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Can ML and AI Help Counter Cybersecurity Challenges?

Cybersecurity is a challenge for large and small and established and emerging businesses alike. We live in a time when the stake of cybersecurity breaches in the business world is higher than ever.

More than 60% of breaches are incidents of hacking. And about 81% of hacking-related breaches happen due to weak passwords or network shielding, while 50% of the cybersecurity trespassing occur because of malware attacks.

The news of hackers’ nasty deeds alarms us every now and then to take preventive measures in our process and while browsing the Internet to avoid becoming a cybercrime victim.

In its simplest definition, an act of cybercrime is a crime carried out through/on cyber (the Internet) and computer. However, the actual face of these crimes isn’t that simple. The risks of cybersecurity attacks are quite damaging for businesses as well as individuals.

But, thankfully, the increasing prominence and helpful use cases of Machine Learning (ML) and Artificial Intelligence (AI) have shown a hope that cybersecurity challenges can be tackled down. There have emerged many deep learning tools that businesses across the globe that modern businesses use to secure their network and data security. These utilities effectively perform penetration testing and provide vulnerability scans and assistance in order for your process to achieve cyber immunity.

But, does that really make sense that an AI or ML application can multiply the fruitfulness of your cybersecurity efforts? According to an article on Towards Data Science, “We don’t have that level of AI (for cybersecurity) yet, so let’s not distract ourselves with these false concepts.”

The article, however, does cite the developments taking place in AI and ML, and mentions that introducing these tools in a process should be a strategic development. It should be implemented with a view to help a company’s security analysts perform their job more effectively. Their job, without a doubt, is to keep track of vulnerabilities and fix them before they solidify into a compromising situation.

Contrarily, a article denies the aforementioned belief and gives a solid argument, “Machine learning will never be a silver bullet for cybersecurity compared to image recognition or natural language processing, two areas where machine learning is thriving.”

The focus of these new-age innovations should never be on developing a process that is completely secure. Because, technically, there is no such thing called complete security. If you can use machine learning to bulletproof your process, hackers too can make use of such contrivances to break into your process and give you a financial heart attack.

The Role of AI and ML in Cybersecurity

AI and ML are closely connected. A machine learns through some kind of artificial intelligence it is blessed with. Still, there are plenty of differences in both the terms. When an application can measure and predict possible solutions for a situation alongside foretelling the (possible) outcomes of each solution displayed – it is an AI-based program.

On the other hand, Machine Learning is an algorithm that, when fed enough information, is capable of recognizing patterns in new data and learning to classify that new data, based on the information it already has. The algorithm, i.e. the pre-loaded data, makes the machine learn and helps the user to create strategies for further enhancement.

If there were no developments taken place in Artificial Intelligence and Machine Learning, keeping private and business data would have been the most intricate puzzle before us, humans. How would a cybersecurity analyst detect malware and phishing attack then?

There are two categories of approaches in Machine Learning: Supervised and Unsupervised. Supervised ML can be used for learning whether something is good or bad. With this approach, we can recognize and categorize a breach as good or bad. In machine learning, this approach allows for acquiring knowledge about new data.

The unsupervised approach in ML enables security analysts to easily create, analyze, and comprehend large datasets. However, this approach doesn’t offer much help in finding the weakest link of a process inviting data breach.

Final Words

Although replacing manpower with AI or ML-based applications is being seen as a savior step to reinforce network and protect databases among companies across the world, experts at UK-based penetration companies suggest, it might not be the ideal way out.

Increased attention on AI and ML is a brand new threat to data, privacy and networks. It’s not just the companies that are using these innovations. Hackers too are upgrading their knowledge to plan more sophisticated cybercrimes.

According to an update from Harvard Business Review, hackers are becoming aware with same techniques imposing a bigger challenge to cybersecurity analysts and businesses.

As mentioned earlier in this blog, AI and ML should not be used for creating leak-proof applications and processes. Rather, the need is to seat a team of cybersecurity experts and equip them with the right set of technologies that helps them detect and fix the openings that might be inviting breaches.

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