AI and ML in cybersecurity

The buzz around artificial intelligence and machine learning is unsettling for a long time now. Especially, when it comes to cybersecurity, AI and ML are performing multiple tasks to accelerate the protection process. Artificial intelligence is spread across the cybersecurity radar in different concepts namely strong AI or true AI that refers to artificial general intelligence. On the other hand, machine learning in cybersecurity performs extremely well when we have lots of data either on the cloud or the endpoint. It combines big data and analytics to come up with disruptive solutions. Therefore, technology’s dominance has further intensified the role of AI and ML in cybersecurity. AI and ML in cybersecurity are useful when the input to the system is varied, large in dimension, in apparently correlated, sparse, dynamic, and cannot be represented as simple static rules. In this article, IndustryWired takes you through some of the advantages and disadvantages of using AI and ML in cybersecurity in the 21st century.

Advantages of AI and ML in cybersecurity

Processing big data: A major advantage of AI and ML is their ability to process a huge volume of data. By automating the creation of algorithms, AI and ML models can help cybersecurity tools detect security threats. The in-depth AI-security scanning system is far better than actual human scanners. It conducts a complete analysis and processes data within a short period of time. 

Detecting frauds and anomalies: The most common way of using AI and ML in cybersecurity is to detect threats. Especially, cybersecurity issues like frauds and anomalies are very common among business organizations. Therefore, they have to employ AI and ML mechanisms to find scam patterns.

Anti-malware: Antivirus software is a very common way to encrypt your device from threatening cyberattacks. As technology has evolved drastically over the past few years, the sophistication of cyberattacks has also intensified. Therefore, AI-powered anti-malware products are being used to segregate the good and the bad files, making it possible to identify new forms of malware even if it's never been seen before. 

Finding uncommon error: Besides the usual day-to-day clean up, AI and ML technologies are also engaging in strategic decision making. For example, algorithms might leave out certain threatening concepts while taking business decisions. Therefore, an AI-based data-driven algorithm finds the hidden patterns and the possible threats behind them.

Disadvantages of AI and ML in Cybersecurity

Cybercriminals are ultra-tech savvies: While AI and ML in cybersecurity are helping big businesses and normal users greatly, we shouldn’t forget the fact that cybercriminals also have the tech in their hands. Today, cybercriminals are as talented as cybersecurity professionals. Therefore, we shouldn’t underestimate their potential and should always look out for security gaps.

Adoption barriers: Although technology has invaded many spaces, its complete adoption is still a dream for many. AI and ML require a lot of human resources, computing power, and other tech supports, compared to a common antivirus. 

AI-backed solutions are expensive: For big companies like Microsoft, Google, and Amazon, getting their hands on AI-backed cybersecurity solutions could be easy. But it is not the same for small business owners. Cybersecurity solutions powered by AI and ML technologies are comparatively expensive. Therefore, many prefer to take up simple antivirus in place for complex cybersecurity models.

Raise in unemployment: Similar to the dominance of artificial intelligence, cybersecurity also possess a bigger threat to unemployment. Many companies are firing their IT experts who used to handle cybersecurity matters. Now, AI and ML are taking over human cybersecurity jobs.