Abstract: Disclosed are techniques for populating a password manager. In an aspect, a processing system of a user device or a remote server scans an email account of a user of a user device to identify a set of service providers from which the email account of the user has received one or more emails, optionally displays, via a user interface of the user device, a list of the set of service providers, optionally receives, via the user interface of the user device, a selection of at least a subset of the set of service providers, and populates a password manager with login information for the user for each of a set of service provider accounts corresponding to at least the subset of the set of service providers.

Abstract: Disclosed are techniques for providing an early warning notification to a user if the user was likely involved in a data breach. In an aspect, an apparatus scans an email account of a user of a user device to identify a set of service providers from which the email account of the user has received one or more emails, searches one or more data breach reporting databases for at least a subset of the set of service providers, and provides an indication to the user of whether personal information of the user is likely to have been exposed in a data breach, wherein the personal information of the user is likely to have been exposed in the data breach based on at least one service provider of the set of service providers being present in at least one of the one or more data breach reporting databases.

Abstract: Disclosed are techniques for providing an exposure rating. In an aspect, a processing system provides, to a user, an exposure rating to enable the user to visualize a level of online exposure of their personal information, wherein the exposure rating is determined based at least in part on an email score and a data broker score, wherein the email score is determined based at least in part on a number of a set of commercial email domains determined from a scan of an email account of the user, a number of a set of protected commercial email domains, and an email portion of the exposure rating, and wherein the data broker score is determined based at least in part on a number of a set of known data brokers, a number of the set of protected data brokers, and a data broker portion of the exposure rating.

Abstract: Disclosed are techniques for determining opt-out compliance to prevent user data exploitation. In an aspect, a user device scans an email account of a user of the user device to identify a list of commercial email domains from which the email account has received one or more emails, transmits an opt-out request to a commercial email domain on the list of commercial email domains, receives an opt-out response from the commercial entity, wherein the opt-out response comprises an email or a webform containing natural language text indicating a response to the opt-out request, applies a machine learning model to the opt-out response to classify, based on the natural language text indicating the response to the opt-out request, a type of the opt-out response as one of a plurality of types of opt-out responses, and displays a notification indicating the type of the opt-out response determined by the machine learning model.

Abstract: Disclosed are techniques for providing an exposure rating. In an aspect, a processing system provides, to a user, an exposure rating to enable the user to visualize a level of online exposure of their personal information, wherein the exposure rating is determined based at least in part on an email score and a data broker score, wherein the email score is determined based at least in part on a number of a set of commercial email domains determined from a scan of an email account of the user, a number of a set of protected commercial email domains, and an email portion of the exposure rating, and wherein the data broker score is determined based at least in part on a number of a set of known data brokers, a number of the set of protected data brokers, and a data broker portion of the exposure rating.

Abstract: Disclosed are techniques for determining opt-out compliance to prevent user data exploitation. In an aspect, a user device scans an email account of a user of the user device to identify a list of commercial email domains from which the email account has received one or more emails, transmits an opt-out request to a commercial email domain on the list of commercial email domains, receives an opt-out response from the commercial entity, wherein the opt-out response comprises an email or a webform containing natural language text indicating a response to the opt-out request, applies a machine learning model to the opt-out response to classify, based on the natural language text indicating the response to the opt-out request, a type of the opt-out response as one of a plurality of types of opt-out responses, and displays a notification indicating the type of the opt-out response determined by the machine learning model.