Abstract: In one aspect, an example methodology implementing the disclosed techniques includes, by a computing device, retrieving touchpoint data of a product and creating a data element based on the retrieved touchpoint data, the data element includes information about a touchpoint of the product by a stakeholder. The method also includes, by the computing device, adding the created data element to a product pedigree list, wherein the product pedigree list is a pedigree list which contains data elements of the product. The method may further include, by the computing device, storing the product pedigree list in a graph database. The method may further include, by the computing device, predicting, using a machine learning (ML) model (e.g., an autoencoder), whether a state of the product pedigree list is normal or anomalous.

Abstract: An example methodology includes, by a computing device, determining that a reset of credentials associated with one or more service accounts is to be performed. The method also includes, by the computing device, responsive to a determination that the reset of the credentials is to be performed, shutting down instances of applications in which the one or more service accounts are used and resetting the credentials associated with the one or more service accounts, wherein resetting the credentials updates current credentials associated with the one or more service accounts to new credentials. The method further includes, by the computing device, propagating the new credentials to the applications in which the one or more service accounts are used and starting the instances of the applications in which the one or more service accounts are used, wherein the started instances of the applications use the new credentials.

Abstract: In one aspect, an example methodology implementing the disclosed techniques includes, by a computing device, retrieving touchpoint data of a product and creating a data element based on the retrieved touchpoint data, the data element includes information about a touchpoint of the product by a stakeholder. The method also includes, by the computing device, adding the created data element to a product pedigree list, wherein the product pedigree list is a pedigree list which contains data elements of the product. The method may further include, by the computing device, storing the product pedigree list in a graph database. The method may further include, by the computing device, predicting, using a machine learning (ML) model (e.g., an autoencoder), whether a state of the product pedigree list is normal or anomalous.