Two factor authentication (2FA) has helped everyone connected to the internet in keeping their accounts, data, services… safe from the attackers. It’s a highly robust security layer, which is extremely hard to crack. The question is, if it is that safe to use 2FA, why do we need adaptive 2FA. The answer is simple, better security and accessibility. Let’s justify the answer over this discourse.
Adaptive authentication is a new security feature that uses machine learning to verify the authenticity of a login before prompting the user for two factor authentication. Let’s break this up. First, from user’s point of view, adaptive authentication is just an add-on to their 2FA authentication solution. The user will not interact with adaptive authentication security layer directly. Second, it uses machine learning. Machine learning uses algorithms to learn patterns in data and make predictions based on that data. This gives machines (processors) the ability to decide. Third, the veracity of the login attempt is confirmed on the service provider’s end. This process checks from various patterns learned from the account owner, if the login attempt is valid and secure.
New authentication factors lead traditional methods towards deprecation.
Choosing the authentication factors
Two factor authentication adds complexity to the login process. Each additional authentication factor added to the login process (3FA, multi factor authentication, etc.) incorporates inefficacy. Adaptive authentication strengthens the security of a user’s account without adding any complexities to the login process. Utmost attention is paid while choosing the authentication factors. Too many authentication factors may ultimately slow down the authentication process and make it cumbersome. A balance is maintained between security and usability by adding only the most significant factors, like login time, device used for access, IP at which the login originated, geolocation, and security of the communication channel. A user’s behaviour is recorded and analysed based on these authentication factors to create the user’s risk profile. The machine learning algorithms adapt to the user’s risk profile and tendencies to develop an effective mechanism for verifying the veracity of the login attempt.
Effortless security for users
Identity management gets easier with the use of adaptive authentication, both for the end-user and enterprise. The user will not have to be bothered with different authentication layers. Instead, the entire process of authentication will be swift and easy. Enterprises will not have to dedicate security personnel to verify the reliability of a login attempt, saving both resources and time. Also, machines are fast. The entire adaptive 2FA process executes in the background with negligible time delay. In most cases, the user will not be even aware of the verification that has taken place.
Efficacy and Usability
Adaptive authentication can verify several factors associated with the login attempt, before the user gets to 2FA. Not only this, adaptive authentication can even allow a user to bypass 2FA based on the veracity of the login attempt. e.g. When not in office, Max always uses his personal mobile device to login to his work account. Before implementation of adaptive authentication, each time Max tried to log in, he was subjected to two factor authentication. But few days after adaptive authentication was implemented on his company’s server, Max stopped getting 2FA requests and could login through user ID and password alone. Through adaptive authentication the server knows that it is Max who is trying to access his account from the same mobile device he’s used in the past. Isn’t it easier? And it’s just through a single factor. The adaptive authentication algorithms use a number of factors and complex statistics to build user profiles.
Dynamic and (per the name) Adaptive
The processes and algorithms involved in adaptive 2FA are dynamic. They keep building and updating the user risk profile. At each attempt, along with verifying the authenticity of the login the attempt, the login pattern is analysed and recorded. The entire process of learning, analysing, and authenticating is dynamic in approach and adaptive to situation. The algorithms learn from and adapt to the login conditions. For high risk profiles or questionable login circumstances, more authentication factors may be incorporated.
Adaptive authentication can even identify malicious users and malicious bots trying to gain access to a user’s account through hacked or stolen passwords and deny them authentication altogether. Any malicious user will not even get to the two factor authentication.
Adaptive authentication is a hidden layer of security that verifies the veracity of the login attempt through machine learning. It is simple, secure, efficient, and dynamic of all things. It uses large range of inputs and factors to build a user’s risk profile to facilitate authentication. It reinforces security of an account without adding any extra verification steps for the user.