Artificial Intelligence and Two-Factor Authentication: A Match Made in Security Heaven

Two-Factor Authentication (2FA) is a cybersecurity measure that uses at least two factors to verify and authenticate a user. 2FA is any combination of these factors: knowledge (password), possession (smartphone or token), and biometrics (fingerprint, facial recognition). With cyber-attacks becoming more devastating and regular, 2FA helps to double-check a login attempt to verify if it is genuine by requesting another authentication factor. 

Artificial Intelligence has extended the scope of 2FA to being more proactive in identifying and eliminating cyber threats. While conventional 2FA starts and ends at login, AI-powered 2FA continues during and after the login session and uses a person’s behavior to create secure digital identity and detect suspicious activities. 

Companies have adopted AI as part of their 2FA mechanisms due to increasing concerns about data security and losses from breach data, driving growth in the AI-based cybersecurity market. It is currently valued at $22 billion and is projected to grow to $60.6 billion by 2028 at a CAGR of 21.9%. 

In this post, we are going to analyze AI-enhanced authentication, its methods, advancements, benefits, and challenges. We will also consider the future of AI and 2FA and emerging trends.

AI-Based Authentication Methods

Infusing AI in authentication has led to better and more accurate security automation. But in what ways does AI work in security authentication? Let’s see them.

Biometric authentication

Biometrics uses parts of the human body to authenticate a user. An example of biometric authentication is facial recognition technology which maps out a user’s face and makes it into a pattern stored on a database. Whenever the user tries to log in, the algorithm scans his face and matches it with the preset image on the database. 

Other examples of biometrics include fingerprints, voice recognition, iris recognition, etc. A combination of more than one of these factors is known as multimodal biometrics. Biometric authentication is seen as more convenient for users due to less friction. 

Behavioral biometrics and machine learning models

Behavioral biometrics passively studies a user’s behavior or how a user interacts with a machine to identify him. It is a type of Multifactor Authentication that uses thousands of parameters such as gesture recognition, session duration, toggling style, keystroke dynamics, etc. 

Machine learning studies all these parameters to find patterns in all these behaviors for user profiling. With these patterns, it can accurately identify a user and prevent an impersonator from accessing a user’s information. 

Passwordless authentication with AI-powered security systems

Though still in use, passwords are no longer fit for purpose as they can be hijacked or easily forgotten by users. The vulnerabilities of passwords make it one of the leading causes of data breaches worldwide. 

Passwordless authentication methods replace passwords with alternative user authentication management methods such as biometrics, wearable devices, FIDO2, and token-based authentication. 

Adaptive Authentication 

This is a risk-based authentication that uses different types of factors to verify a user. Depending on the type of risk, the verification method will be different. For instance, if an employee tries to log in to the company account on a private device, the system could ask a security question to confirm identity. Adaptive authentication is not static and uses unpredictable factors to detect a genuine login attempt. 

Advancements in AI for 2FA

The continued development of AI is helping to improve 2FA. These advancements in AI are evident in different ways which contribute to strengthening user authentication. 

Deep learning algorithms for improved accuracy and reliability

Deep learning uses Artificial Neural Networks to process large amounts of data to detect patterns. Deep learning is seen in natural language processing, speech, and image recognition technologies. Deep learning systems use computer vision to detect fraudulent activity, such as credit card fraud. 

Due to its ability to filter information to identify patterns, deep learning has enabled AI to accurately detect a user’s biometrics in ways that the human mind cannot. 

With deep learning, AI can reduce false positives and negatives in user verification, leading to a more reliable authentication system. The downside to deep learning is the requirement to supply it with large amounts of data, which is time-consuming, and the results may be hard to interpret. 

Neural Networks for advanced fraud prevention and risk assessment

Neural networks, or cognitive computing, imitate the human brain to process information. It is a category of AI that aims to teach computers how to think, learn and solve problems like humans. Neural networks depend on datasets being fed into them for risk-based assessment and to check the possibilities of a data breach using details of previous experiences. 

For instance, neural network algorithms help banks and other Fintechs generate information on a potential credit card fraud based on the previous history of customer transactions and records of such incidents. 

Predictive analytics tools for proactive threat detection

Another advancement in AI is the ability for companies to be proactive in detecting and dealing with threats before they become more dangerous. Predictive analytics uses predictive modeling, which uses historical and present data to formulate a model to anticipate a future event accurately. 

This real-time threat detection provides fîrms with the tools for anomaly detection and potential attacks. This allows a firm to be one step ahead of cybercriminals and to respond quickly to potential threats to its system. 

Benefits of AI-Enhanced 2FA

AI-powered security systems may be expensive, but the benefits of using this intelligent authentication are worth the expense. Here are some of the benefits. 

• Improved user experience and convenience

AI-enhanced authentication gives the user a frictionless experience due to the processing speed. The user doesn’t have to memorize passwords or entertain the fear of passwords being hijacked. The user can verify his identity using identity verification technologies like biometrics within seconds. AI authentication is more user-friendly with intuitive user interfaces. 

• Enhanced security and protection against cyber threats

AI-powered 2FA offers better protection for user identification. Due to the dynamic nature of AI, it can detect unforeseen threats and makes it much harder for criminals to compromise your security infrastructure. In addition, AI-based authentication, such as user behavior analytics, is harder to replicate or hijack because most factors are unique to the user. 

• Greater efficiency and cost savings for organizations

According to IBM, organizations using AI as part of their security infrastructure had a 74-day shorter breach life cycle and saved $3 million more than those without it. 

Installing and maintaining an AI-based 2FA system is a cheaper alternative for enterprises. 

AI allows businesses to conduct a dynamic risk assessment and continuous security monitoring, freeing up more staff for other tasks. The result of this is a more efficient workflow system. Companies can conveniently cut down on the costs of hiring staff, such as customer support officers, who are usually bogged with password reset issues. 

Challenges and Risks of AI in 2FA

AI-powered 2FA is relatively new and has considerable challenges, especially regarding data privacy and exploitation. 

Data privacy concerns and ethical considerations

There are fears that AI authentication protocols can be used to collect sensitive information from users. For instance, a behavioral biometric system that studies a user’s behavior may collect too much private information without the user’s consent. 

Another challenge with AI in 2FA is ethical issues stemming from AI systems being trained on biased data. This usually results in discriminatory outcomes where real users are denied access to their accounts. 

Risks of AI-generated false positives and false negatives

AI-powered systems are not foolproof and could make costly errors. It could flag a legitimate operation as suspicious due to a change in user behavior known as a false positive. The system could also deem a suspicious activity legitimate due to similarities in the behaviors of the genuine user and cybercriminal, known as a false negative. Where any of these happens, it wastes the time of security analysts investigating the incident and may jeopardize the security of sensitive data. 

Security risks and potential for exploitation

As with any system, AI-based authentication is not immune from cyber-attacks. If the system is compromised, users could find themselves locked out and unable to access their accounts. AI systems depend on pattern recognition to work. A hacker could deceive the system with similar patterns to bypass it. In addition, there may be inaccurate or incomplete data in the system which could make it malfunction. 

Future of AI and 2FA

What should you expect from AI soon, and how will these changes affect 2FA?

Emerging trends and developments in AI-enhanced 2FA

Let’s see some trends in AI-enabled cybersecurity. 

• Aggressive AI

Artificial Intelligence systems are being trained to detect threats and repel them automatically. This trend is set to reduce the response time to cyber threats by enabling the system to be adversarial in attack prevention. 

• Explainable Machine Learning

Sometimes it is difficult to understand how an AI system decided how it identified a cyber threat or why it allowed or failed to identify a user. Explainable AI provides transparency for machine learning algorithms and makes it easier for people to understand. It will also help to track the origin of biased outcomes to make the system fairer. 

• AI and Cloud Security

Data storage has moved from onsite infrastructure to cloud-based storage. Cloud storage is scalable and can host huge amounts of data but faces huge security risks. With the help of AI, cloud security systems can process efficiently and block threats before they occur. 

• AI in the Internet of Things (IoT) 

IOT seeks to connect all smart devices to communicate with each other. Billions of smart devices will be involved in IOT. Unsurprisingly, this poses great security challenges for users and companies. Thanks to AI, it is now possible to rapidly analyze large amounts of data to pinpoint cybersecurity threats. AI can also tackle these threats using machine learning. 

Predictions for the Future of Authentication and Security

The future of authentication is set to be passwordless and an ongoing process. Using continuous authentication, AI-powered 2FA will not start and end at the point of login but will run through the user’s session duration. This means that the session cannot be hijacked throughout the time a user is logged in. 

Another prediction for AI-based 2FA will be a shift from supervised learning to allowing the AI system to look for patterns humans don’t know. This increased intelligence will make 2FA more comprehensive and unpredictable as it will be able to discover more factors to assess. 

Implications for organizations and individuals

As AI evolves, its impact on 2FA will result in better safeguards for users and companies. Cutting-edge technology in the hands of cybercriminals has made passwords more vulnerable to attacks. AI-based 2FA offers a better alternative to data security. The ability to use sophisticated authentication factors such as biometrics gives it a considerable edge over password-based authentication methods. Moreso, it is faster and more proactive in dealing with perceived threats.

The implication for organizations is that AI-based 2FA enables them to secure their data with security automation better. At the same time, individuals enjoy a better user experience and don’t have to worry about forgetting or losing their passwords. 

Conclusion

The continued evolution of AI has made 2FA stronger. Although not foolproof, the combination of AI and 2FA is guaranteed to safeguard online data and transactions better than passwords. Additionally, AI’s ability to analyze data on a larger scale means companies can scale their cybersecurity efforts with intelligent authentication solutions. Organizations must leverage 2FA to protect customer data, as a data breach is costly. Ready to implement 2FA into your business? BSG global communication platform has got you covered. We are a global communication platform that offers 2FA solutions to businesses to secure customer details and transactions.