Abstract: This invention relates to a process for detecting and mitigating risk generated when a customer's log-in credentials are compromised. A significant majority of stolen credentials and customer's personally identifiable information data eventually make their way to the dark web. By dynamically monitoring the dark web and combining the analysis with related information about the user and their credentials on the deep web and the surface web, through a machine learning model, a service provider pre-emptively or otherwise can act to mitigate the risk arising from such compromise of said customer log-in credentials.

Abstract: This invention relates to a process for detecting and mitigating risk generated when a customer's log-in credentials are compromised. A significant majority of stolen credentials and customer's personally identifiable information data eventually make their way to the dark web. By dynamically monitoring the dark web and combining the analysis with related information about the user and their credentials on the deep web and the surface web, through a machine learning model, a service provider pre-emptively or otherwise can act to mitigate the risk arising from such compromise of said customer log-in credentials.