Introducing New Levels of Cybersecurity with Machine Learning


Cybercrimes have become more prevalent, exposing businesses to potential data and financial losses, and reputation damage. Unfortunately, cybercriminals aren’t about to give up, especially with the growth of cyberspace use in areas such as remote work, eCommerce, and IoT among others. Malicious people have a wider threat surface to devise new ways to attack and develop more impactful and dangerous attacks. The best way to stay ahead is to be more proactive in preventing and responding to potential threats. This is where machine learning comes in. Applying AI and machine learning to improving cybersecurity are giving enterprises a leg up in the fight. In this article, we explore the role that machine learning is playing and its impact on cybersecurity.

Predicting the next attack

They say prevention is better than cure. In cybersecurity, organizations would be in a better position if they were aware of the next attack and implemented preventive measures before damage happens. AI and machine learning are enhancing proactive defense through the invention of robust AI-powered solutions. These AI-based cyber security solutions are helping enterprises prevent future attacks by empowering enterprises to create environments that give attackers a hard time. This is made possible in the following ways.

Detecting anomalies

The amount of data that an organization’s network, website, and other applications generate is huge, not forgetting that it comes in both structured and unstructured forms. This makes it difficult for the human mind to analyze and comprehend. Machine learning has the ability to analyze tons of data from various sources. Moreover, knowledge graph machine learning technology comes in handy to help integrate structured and unstructured data, revealing the relationship between entities. Besides helping interpret data, the semantic representation helps identify new facts and anomalies in the existing data, giving insights into potential threats and new vulnerabilities.  

Unfortunately, hackers are working harder to stay ahead of enterprises. They are using AI tools to develop attacks such as fileless malware that are resistant to AI security solutions, making them harder to detect. However, unsupervised learning, a type of machine learning comes in handy in detecting such attacks.

Analyzing behavior

Machine learning can be used to identify changes in human behavior and habits. The technology learns and understands human – customers, and employees’ trends, and habits on a company network and forms a baseline. It then analyzes this baseline for any activity that might deviate from the normal. Anything unusual from the baseline can be interpreted as malicious activity and the security team is alerted to investigate it further.

Behavioral analysis can be applied in cloud data protection by identifying suspicious login attempts in cloud applications. In addition, machine learning can be used to detect anomalies and conduct IP reputation analysis. Machine learning-powered solutions such as cloud access security brokers help organizations provide a secure connection between cloud-based applications and the end users.

Scoring network risk

Understanding risks that exist in the network can help enterprises boost security in vulnerable areas to prevent future attacks. Machine learning can be used to analyze previous attack datasets and identify the specific areas of the network that was involved. It can then tell whether or not attackers will exploit identified vulnerabilities. This discovery can then be used to gauge the likelihood and determine the impact of an attack in that area. This allows enterprises to beef up security in the most vulnerable areas.

Automating repetitive tasks

One of the major obstacles to cybersecurity is the lack of resources and a shortage of skilled professionals in the field. Besides this, cybersecurity tasks can be repetitive and tedious. Machine learning has made it possible for enterprises to bypass these challenges by allowing the automation of various cybersecurity tasks. Automating mundane tasks reduces the need for human intervention, eliminating the need for full-time employees, and freeing up resources that can be channeled into cybersecurity solutions. Moreover, automation also frees up time for cybersecurity professionals to focus on other more important tasks.

One of the areas in that automation has helped promote better cybersecurity measures is in automating incident management and response tasks. After automatically detecting attacks, machine learning can respond to these attacks with no human intervention using cybersecurity tools such as SOAR software. The beauty of this is that it enables real-time response to threats around the clock, reducing the possibility of damage. Besides allowing real-time response to attacks, machine learning can help enterprises recover faster from attacks by enabling reinstatement to the pre-compromise state.


Cyberattacks have been a major struggle for enterprises as malicious people continually seek to cash in on the vast amounts of data that companies hold. However, machine learning and AI is giving a glimpse of hope in winning the fight against attackers. Even with more sophisticated attacks, machine learning-powered solutions are helping enterprises prevent the next attack through detecting anomalies, behavioral analysis, real-time response, and determining network risks. A Java Development Company can also be helpful for your privacy, beside the help for your business grown. This helps create an environment that is harder for malicious people to play, enhancing proactivity in cybersecurity.



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