Paypal, Inc.

Alejandro Picos

Deepa Madhavan

Srinivasabharathi Selvaraj

Vladimir Bacvanski

Gaoyuan Wang

Ravi Retineni

Li Cao

Amir Hossein Youssefi

Sudheer Kilari

Meena Nagarajan

Arunkumar Kannimar Ponnaiah

Suhail Sadiq

Devdatta Rivonkar

Rahul Mahendrakumar

Techniques for data lifecycle discovery and management. Data lifecycle discovery platform (DLDP) can identify data of users, data type, and language of data stored in data stores (DSs) of entities based on scanning of data from databases. DLDP determines compliance of DLDP and DSs with obligations relating to data protection arising out of jurisdictional laws or agreements. DLDP generates rules to facilitate complying with and enforcing laws and agreements. DLDP can determine, and present to authorized users, risk scores relating to levels of compliance of the DLDP, associated platforms, or entities, risk indicator metrics, or a privacy health index of the organization associated with DLDP. DLDP can manage user rights regarding data, and access to data in DSs and information relating thereto stored in secure data store of DLDP. DLDP can remediate issues involving anomalies indicating non-compliance. DLDP can utilize machine learning to enhance various functions of DLDP.

Rights management regarding user data associated with data lifecycle discovery platform

Aspects of the present disclosure involve, a customizable system and infrastructure which can receive privacy data from varying data sources for privacy scanning, containment, and reporting. In one embodiment, data received is scanned for privacy data extraction using various data connectors and decryption techniques. In another embodiment, the data extracted is transferred to a privacy scanning container where the data is analyzed by various deep learning models for the correct classification of the data. In some instances, the data extracted may be unstructured data deriving form emails, case memos, surveys, social media posts, and the like. Once the data is classified, the data may be stored or contained according to the classification of the data. Still in another embodiment, the classified data may be retrieved by an analytics container for use in reporting.

System and method for generating privacy data containment and reporting

Techniques for managing erasure of data are presented. In response to receiving a request for erasure of data from a set of data stores, an erasure component can analyze a set of rules and information relating to the user account, including an account status and erasure hold status associated with the user account. The set of rules can be based on legal or contractual obligations applicable to the set of data stores, and can indicate various conditions under which data associated with a user account of a user can be eligible to be erased from the set of data stores or an associated data vault repository. The erasure component can determine eligibility for erasure of all or a portion of the set of data from the set of data stores based on the analysis results. Erasure component can determine erasure eligibility scores to pre-qualify user accounts for erasure eligibility.

Management of erasure or retention of user data stored in data stores

Techniques for data lifecycle discovery and management are presented. Data lifecycle discovery platform (DLDP) can identify data of users, data type, and language of data stored in data stores (DSs) of entities based on scanning of data from databases. DLDP determines compliance of DLDP and DSs with obligations relating to data protection arising out of jurisdictional laws or agreements. DLDP generates rules to facilitate complying with and enforcing laws and agreements. DLDP can determine, and present to authorized users, risk scores relating to levels of compliance of the DLDP, associated platforms, or entities, risk indicator metrics, or a privacy health index of the organization associated with DLDP. DLDP can manage user rights regarding data, and access to data in DSs and information relating thereto stored in secure data store of DLDP. DLDP can remediate issues involving anomalies indicating non-compliance. DLDP can utilize machine learning to enhance various functions of DLDP.

Data lifecycle discovery and management

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New York, NY, United States of America

Alejandro Picos