DigiCert, Inc.

Systems and methods for monitoring and managing cryptographic libraries within a version control system include steps of subsequent to integrating a monitoring agent with the version control system as a hook or plugin that triggers upon one or more version control events including commits, pushes, or merges, continuously observing code changes in real-time as source code is committed to the version control system; analyzing newly added or modified files within the committed code to detect cryptographic libraries and associated cryptographic functions; and generating alerts upon detection of introduced, deprecated, or vulnerable cryptographic components.

Systems and methods are provided for recommending a deployment of cybersecurity products according to threat intelligence pertaining to an organization. A method, according to one implementation, includes a step of extracting, from one or more sources, cybersecurity intelligence related to an online presence of an organization. The method also includes a step of analyzing the cybersecurity intelligence to determine a cybersecurity posture of the organization, where the cybersecurity posture is defined by at least a set of one or more cybersecurity issues. Also, the method includes a step of creating a cybersecurity product deploying recommendation to assist the organization with mitigating the set of one or more cybersecurity issues.

Systems and methods for validating documents, organization, and individuals are provided, utilizing both automated and manually controlled validation checks. In one implementation, a method includes a step of receiving a request to perform a validation analysis with respect to an organization, wherein the validation analysis includes Machine-Learning (ML) procedures for checking multiple validation metrics. In response to gathering multiple documents relevant for performing the validation analysis, the method further includes a step of extracting data from each of the multiple documents relevant for checking the multiple validation metrics. Also, the method includes a step of accepting manual assistance from a validation specialist when needed for performing the validation analysis.

Systems and methods are provided for predicting whether digital content is generated by a human or by a machine. In one implementation, a method includes a step of receiving digital content to be tested. The method further includes a step of analyzing the digital content with respect to both a human classification model associated with a specific individual and a computer classification model associated with a specific Generative Artificial Intelligence (GenAI) engine. In addition, based on results of analyzing the digital content, the method includes a step of predicting whether credit for creating the digital content is to be assigned to the specific individual or the GenAI engine.

Systems and methods are disclosed for securing and validating training data used to train machine learning models through watermarking. Initially, watermark entries indistinguishable from genuine data are algorithmically generated from actual training dataset entries. These watermark entries are embedded into the training dataset, forming a watermarked dataset, which is then published for subsequent use. Verification involves applying a predetermined one-way detection function configured to identify watermark entries, confirming dataset authenticity without allowing unauthorized watermark generation. Optionally, the watermarked dataset can also be digitally signed by the dataset provider using a cryptographic certificate, enhancing trust through verified identity. Upon successful watermark detection and optional signature validation, the dataset is approved for machine learning model training, ensuring the integrity and legitimacy of data used in critical ML applications.

Systems and methods are provided for recommending a deployment of cybersecurity products according to threat intelligence pertaining to an organization. A method, according to one implementation, includes a step of extracting, from one or more sources, cybersecurity intelligence related to an online presence of an organization. The method also includes a step of analyzing the cybersecurity intelligence to determine a cybersecurity posture of the organization, where the cybersecurity posture is defined by at least a set of one or more cybersecurity issues. Also, the method includes a step of creating a cybersecurity product deploying recommendation to assist the organization with mitigating the set of one or more cybersecurity issues.

Systems and methods for providing directory support in an organization are described. A method, according to one implementation, includes gathering digital data from multiple sources within an organization. The method also includes indexing the digital data in a table that includes at least a first column including names of a plurality of members of the organization and a second column including keywords attributed to the plurality of members. Also, the method includes training a Machine Learning (ML) model by data crawling through the digital data, assigning weights to the keywords, and storing the weights in a third column in the table. In response to receiving an inquiry from a user, the ML model is configured during inference to use information in the table to provide an output to the user identifying a member in the organization who demonstrates expertise on a specific topic.

Systems and methods for watermarking machine learning training data for verification thereof, include, for a training data provider, obtaining training data; responsive to verifying the training data based on a use case or application associated therewith, watermarking the training data by adding watermarked data therein; and publishing the watermarked training data. For a machine learning model provider, the systems and methods include obtaining training data to train the machine learning model for an application or use case, to any of make predictions, inferences, and classifications related to the application or use case; obtaining a one-way function associated with the application or use case; checking the training data with the one-way function to determine validity thereof; and, responsive to the validity, utilizing the training data to train the machine learning model.

Systems and methods for watermarking machine learning training data for verification thereof, include, for a training data provider, obtaining training data; responsive to verifying the training data based on a use case or application associated therewith, watermarking the training data by adding watermarked data therein; and publishing the watermarked training data. For a machine learning model provider, the systems and methods include obtaining training data to train the machine learning model for an application or use case, to any of make predictions, inferences, and classifications related to the application or use case; obtaining a one-way function associated with the application or use case; checking the training data with the one-way function to determine validity thereof; and, responsive to the validity, utilizing the training data to train the machine learning model.