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AI in Security/Authentication
User Authentication is a major part of cybersecurity and therefore when MFA (Multi factor authentication we saw many businesses adopting it for better secure access. But cybercriminals are also actively improving their strategies, including adding to their toolkit artificial intelligence (AI). Which is why very sophisticated AI is quickly introduced by identity and access management systems to further improve MFA.
As artificial intelligence gains popularity, even biometrics can be produced even more quickly, allowing fake fingerprints and facial images with ample matching points to pass a scan. But by including a vital piece of information: meaning, the MFA can be improved. Context is the login details of the user, such as where the user is when trying to log in or using the computer. This method is called risk based authentication.
To give more context, in this method of authentication additional factors such as location, past login behavior, change in IP address and device type help identify if a device is stolen. To implement risk-based authentication, companies like OneLogin and Google, use AI-backed technologies. The AI measures and weighs individual variables of the login attempt to achieve a risk score for the scenario. AI™, monitor several factors in a user’s logins over time and build a profile for each user to understand login patterns. When a user varies from that profile on a given authentication attempt, the AI system assesses the variable factors and determines a risk score for the current login attempt. Some of the factors typically accounted for include:
- Network reputation
- User’s geographic location
- The device fingerprint (such as the manufacturer, model, or browser)
- Time of login
Risk-based authentication would gradually shift from supervised learning, where findings are included in the dataset, to unsupervised learning, where the AI identifies new trends that people might not have found and predicts possible factors to be evaluated. Being able to cross reference several machine learning algorithms and use pattern recognition and predictive algorithms based on time series would enhance the accuracy and reach of AI-based authentication offerings for future web application logins, but also for other cybersecurity aspects, such as network intrusion and botnet detection.
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