Bank of America Corporation

Vinesh Patel

Michael Robert Young

Manu Jacob Kurian

Michael Young

Melissa Gordon Glenn

Ana Maxim

Systems and methods may include contributing to a quantum-resistant blockchain that stores a quantum-resistant audit trail of changes to a dataset. A non-transitory computer readable medium may store instructions that are run on a computer processor to populate a block with a log of a dataset, run a quantum-resistant cryptographic algorithm to encrypt the block, distribute the block to and receive approval from blockchain nodes to add the block to the blockchain, and run the algorithm to encrypt the block onto the blockchain. The method may include repeating the steps of running the non-transitory medium on the computer processor to populate a second block with a second log of the dataset, run the algorithm to encrypt the second block, distribute the second block to and receive approval from the blockchain nodes to add the block to the blockchain, and run the algorithm to encrypt the second block onto the blockchain.

Quantum-compatible blockchain for accountability

A processor receives request to generate content using an AI tool and generates the requested content using the AI tool. The processor detects that the AI tool used a first AI module and a second AI module associated with the AI tool to generate the requested content. In response, the processor accesses, from a memory, a first digital signature associated with the first AI module and a second digital signature associated with the second AI module. The processor generates a combined digital signature based on the first digital signature and the second digital signature and embeds the combined digital signature in the generated content to generate a marked content.

System and method for digitally marking artificial intelligence (AI) generated content

Apparatus and methods for an artificial intelligence implemented termination by an auditor artificial intelligence ('aAI') of a malicious artificial intelligence (“mAI”) are provided. The aAI may detect a mAI on a network and determine which data the mAI can access on the network. The aAI may then degrade all or part of the data in various ways to prevent the mAI from producing valid content based on the data. The aAI may also create code and inject the code into the mAI to degrade the mAI's operations. As the mAI may rely on valid data to produce valid output, degrading the data may degrade the mAI.

AI implemented AI termination

Apparatus, methods and systems for continual biometric authentication of a user on a user device is provided. Methods may create, for the user, a biometric authentication reference model. Methods may store the biometric authentication reference model at a database associated with the user device. In response to an instantiation of a session by the user on the user device, methods may execute a continual biometric scan. The scanning may be executed until the session is terminated. Methods may identify a number of comparison points between the scanned and stored biometric identifiers. In response to determining that the number of identified comparison points is below a predetermined threshold number of comparison points, methods may initiate a passcode request. The passcode request may request a confirmation of a preset user-selected passcode. In response to determining that the passcode response does not match the preset user-selected passcode, methods may terminate the session.

Continual biometric authentication

Apparatus, methods and systems for voice surveillance using enhanced metadata and geotagging capabilities are provided. Systems may monitor communications being transmitted from, and being received at, entity devices. The communications may be monitored for anomalies. The anomalies may be identified as communications outside of a regular pattern of communications. Various factors may be used to identify the anomalous communications. The factors may include a number of communications per predetermined time period, a geolocation of the communication, identification of a second communicative party of the communication and any other suitable factors. Stakeholders may be alerted regarding any anomalous communications. Additionally, once the communications have been labeled as anomalous or non-anomalous, the labeled communications may be used to create and/or update a set of labeled training data. The created and/or updated set of labeled training data may be used in an artificial intelligence engine that labels communications as anomalous or non-anomalous.

Voice surveillance system using enhanced metadata and geotagging capabilities

Systems and Method are provided for an Anti-Pattern Captcha. Methods may include AI software receiving a request from a user to access a system or website, the request comprising user credentials. Methods may include the AI software identifying a human user associated with the user credentials. Methods may include the AI software compiling data related to a plurality of unique behavior patterns associated with the human user when interacting online. Methods may include the AI software analyzing the compiled data to establish the unique behavior patterns. Methods may include the AI software presenting a challenge to the user. Methods may include the AI software verifying that the user is the human user upon a response to the challenge that deviates from at least one of the unique behavior patterns. Methods may include the AI software prompting the user to interact with the system or website upon verification.

Anti-pattern captcha

Systems and methods for an artificial intelligence-based auditor of artificial intelligence may be provided. The artificial-intelligence-based auditor may operate on one or more hardware processors. The artificial intelligence-based auditor may continually scan one or more operating artificial intelligence systems for productivity and operability. The scan may identify a data source powering the one or more operating artificial intelligence systems. The scan may measure a delta between an output from each of the one or more operating artificial intelligence systems. The scan may identify trends in quality of the one or more operating artificial intelligence systems. The scan may analyze instances where the one or more operating artificial intelligence systems outputted data outside of a parameter range. Based on the scan, the auditor may label each of the one or more operating artificial intelligence systems as positive, neutral or negative.

Artificial intelligence-based auditors of artificial intelligence

Methods for securing an electronic communication is provided. In a registration process, an anti-phish, personalized, security token may be created and/or selected for a predetermined account. The token may be stored in a database at an enterprise location. An electronic communication may be generated at a third-party location on behalf of the enterprise. The communication may be forwarded from the third-party location to a recipient associated with the account. The communication may be intercepted at an edge server. The edge server may be located at the third-party location or the enterprise location. The edge server may be in communication with the database. The edge server may select, from the database, the anti-phish token that is associated with the account. The selected token may be injected into the communication. The communication with the token may be transmitted to the recipient.

Anti-phish network for securing electronic communications

A device is configured to receive an interaction request from the first user that identifies an address for a contract that is stored in a blockchain and to access machine-executable instructions associated with the contract. The device is further configured to determine a projected number of virtual resources and real-world resources for the first user based on a number of available resources for the first user and the machine-executable instructions for transferring resources between the first user and the second user in the contract. The device is further configured to output the projected number of virtual resources to the first user and to output the projected number of real-world resources to the first user. The device is further configured to initiate a transfer of resources between the first user and the second user.

Resource transfer validation within a metaverse

An apparatus for geotagging users for authentication comprises a processor associated with a server. The processor is configured to generate a first geotag based on residence information associated with a first user and to transmit the first geotag to a first user device for assignment to a first avatar associated with the first user in a virtual environment. The processor is further configured to receive an interaction request to authorize an interaction between the first avatar and a second avatar associated with a second user. The processor is further configured to compare a first historical log to a second historical log to determine that the first avatar is associated with the first user. In response to determining that the first avatar is associated with the first user, the processor is further configured to conduct a first step in authorizing the interaction between the first avatar and the second avatar.

System and method for geotagging users for authentication

Methods and system are provided for tuning an artificial intelligence ('AI') system. The methods and system may include training the AI system to identify distinct users their distinct interactions. The training may include receiving datatsets related to a plurality of distinct users and their plurality of distinct interactions. The AI system may parse the datasets to arrive at a plurality of sets of distinct interactions performed by respective distinct users, each distinct interaction may be associated with a level of security clearance. The AI system may receive a request from a user to access information. The methods and system may include the AI system initiating a communication with the user. The communication may be compared with the each set of distinct interactions until a user and level of security can be identified. The AI system may prompt the user to access information on the level of security clearance identified.

Tunable AI system and models associated therewith based on security clearance level

A device is configured to receive a user input from a user device indicating a first gesture performed by a user in a real-world environment and to identify a first token within a gesture map corresponding with the first gesture. The device is further configured to identify a second gesture performed by an avatar within a virtual environment that is associated with the user and to identify a second token within the gesture map corresponding with the second gesture. The device is further configured to combine the first token and the second token to generate an authentication token and to verify an identity of the user based on the authentication token.

Gesture-based authentication tokens for information security within a metaverse

A device is configured to identify a physical location within a virtual environment for an avatar that is associated with a user and to obtain a first token from a token map based on the physical location within the virtual environment for the avatar that is associated with the user. The device is further configured to receive physical attribute information for the user in the real world and to obtain a second token from the token map based on the physical attribute information for the user in the real world. The device is further configured to generate an authentication token by combining the first token and the second token and to verify an identify of the user based on the authentication token.

Multifactor authentication for information security within a metaverse

Methods for increasing a data security of information shared and viewed within a social network platform is provided. Methods may include generating a social media impersonation score ('SMIS') for a first user account on the social network platform. The SMIS may indicate a veracity of the user profile data associated with the first user account. Methods may include verifying the veracity of the user profile data by determining an authenticity of a corporate email address associated with the first user account. Methods may include determining that the corporate email address exists and verifying that a domain associated with the corporate email address is a trusted domain. Methods may include generating the SMIS based on results of the verifying and linking the SMIS to the first user account. Methods may further include displaying the first user account and the SMIS on a graphical user interface (“GUI”) of the social network platform.

Systems and methods for increasing data security in social media online platforms

An apparatus for transferring virtual resources comprises a processor associated with a server. The processor is configured to conduct an interaction between a first user and an entity to transfer a physical object and a non-fungible token (NFT) to the first user, wherein the NFT is associated with the physical object. The processor is further configured to receive the NFT for storage in a first digital folder and to receive a request to establish an interaction session between a first avatar in a virtual environment and a second avatar associated with a second user. The processor is further configured to authorize the interaction session in response to authenticating the NFT and to conduct the interaction. The interaction includes initiating a transfer of the physical object from the first user to the second user and transmitting the NFT to a second digital folder associated with the second user.

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London, United Kingdom

Vinesh Patel