Bank of America Corporation

Elijah Clark

George Albero

Edward Lee Traywick

Scot Lincoln Daniels

Rick Wayne Sumrall

Emanuel David Guller

Konata Stinson

Jake Michael Yara

Maharaj Mukherjee

Elizabeth R Liuzzo

Elizabeth Swanzy-Parker

Michael Ogrinz

Michael Beveridge

Benjamin D Moores

Madhavi Mendu

Caleb G Mann

Ariel Fontaine Hill

Michael B Jones, Jr

Hannah Kaplan

Andrew Sides

A computing platform may receive, from a plurality of IoT information sources, historical information associated with a user. The computing platform may generate, based on the historical information, a user specific policy, defining information distribution rules for the user. The computing platform may receive, from an information collection system, a request for information. The computing platform may identify whether or not the request for information requests personal information of the user. Based on identifying that the request for information requests the personal information of the user, the computing platform may identify whether or not the requested personal information violates the user specific policy. Based on identifying that the requested personal information does not violate the user specific policy, the computing platform may obscure the requested personal information, and send, to the information collection system, the obscured personal information.

Permission based information transfer based on internet of things (IoT) insights

Detecting malicious/spoofed Universal Resource Locators (URLs) and/or files in received electronic mail (i.e., email) and, in response to detection, automatically taking responsive measures to prevent interaction/access to the malicious/spoofed URLs and/or files and/or limit, network-wide, the exposure to the malicious/spoofed URLs and/or files. Valid URLs and/or files are assigned a token, such as an alpha-numeric passcode or the like and the assigned token is verified upon receipt of the email by the intended recipient. In the event that the token cannot be verified or a token is not received for a URL or file, the automated responsive actions ensue.

Malicious universal resource locator and file detector and response action engine

A system is provided for machine-learning based identification and filtering of electronic network communication. In particular, the system may continuously monitor and pull electronic communications data from one or more networked computing systems in an enterprise environment. Based on the electronic communications data, the system may use machine learning algorithms to generate a database of associations between one or more users and one or more topics of interest. The system may then output one or more recommendations to one or more users for transmitting communications associated with the one or more topics of interest. In this way, the system may improve the efficiency of communications received and transmitted within a network.

System for machine-learning based identification and filtering of electronic network communication

Aspects of the disclosure relate to electronic messaging security. A computing platform may push, to a client device, an electronic messaging plugin configured to validate steganographic modifications, embedded in electronic messages by the computing platform. The computing platform may intercept an electronic message directed to the client device. The computing platform may generate a steganographic modification to the electronic message. The computing platform may embed, in the electronic message, the steganographic modification, resulting in a modified electronic message. The computing platform may send, to the client device, the modified electronic message, where the client device may configured to validate, using the electronic messaging plugin, the steganographic modification prior to launching the modified electronic message at the client device.

Dynamic steganographic embeddings for message threat detection

Aspects of the disclosure relate to real-time detection of anomalous content in a transmission of textual data. A computing platform may monitor, in real-time and via a computing device, a transmission of textual data from a user device. Then, the computing platform may scan, via the computing device, a content of the textual data. The computing platform may then perform, via the computing device and based on the scanning, textual analysis of the scanned content. Subsequently, the computing platform may detect, in real-time and based on the textual analysis, an anomalous pattern indicative of secure enterprise information. Then, the computing platform may trigger, via the computing device, one or more security actions to prevent the transmission of the secure enterprise information.

Real-time detection of anomalous content in transmission of textual data

Aspects of the disclosure relate to computing hardware and software for performing uniform document updates. A computing platform may receive, from a user device, a document change request. The computing platform may authenticate authority of a user of the user device to perform the document change request. Based on authenticating the authority of the user of the user device to perform the document change request, the computing platform may identify storage locations at which documents that are affected by the document change request are located. The computing platform may access the documents at each of the storage locations. The computing platform may scan the documents to identify locations, within the documents, of changes to be made, which may include identifying the locations based on enterprise-adopted change tags. The computing platform may write the changes to the documents at the identified locations based on the enterprise-adopted change tags.

Recursive data and electronic signature document updater

Aspects of the disclosure relate to real-time management of access controls. A computing platform may detect, via the communication interface, a trigger related to an activity of an enterprise user over a network. Then, the computing platform may retrieve, via the communication interface and from the network device, data associated with the activity. The computing platform may then compare, via the computing device, the retrieved data with a central tendency for activities with the network device. Subsequently, the computing platform may identify, based on the comparing, a deviation from the central tendency. Then, the computing platform may adjust, dynamically in real-time and in response to identifying the deviation, an access control or multiple access controls for the enterprise user to the network device.

Real-time management of access controls

When a system tries to access a network (e.g., another system, an application, data, or the like) at least two-factor authentication may be used to validate the system. At least one authentication factor may include utilizing authentication credentials of the entity or system accessing the network. At least a second authentication factor may include using an environment hash of the system, which is a representation of the configuration (e.g., hardware, software, or the like) on the system trying to access the network. The environment hash may be compared to hash requirements (e.g., authorized environment hashes, unauthorized environment hashes, or the like) to aid in the validation. The system may only access the network when both the authentication credentials and the environment hashes meet requirements.

System for prevention of unauthorized access using authorized environment hash outputs

Arrangements for detecting anomalies and dynamically generating a response are presented. In some examples, attribute data including a plurality of source elements may be received. The source element data may be received from a plurality of source computing systems. The attribute data, including the source element data, may be analyzed using machine learning techniques to identify any anomalies in the attribute data. If an anomaly is detected, a notification may be generated including data associated with the anomaly, a user associated with the anomaly and the like. The notification may be transmitted to a computing device for display. In some examples, if an anomaly is detected, data associated with the anomaly may be compared to pre-stored rules to determine whether a pre-stored rule applies to the identified anomaly. If a pre-stored rule applies, an instruction or command may be generated and transmitted to one or more source computing devices or systems.

Multi-source anomaly detection and automated dynamic resolution system

Methods, systems, and computing platforms for data communication are disclosed. A computer-data communication based network, including receiving a set of virtual nodes each with a data payload may include an originating node attribute, an infosec data attribute, an behavioral data attribute, a biometric enterprise attribute and at least one data element associated with the originating node attribute. A machine learning module may learn from across multiple of collection points to determine control triggers and control durations. A user anomaly collector/module may be configured to identify an unusual or anomalous usage of an application.

Method and system for data communication with anomaly detection

Aspects of the disclosure relate to physiological sensor-based monitoring and control systems. A computing device may determine a physiological measurement. Then, the computing device may compare the physiological measurement with one or more baseline values to determine whether the physiological measurement is anomalous with respect to the one or more baseline values. When the physiological measurement is determined to be anomalous with respect to the one or more baseline values, the computing device may execute access control on a device to prevent access by a user to one or more systems, applications, resources, or the like.

User monitoring and access control based on physiological measurements

Methods, systems, and computing platforms for data communication are disclosed. Exemplary implementations may: electronically process with a machine learning controller; electronically process the data payloads in the network with deep machine learning; and real-time adjusting of a plurality of network infosec controls associated with the originating node attribute based on the infosec control attribute.

Data management system

A virtual environment system for validating executable data using authorized hash outputs is provided. In particular, the system may generate a virtual environment using a virtual environment device, where the virtual environment is logically and/or physically separated from other devices and/or environments within the network. The system may then open a specified set of executable data within the virtual environment and perform a set of commands or processes with respect to the executable data. If the system determines that the executable data is safe to run, the system may generate a hash output of the executable data and store the hash output in a database of approved executable data. In this way, the system may securely generate a repository of authorized hashes such that the system may ensure that only safely executable code is processed by the computing systems within the network environment.

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Charlotte, NC, United States of America

Elijah Clark