) a redacted version of the record that would be allowed. The decision is audited, so developers don’t have to implement any other logic to know they’re compliant.
The streamlined User Interface provided for non-technical roles to make sure they’re working with the right data for any given purpose.
User Interface for Business Documents
It’s common for Advertising, Marketing, Sales, Finance, Analytics, and other departments to ask for bulk exports of data into documents like CSVs that they know how to work with. Today, those teams then spend days or weeks going back-and-forth with Legal trying to understand which subsets of those documents can be used for a specific task. With Tranquil Data, you just pick the purpose that you want to assert, and drag-and-drop the CSV. The document is evaluated line by line, and a field-level redacted document is returned that’s known to be valid for the purpose. The decision is audited, and there’s an API to embed this feature into your existing tools.
Actionable Decisions and Audit
Whether you’re calling the single-record API or the bulk-document API, or using the UI to filter a document, the decisions are all being audited in the same format to a common stream. This gives you two business advantages. First, the the audit trail turns into BI to share internally with stakeholders and externally with partners and prospects. Second, simple explanations are provided like “this was allowed based on a contract” or “this was redacted based on the user’s age.” This is a starting-point for teams to understand where specific blockers are repeating, and how the business may want to evolve.