Wells Fargo Bank, N.A.

Jerry Reynolds

Michael Sbandi

Matthew Thomas McDonald

John Masiliunas

Brian Matthew White

Richard Lee Goble

Edward J Patterson

Venkat Sriram Karicherla

A system for a business entity is disclosed that is configured to execute an authorized countermeasure against a malicious actor via a smart contract in response to the system determining that the malicious actor has performed a malicious action. The system determines that a smart contract established between the business entity and an official agency defines an authorized countermeasure to be performed in the event of the malicious action. The system also determines that the malicious actor has performed the malicious action on the business entity. In response to the determination that the malicious actor has performed the malicious action, the system executes the authorized countermeasure with respect to the malicious actor, which may include one or more of a ransomware attack, a malware distribution, a beacon, a Distributed Denial of Service attack, a destruction of data, an encryption of data, a tokenization of data, or a scrambling of data.

Data devaluation through smart contracts

A method may include querying a project data store with a project identifier using a processing unit. The query retrieves the functional requirement of a stored project data structure associated with the identifier. This requirement is then input into a trained machine learning model, which has output nodes corresponding to a set of requirement classifications. The output values from the model are accessed, and a requirement classification is added to the project data structure based on these values.

System and methods for software security integrity

A project data store is queried using a processing unit and a project identifier. The result is the retrieval of functional requirements for the project's data structure tied to that identifier. This information is then input into a machine learning model configured with the model's output nodes corresponding to a set of security concerns. Upon processing, the model's output values are accessed. A corresponding security concern is then added to a project data structure based on these output values.

Techniques are described for assessing the health of one or more applications. For example, this disclosure describes a computing device configured to obtain information associated with one or more software assets, wherein the information associated with one or more software assets comprises information associated with a lifecycle of the one or more software assets. The computing device is also configured to compute, based on at least a portion of the information associated with the one or more software assets, a health score that indicates a security risk of the one or more software assets. The computing device is further configured to perform an action based on the health score.

Software asset health score

Techniques described herein include assessing factors that may affect the quality of software under development by a development team or that may be used to evaluate the health of software already deployed in a computing environment. In one example, this disclosure describes a method that includes collecting, by a computing system, historical information about a plurality of prior development projects; correlating, by the computing system, the historical information with each of a plurality of developers that participated in one or more of the plurality of prior development projects; collecting, by the computing system, information about an application developed by a subset of the plurality of developers; generating, by the computing system and based on the information about the application and the correlated historical information, a predicted outcome for the application; and taking an action, by the computing system and based on the predicted outcome, to prevent the predicted outcome.

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Charlotte, NC, United States of America

Jerry Reynolds