A COGNOSCIBLE COMPUTING ENGINE FOR AUTOMATIC DECISIONING ON SENSITIVE, CONFIDENTIAL AND PERSONAL DATA

Abstract

A computer-implemented apparatus, system, and method is disclosed for protecting sensitive data. A cognoscible computing engine is multi-layered and multi-pathed. It includes features for handling different data formats, including structured, semi-structured, and unstructured data. Features are included to support near real-time processing at scale with high accuracy. Applications include redacting or masking sensitive data to comply with data privacy and security standards.

Specification

WE CLAIM:
1. An apparatus for protecting sensitive data, comprising: a multi-layered and multi-pathed computing engine, including:

a data source identifier including a parser and data extractor to classify ingested data, identify metadata, schema, and database types for structured data, semistructured data, and unstructured data types, the data source identifier indexing and storing the extracted data;

a detection module including a semantic rules engine and an ensemble of artificial intelligence models configured to perform context based classification;

an identification module receiving detected data attributes output by the detection module and invoking identification markers to generate sensitive data identification information;

a confirmation module to confirm the sensitive data identification information utilizing the context information to associate data elements and confirm the presence of sensitive data elements; and

a data tagging and classification module tag sensitive data.

2. The apparatus of claim 1, wherein the detection module is configured to perform entity classification, determine and classify entity protocols, and apply the semantic rules engine to generate an output for the ensemble of artificial intelligence models configured to perform context-based classification.

3. The apparatus of claim 2, wherein the computing engine comprises control access instructions to vector encapsulate functions to be applied to a raw data file.

4. The apparatus of claim 3, wherein the control access instructions include functions to transform a metadata configuration file to a metafile matrix to configure a workflow.

5. The apparatus of claim 4, wherein the control access instructions include instructions for global access control, enterprise access control, system access control, and open authentication access control.

6. The apparatus of claim 1, further comprising a configurable UI to configure graphical user interfaces, browsers, authorization levels and different reporting use cases.
The apparatus of claim 1, wherein the detection module analyzes parts of speech, noun phrases, verb phrases and dependency parsing signals generated by a natural language processing framework.

The apparatus of claim 4, wherein the detection module comprises a context builder to build a context for recognized named entity based at least in part on the control access instructions, role detection, action-intent detection, and rule-based persona.
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The apparatus of claim 8, where the context builder prepares context training data to train an Al/machine model.

The apparatus of claim 9, where the context builder trains an AI/ML model to perform context prediction.

The apparatus of claim 8, where the semantic rule engine identifies a set of rules for the ensemble of artificial intelligence models based at least in part on entity classification and entity protocol determination and classification.

The apparatus of claim 1, wherein the apparatus tracks data lineage between ingested data and distributed data.

The apparatus of claim 1, wherein the detection module analyzes the syntactic, semantic and morphological elements to be incorporated to identify entities.

The apparatus of claim 1, wherein the parser is configured to parse data sources including video and GIF files, audio and speech files, PNG and JPEG image files, textual files, and database tables.

The apparatus of claim 1, further comprising a masking engine to mask tagged sensitive data.

The apparatus of claim 1, wherein the tagging and classification module identifies sensitive data to be redacted.

The apparatus of claim 1, wherein the apparatus is configurable to mask sensitive data.

The apparatus of claim 1, wherein the apparatus is configurable to redact sensitive data.

The apparatus of claim 1, further comprising a compliance engine to monitor compliance with at least one data privacy, security, or protection protocol.

The apparatus of claim 1, wherein the computing engine protects at least one of on-premises data and cloud application data.

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