Onetrust, LLC

Milap Shah

Subramanian Viswanathan

Kevin Jones

Jonathan Blake Brannon

Kabir A Barday

Jason L Sabourin

Saravanan Pitchaimani

Dylan D Patton-Kuhl

Anand Balasubramanian

Ramana Malladi

Matthew Hennig

Haribalan Raghupathy

Mahesh Sivan

Rahul Shinde

Zara Dana

Sivanandame Chandramohan

Abhishek Upadhyay

Aadil Allidina

Jonathan Lynn

Nicholas Ian Pavlichek

Bryan Patrick Kveen

Eliza Rose Crawford

Tianchen Cai

Methods, systems, and non-transitory computer readable storage media are disclosed for optimizing computer memory usage for lookup lists in computer memory via probabilistic data structures. For example, the disclosed system generates a probabilistic data structure (e.g., a Bloom filter) to represent data in a lookup list including multi-token items by hashing items of the lookup list to sets of bit values in a bit vector. The disclosed system classifies text content in a digital document by utilizing a maximum number of tokens from multi-token items in the lookup list to select and compare sets of sequential tokens in the digital document to the probabilistic data structure. The disclosed system also iteratively reduces the number of tokens in sets of sequential tokens for subsequent comparisons. Furthermore, in some aspects, the disclosed system causes a computing device to modify a digital document and/or database operations based on the classifications.

Generating probabilistic data structures for lookup tables in computer memory for multi-token searching

Various embodiments provide methods, apparatus, systems, computing devices, computing entities, and/or the like for identifying targeted data for a data subject across a plurality of data objects in a data source. In accordance with one embodiment, a method is provided comprising: receiving a request to identify targeted data for a data subject; identifying a first data object using metadata for a data source that identifies the first data object as associated with a first targeted data type for a data portion from the request; identifying a first data field from a graph data structure of the first data object that identifies the first data field as used for storing data having the first targeted data type; and querying the first data object based on the first data field and the data for the first targeted data type to identify a first targeted data portion for the data subject.

Systems and methods for targeted data discovery

In general, various aspects provide methods, apparatuses, systems, computing devices, computing entities, and/or the like for performing data discovery on a target computing system. In various aspects, a third party computing connects, via a public data network, to an edge node of the target computing system and instructs the target computing system to execute jobs to discover target data stored in data repositories in a private data network in the target computing system. In some aspects, the third party computing system may schedule the jobs on the target computing system based on computing resource availability on the target computing system.

Systems and methods for discovery, classification, and indexing of data in a native computing system

In general, various aspects of the present invention provide methods, apparatuses, systems, computing devices, computing entities, and/or the like for generating and managing custom workflows for domain objects defined within microservices. In accordance with various aspects, a method is that comprises: receiving an attribute value for a custom workflow to include in a microservice that corresponds to an attribute defined for a workflow component; accessing mapping data for an attribute; identifying, based on the mapping data, a corresponding field of a workflows table mapped to the attribute; storing a record in the workflows table for the custom workflow with the attribute value stored in the corresponding field for the record to persist the custom workflow in the microservice.

Managing custom workflows for domain objects defined within microservices

In general, various aspects of the present invention provide methods, apparatuses, systems, computing devices, computing entities, and/or the like for integrating third party computing system functionality into a first party computing system by providing a risk management and mitigation computing system configured to analyze a risk of integrating the functionality provided by the third party computing system and facilitating implementation of one or more data-related controls that include performing computer-specific operations to mitigate and/or eliminate the identified risks. For example, the risk management and mitigation computing system can access risk data in tenant computing systems to determine a risk score related to the integration of the third party computing system functionality based on risks determined during prior integrations of the third party computing system functionality by other tenant computing systems. The risk management and mitigation computing system can generate a recommended control when integrating the third party computing system functionality.

Systems and methods for mitigating risks of third-party computing system functionality integration into a first-party computing system

In general, various aspects of the present invention provide methods, apparatuses, systems, computing devices, computing entities, and/or the like for generating and managing custom attributes and corresponding values for domain objects defined within microservices. In accordance with various aspects, a method is provided that comprises: receiving a custom attribute request for a domain object defined in a microservice that comprises a domain object identifier, a custom attribute to add to the domain object, and a value type for the custom attribute; identifying, based on the domain object identifier and the value type, a custom value table for the domain object that comprises placeholder fields; identifying a placeholder field that is available in the custom value table; and storing a record in the attribute schema table for the domain object and the custom attribute comprising mapping data that maps the custom attribute to the placeholder field for the custom value table.

Managing custom attributes for domain objects defined within microservices

Data processing systems and methods, according to various embodiments, are adapted for automatically assessing the level of security and/or privacy risk associated with doing business with a particular vendor or other entity and for generating training material for such vendors. In various embodiments, the systems may automatically obtain and use any suitable information to assess such risk levels including, for example: (1) any security and/or privacy certifications held by the vendor; (2) the terms of one or more contracts between a particular entity and the vendor; (3) the results of one or more privacy impact assessments for the vendor; and/or (4) any other suitable data. The system may be configured to automatically approve or reject a particular vendor based on the assessed risk level associated with the vendor and this information may be automatically communicated to an entity considering doing business with the vendor and/or the vendor itself.

Data processing and scanning systems for assessing vendor risk

An automated classification system may be configured to substantially automatically classify one or more pieces of personal information in one or more documents (e.g., one or more text-based documents, one or more spreadsheets, one or more PDFs, one or more webpages, etc.). The system may be implemented in the context of any suitable privacy compliance system, which may, for example, be configured to calculate and assign a sensitivity score to a particular document based at least in part on one or more determined categories of personal information identified in the one or more documents. The storage of particular types of personal information may be governed by one or more government or industry regulations, which may require particular security measures, storage techniques, handling, etc. for documents based on one or more categories of information contained therein.

Data processing systems for automated classification of personal information from documents and related methods

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Bengaluru, India

Milap Shah