How to Improve Cybersecurity Using Artificial Intelligence

While the future might seem bleak, there have been significant developments and modifications in technology that have significantly impacted the development of cybersecurity. The major game-changer when it comes to cybersecurity is techniques and tools supported and developed by Machine Learning (ML) and Artificial Intelligence (AI) as a subset.

According to the research of Markets and Markets, by 2025, the industry of artificial intelligence is expected to reach a height of $190 billion.

They've estimated that by the end of 2021, three quarters of all commercial enterprise applications will use artificial intelligence.

Artificial Intelligence or Al is now extensively used in various industries. Automation, education, customer service, and likewise are only a few of the various sectors where Artificial Intelligence has highly initiated advancements. Artificial Intelligence is even playing a huge role in the ongoing fight for cybercrime. 

In this article, we will read about how Al is one of the best IT infrastructure solutions that has provided the required boost to cyber-security.

Machine Learning for the Detection of Cyber Threats

To stop cybercrime, organizations need to have the capability to detect a cyber-attack early to prevent what the adversaries are trying to achieve. Artificial Intelligence includes Machine Learning that is widely used for detecting cyber threats. It is done by analyzing data while simultaneously identifying the threat before it attacks your information system. Through Machine Learning, computers can use and adapt algorithms that are based on the received data. Hence, the computer will be able to predict threats as well as observe any anomalies more accurately compared to a human. 

As traditional technology mostly relies on past data and has no area for improvisation like Artificial Intelligence. Conventional technology cannot cope with the new tricks and mechanisms of hackers like Artificial Intelligence can. Moreover, Al can easily and rapidly deal with the increasing volume of cybercrime cases compared to humans. 

Al Authentication and Password Protection

Passwords are fragile control and the only barrier between the people's accounts and hackers. Most people being lazy use the same password across various accounts, saving them as drafts on their phone, not changing them, and likewise. Biometric authentication is tested as a better alternative to passwords. However, they are not so convenient either. For instance, the face recognition system can get quite a handful as it can fail to recognize the user due to a new hairstyle. Hackers can use this loophole by using the user's image from their social media handles and use them to bypass the security line. 

That is why developers are using Artificial Intelligence for enhancing biometric authentication and making it reliable by getting rid of its imperfections. A good example is the face recognition technology used by Apple in their iPhone X devices. The technology is called 'Face ID' that works by processing the facial features of the user via neural engines and built-in infrared sensors. The Artificial Intelligence software crafts a model of the face of the user by identifying key patterns and correlations. Apple assures that through this technology, it is nearly impossible for the hackers to fool the Al and open the device in any other way. Apple's IT security solutions have architectured the Al software in such a way that it can even work in a wide range of lighting conditions as well as compensate for changes such as wearing a hat, growing facing hair, new haircuts, and more. 

Network Security and Al

The network security includes two Important parts that are figuring out the network topography of an organization and the creation of security rules and policies. However, both activities are extremely time-consuming. But various IT infrastructure services are now using Artificial Intelligence for expediting the processes. Al does that by learning and observing network traffic patterns that help in suggesting security policies. This does not only help in saving time, but also an enormous amount of resources and efforts which can be applied elsewhere like areas of technological advancement and development. 

In addition to that, Artificial Intelligence can also help in detecting a buffer overflow. This happens when an app inputs more data compared to what it usually does in a buffer. As employee mistakes are a major cause of data breach, Al can detect those mistakes easily to prevent damage. 

Conclusion

Even though various IT security services are using Artificial Intelligence to improve their security features in terms of cybercrime, there is still room for improvement. For instance, while detecting abnormalities, Al can help in preventing unauthorized access or detecting malware in the initial stages. However, software companies and security firms will keep on leveraging Artificial Intelligence for improving detection times, increasing detection rates, preventing malware from spreading, increasing customer security, and protecting systems. Even though Artificial Intelligence still has to improve as well as improvise a lot, it already has a huge impact on the landscape of cybersecurity.