The organization may realize that a bad actor is accessing the accounts for performing fraudulent activities such as purchases, increased chargebacks, or customer data stealing. Before the fraudulent activity a bad actor finds the login interfaces to test leaked credentials to gain access to an account. AuthSafe solution uses the cognitive machine learning engine to get trained from the customer’s end user behavioral analytics to identify the good users and bad users away from the digital service at mass scale.
The organization may realize that a bad actor is accessing the accounts for performing fraudulent activities such as purchases, increased chargebacks, or customer data stealing. Before the fraudulent activity — a bad actor finds the login interfaces to test leaked credentials to gain access to an account. AuthSafe solution uses the cognitive machine learning engine to get trained from the customer’s end-user behavioral analytics to identify the good users and bad users away from the digital service at mass-scale.
AuthSafe’s cognitive user behavior engine allows legitimate users to have a delightful experience and refrain from engaging with bad actors. AuthSafe provides detailed data about risks associated with each device, which are calculated using the cognitive engine.
Developers can use API to provide the user with more control by creating a custom page to report a suspicious device. AuthSafe’s API allows you to list the number of registered devices, integrating webhooks for security alerts.
AuthSafe’s cognitive user behavior engine allows legitimate users to have a delightful experience and refrain from engaging with bad actors. AuthSafe provides detailed data about risks associated with each device, which are calculated using the cognitive engine.
Developers can use API to provide the user with more control by creating a custom page to report a suspicious device. AuthSafe’s API allows you to list the number of registered devices, integrating webhooks for security alerts.
First, when your customer visits the application’s pages, AuthSafe’s cognitive engine starts learning about your customer’s behaviors. The cognitive engine begins behavior analysis of pre- and post-authentication, page views, and device patterns using your end-user inputs. The cognitive machine learning engine starts evaluating the risks of your end-user and identifies any credential stuffing, brute force attack, or potential compromise.
Second, cognitive machine learning naturally makes your digital service more secure and offers the customer an extraordinary experience with airtight account security. You can set the risk threshold to allow or disallow the user, and the results of the threshold are sent to login business logic in real time. Even if you over calculate the risk threshold,users are never locked out, and we achieve zero lockouts using recovery workflows.
Third, when a bad behavior is identified, a recovery workflow is initiated in real time. The potentially compromised user is notified via webhooks to lock the user. Based on the risk policy developed from the customer’s side, the user’s access is allowed, declined,or challenged by 2F (two-factor) authentication.