Every business places the highest priority on user profile security. As a security mechanism, multi-factor authentication (MFA) asks users to sign in using multiple forms of identification. As concerns about data security have increased, it has become a more widely used method for preventing unwanted access.
This kind of verification is frequently combined with additional techniques, including having users enter a pin code sent to their mobile devices. Recently, artificial intelligence has grown into a method of putting MFA into practice. By examining a user's activity and comparing it to a database of recognized patterns, artificial intelligence can be used to confirm a user's identity.
What Is MFA?
In order to access a resource like an application, or an online account, the user must submit multiple authentication factors, which is known as multi-factor authentication. A strong identity and access management policy needs to include MFA as a fundamental element. MFA demands several extra verification elements which lessen the possibility of unauthorized access.
Why Do We Require Multi-Factor Authentication?
The hazards and requirements for security increase as businesses digitize operations and assume increased responsibility for the storage of client data. Verifying user identity has become crucial since it has long been common for attackers to use the user login information to access sensitive systems.
Since users often have problems managing their usernames and passwords across several accounts, repeat passwords frequently, and create passwords with little complexity, authentication based just on usernames and passwords is unreliable and cumbersome.
Due to the ease with which they can be obtained through malware, phishing, and hacking, passwords can provide inadequate security. MFA is a useful technique for data protection in these situations. It can aid in preventing unauthorized access to information when used in conjunction with other security measures. Companies that deal with sensitive data, such as financial institutions or healthcare providers, may find this protection to be of particular importance.
Application of AI in MFA
The application of AI to MFA is a quite recent innovation. However, because it can confirm a user's identity by examining their behavior, it is swiftly gaining popularity as a method of authentication. AI can be applied to MFA in a variety of ways. One common technique is to utilize AI to confirm a user's identification by examining their behavior (Adaptive MFA).
Finding patterns in user behavior is another method that AI may be used for MFA. When suspicious activity is detected, this information can be utilized to block illegal access. AI can also be used to keep an eye out for symptoms of device tampering. This kind of monitoring can assist in preventing unauthorized people from accessing any devices.
Adaptive MFA
Adaptive MFA is where the AI in a system gathers and processes contextual data. This information is used to calculate a risk score. The method of user authentication is chosen based on this risk score.
Low risk: The login method only requires a password.
Medium Risk: MFA is used to establish identity.
High Risk: Additional authentication processes are taken, and the system warns the user of any suspicious activity via the registered phone number and email address.
The following variables are taken into consideration while determining the risk score:
Time of Login: There is a chance that the data will be taken if a login attempt is made shortly after the original login process.
Location: AI systems get suspicious if a person attempts to log in to an account from two separate locations.
IP Address: Typically, a person will try to log in from the same IP Address. The AI will confirm the user's identity after any changes to this address.
Biometric Authentication
Biometric verification is one of the most common and convenient authentication techniques which use AI and deep learning. Here are a few examples of biometric authentication,
Fingerprints Recognition: A finger image is captured in black and white by the fingerprint scanner. Deep learning algorithms are then used to extract the image's complex features.
Iris recognition: Every single person in the world has a distinctive iris pattern, which is processed using image processing methods and classified using a decision tree algorithm.
Palm recognition: The palm is scanned using infrared sensors, and the result is a picture that looks like the blood vessels. The classification is then carried out using methods for machine learning.
Voice recognition: When we provide any voice input, neural networks are used to turn the analog data we provide into digital data.
Face recognition: This uses deep learning techniques to assess and interpret information about the alignment, size, and shape of the face in a face image.
Endnote
The application of AI to MFA is a relatively recent innovation. However, because it can confirm a user's identity by examining their behavior, it is swiftly gaining popularity as a method of authentication. MFA can increase customer trust and decrease attrition. Additionally, it can aid in lowering fraud and shield firms from the potential financial losses that could occur from it.