Influence of Artificial Intelligence & Machine Learning On Cyber Security

With more businesses readily adopting online business and e-commerce models, the need for cybersecurity has substantially increased in recent years. As more and more data is thrown on the cloud, people with malicious intent are increasingly becoming invigorated at the possibilities of hitting the jackpot.

On the other cybersecurity awareness is also gaining a lot of noise, and several organizations and forums are willing to educate young business owners about major threats of operating and running a business online.

Let’s have a quick look at some stats regarding AI and ML with cybersecurity.

According to a recent study by the Institute of Electrical and Electronics Engineers, the cost of recovering from a typical data breach is around $3.86 million, and it may take around 196 days for a full recovery. 

What AI can do is use signature-based techniques to detect about 90% of threats and can improve detection rates by 95% as compared to traditional techniques. There is no doubt that AI and machine learning can drastically improve cybersecurity and having protection against cyber-criminal activity is highly recommended. 

How Agile and Smart Projects can Adjust?

Many critics, back in the day, would strongly recommend not using Agile practices in highly vulnerable environments. However, today all thanks to progress in technology Agile methods not only work well but can also help in improving cybersecurity at the workplace instead of weakening it. 

In contrast, it is the waterfall methodology is the one that can cause major issues due to the fact that there is a lack of integration between departments which can specifically work independently of one another. However, in an Agile environment, there is a huge opportunity for you to integrate security features more fluidly, and this can actually help you to catch problems earlier in the process.     

With that out of the way, let’s take a quick at how artificial intelligence and machine learning can impact cybersecurity.

1. Prognosis – Detecting Threats Before They Happen

I know for a lot of you prognosis holds a different meaning and it specifically refers to predicting the likelihood of developing a disease in the near future.

However, to be honest, this is what AI, and machine learning exactly are capable of doing. Due to their tenacity to study incredible amounts of data and recognize patterns, AI systems are evolving at a rapid pace. 

With deep learning capabilities and the ability to work with complex algorithms, rule-based systems deploying AI and ML are capable of understanding necessary improvements required to prevent a system from failing.

This is also known as threat hunting where your AI-empowered cybersecurity system is actually able to outperform humans. A great example is that of Darktrace, which was developed by the collaboration of mathematicians from the University of Cambridge and the cyber intelligence experts from the governments of both US and the UK back in 2013.   

2. Real-Time Support

Without question, AI can be used in cybersecurity to accurately stop cyber-attacks as well as provide real-time support to systems and neural networks.

One fine example is that of Vectra’s Cognito that utilizes AI to detect cyber-attacks happening in real-time. The system is designed in such a way that it can enrich threat investigations along with a conclusive chain of forensic evidence. 

This is highly important for companies that have a vast amount of data, and their network is laid down as different components distributed over largely separated physical locations.

Hence if one of the data centers is being compromised, the cybersecurity system would instantly act to protect all the other data centers while fighting off the attack simultaneously.   

3. A System That Continues To Get Stronger

Machine learning is capable of self-improvement, which is done through real-time cybercrime mapping, pattern detection, preemptively stamping out cyber threats, and bolstering the security infrastructure. 

In fact, many systems imbued with ML capabilities would often perform their own thorough penetration testing to find out flaws and loopholes that can be used for exploitation by entities with malicious intent. 

This continuous learning and responding to changing behavior makes the security system stronger and capable of preventing similar attacks that occurred in the past.

Hence with the passage of time, these security systems evolve and are better able to respond to active attacks in the future. One fine example is that of CrowdStrike’s Falcon platform that utilizes AI for quicker visibility protection and preventing endpoint attacks across the entire organization. 

4. Extensive Vulnerability Management

Vulnerabilities seem to be never-ending as new vulnerabilities are being introduced on a daily basis. While conventional security measures only give you a fighting chance to win back your information once the hackers have already exploited the vulnerability, all of this can change with smarter systems. 

Tools like UEBA (user and event behavior analytics) powered through AI can be used to evaluate and analyze users and their behaviors. This can help systems to detect anomalies that might initially go unchecked as an unknown attack.

Organizations can then officially report these vulnerabilities for the great good and then work together to find a reasonable solution in the form of software and tool patches.  

5. Better Management of Resources

AI can increasingly assist IT managers to focus their energies on greater threats while AI can provide a decent amount of protection to daily operations without requiring the need for human intervention or supervision.

However, this may not be the case all the time as a human intervention may be necessitated in the case of more complex or complicated attack type used by hackers. 

Blue Hexagon can be taken as an example here that utilizes global threat data and test its own system to push its capabilities to the limit. This allows the ability for IT managers to focus their efforts on preventing other types of breaches and thus substantially allows a company to manage its resources more effectively.  

6. Automating Mundane Tasks

As we all know that AI utilizes data to make predictions through the analysis of data, it can also be used to automate certain actions that might be considered mundane. However, these tasks can still play an important role in daily operations and processes. 

In the post-COVID-19 era, AI is speculated to automate cyber security.

An example of Vade Secure would be relevant here as it provides email defense for companies. Through its capacity to work all by itself, Vade Secure is able to provide protection for more than 600 million mailboxes in more than 76 countries. 


While AI and ML can unlock the future for all cybersecurity systems in the future, one can also question the fact that even cybercriminals can use AI and ML to create more complex attacks. This cat and mouse chase seem to be never-ending; however, having protection for online systems is always better than staying vulnerable to attack. For more questions regarding the topic, feel free to leave a mention in the comment section below. 

Author Bio:

Samantha Kaylee currently works as an Assistant Editor at Crowd Writer. This is where higher education students can acquire essay writing service UK from professionals and experts specializing in their field of study. During her leisure time, she likes to indulge herself in pop-culture, including music, movies, and occasionally video games.