Abstract: 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.

Abstract: Aspects of the disclosure relate to energy optimization. A computing platform may receive an event processing request. The computing platform may identify parameters of the event processing request. The computing platform may input the parameters into a global energy optimization model, to identify an edge computing system at which to process the event processing request. The computing platform may route the event processing request to the edge computing system along with commands to process the event processing request using an energy mix identified by a local energy optimization model, corresponding to the edge computing system, which may cause the edge computing system to: input the parameters of the event processing request into the local energy optimization model to identify the energy mix, and process, using the energy mix, the event processing request.

Abstract: Aspects of the disclosure relate to adjustable control thresholds. A computing platform may monitor employee activity within an enterprise organization. The computing platform may generate, for each employee of the plurality of employees, employee control thresholds. The computing platform may generate, based on an indication from an employee computing device, modified employee control thresholds associated with the employee. The computing platform may generate, based on the indication, test data including a subset of the employee activity associated with the employee.

Abstract: A method enabling processing electronic transactions received at the ATM when network connection with the central server is disrupted. The method may enable processing each electronic transaction received using an alternative data routing pathway. The alternative data routing pathway may leverage one or more edge devices within close proximity to the ATM to serve as a proxy for transmitting data to the central server. The one or more edge devices may be enabled to carry electronic transaction data from electronic transactions processed locally at the ATM to a location where a network connection between the one or more edge devices and the central server may be established. When in a location including network connection, the one or more edge devices may transmit the electronic transaction data to the central server.

Abstract: A computing platform may receive an event processing request. The computing platform may identify, by inputting first communication parameters of the event processing request into a secure communication model, communication validation information for the event processing request. The computing platform may embed, using one or more tonal bits of an audio communication corresponding to the event processing request, the first communication validation information, wherein embedding the first communication validation information generates a secure event communication. The computing platform may send the secure event communication to a recipient user device, where the recipient user device may be configured to verify the secure event communication based on the first communication validation information.

Abstract: Aspects related to an amalgamation platform providing concealed detection of code-passing using steganography are provided. An amalgamation platform may train a scoring engine to generate suspicion scores for code snippets and an amalgamation engine to generate execution scenarios for the code snippets. The platform may embed code in base code of a network using steganography. The platform may use the steganographic code to detect transmission of a code snippet. The platform may generate a suspicion score for the code snippet. The platform may update the scoring engine based on identifying the suspicion score satisfies a threshold. The platform may generate execution scenarios for the code snippet. The platform may determine a match between an execution scenario and a malicious code scenario. The platform may identify the code snippet as malicious and update the amalgamation engine based on the match. The platform may initiate security actions based on the match.

Abstract: Systems, devices, and methods for user authentication are described. A security platform may authenticate a user based on a machine learning model created using historical user behavior. The user behavior may correspond to user interaction with and/or operation of a computing device. The user behavior may correspond to, for example, historical user purchase patterns. Applications include user authentication for online and offline purchases, access to computing resources, and/or access to physical locations.

Abstract: Apparatus and methods for determining transaction transmission fidelity are provided. A sender may receive a transaction log including multiple transactions in two or more rows and two or more columns. The transaction log may be encrypted. Each of the transactions may be tagged. Each row and column of the transaction log may be hashed. The encrypted transaction log and the hash values may be transmitted to a recipient. The recipient may decrypt the transaction log and hash each row and column of the decrypted transaction log. The hash values may be compared and if there is a difference, a report or alert may be generated.

Abstract: A computing platform may train an identity verification model to identify authentication questions to validate an identity of an individual. The computing platform may detect a call of the individual. The computing platform may input, into the identity verification model and while the call is paused, information of the call, the individual, and/or a second individual, which may cause the identity verification model to output the authentication questions. The computing platform may send, while the call is paused and to a user device of the individual, the authentication questions. The computing platform may receive, while the call is paused and from the user device, authentication information comprising responses to the authentication questions. The computing platform may validate, while the call is paused, the authentication information. Based on successful validation of the authentication information, the computing platform may cause the call to resume.

Abstract: A computing platform may generate a digital watermark configured to verify an identity of an initiator of a communication session. The computing platform may send, to a first user device, a digital watermark signal, where the digital watermark signal indicates: a validated digital watermark comprising the digital watermark, and a first frequency at which the digital watermark is embedded. The computing platform may embed, into a request to initiate a communication session, the digital watermark. The computing platform may send, to the first user device, the request to initiate the communication session, where the first user device may be configured to: 1) identify, based on the first frequency, the digital watermark, 2) compare the digital watermark to the validated digital watermark, and 3) based on identifying that the digital watermark matches the validated digital watermark, output an authenticated communication session notification.

Abstract: A computing platform may generate a graphical user interface. The computing platform may embed, into the graphical user interface, steganography, which may include information corresponding to the graphical user interface that is not displayed on the graphical user interface. The computing platform may receive, from a user device of a user, an interface access request and user access credentials. The computing platform may identify, based on the user access credentials, access permissions of the user, which may define portions of the steganography to which the user has access. The computing platform may record, in a change log, interactions of the user with the graphical user interface. The computing platform may compare the interactions in the change log with the access permissions to identify whether or not unauthorized access is detected. Based on detecting the unauthorized access, the computing platform may initiate security actions for the graphical user interface.

Abstract: 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.

Abstract: A computing platform may train, using historical call information, a prompt generation model to identify, for an initiated call between a first individual and a second individual, one or more security prompts to validate an identity of the first individual. The computing platform may detect and temporarily pause a call. The computing platform may input, into the prompt generation model, information of the call, which may cause the prompt generation model to output the security prompts, which may be customized CAPTCHA tests based on the information. The computing platform may send, while the call is paused and to a user device of the first individual, the security prompts. The computing platform may receive, while the call is paused and from the user device, responses to the one or more security prompts. The computing platform may validate, while the call is paused, the responses, and resume the call.

Abstract: Aspects of the disclosure relate to using machine-learning models to determine graduated levels of access to secured data for remote devices. In some embodiments, a computing platform may establish a connection with a mobile device. Subsequently, based on establishing the connection, the platform may identify initial device information, device features, and user information. The platform may input the identified information into an authentication model to compute a baseline authentication score and then may identify an initial level of access to secured resources for the mobile device. Thereafter, the platform may receive from the mobile device, AR/VR device information captured by the mobile device. The platform may input the AR/VR device information into the authentication model to compute an augmented authentication score. Based on the augmented score, the platform may identify an augmented level of access to secured resources for the mobile device.

Abstract: 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.

Abstract: Methods for reducing storage demands at a centralized database and for reducing probability of a security breach such as an interception attack and a man-in-the-middle attack. Methods may include obtaining a document at the edge computing device. Methods may further include storing the document at the edge computing device. Methods may include executing, using a processor, on the edge computing device, a hashing algorithm on the document. Methods may include outputting, using the processor, a hash-value that is an outcome of the hashing algorithm, where the hash-value corresponds to the document. Methods may include transmitting, using the processor, the hash-value to the centralized database over a low bandwidth communication line while maintaining a storage of the hash-value at the edge computing device. Methods may include storing the hash-value at the centralized database. The hash-value may provide a condensed representation of the document.

Abstract: A system includes a memory and a processor configured to monitor a plurality of written electronic communications from as first user and a second user, wherein the first user has access to confidential information and the second user does not have access to the confidential information. Based on the monitoring, the processor determines a text pattern common across at least a portion of the communications. The processor compares the text pattern with the confidential information and determines a first correlation pattern between the text pattern and the confidential information. In response to determining the first correlation pattern, the processor determines that the first user included confidential information in the communications, raises an alert, and blocks subsequent communications between the first user and the second user.

Abstract: Aspects of the disclosure relate to digital check processing. A computing platform may receive, from a first recipient, a request to transfer a first portion of funds, corresponding to the first digital check image, to a second recipient account and a second portion of the funds, corresponding to the first digital check image, to a third recipient account. The computing platform may generate second and third digital check images representative of the first and second portions of the funds respectively. Based on successful validation of the second digital check image and the third digital check image, the computing platform may embed, into the second and third digital check images, a digital chip that indicates the successful validation. The computing platform may send, to the second recipient account and the third recipient account, the second and third digital check images respectively.

Abstract: Arrangements for continuous authentication and secure access control are provided. In some aspects, a computing platform may receive user data from a plurality of user data sources. The user data may include a plurality of different data types. The computing platform may use the user data to train a machine learning model, which may then be used to generate user specific baseline data. Subsequent user data may be received and analyzed, using the machine learning model, to determine whether an anomaly exists between the subsequent user data and the baseline data. If not, the user may be considered authenticated and second user data may be received and analyzed to continuously authenticate the user. If an anomaly is detected, the anomalous data and other data may be further analyzed to determine whether to authenticate the user or execute a response action.

Abstract: Aspects of the disclosure relate to computing hardware and software for enhancing collaborative experience security. A computing platform may receive, from a user device, a request to join an enhanced reality collaborative experience involving use of an avatar in an AR or VR environment. The computing platform may verify an authorization channel for the user device, and may grant the user device access to the enhanced reality collaborative experience. The computing platform may receive biometric information corresponding to the user. Using data collected by the user device and the biometric information, the computing platform may perform anomaly detection to detect anomalies in the avatar's participation in the enhanced reality collaborative experience. In response to detecting the anomalies, the computing platform may perform one or more security actions for the enhanced reality collaborative experience.