The Impact of AI and Machine Learning on Cybersecurity

Many experts believe that Artificial Intelligence (AI) and Machine Learning (ML) have played a critical role in developing and fortifying cybersecurity. As the world turns more digital and connected through the internet, cybercrimes have increased in number and sophistication. Human brainpower alone is not enough to manage these growing and sophisticated threats to cybersecurity. 

Defining Key Terms

Cybersecurity

IBM defines cybersecurity as the practice of protecting sensitive information and critical systems against malicious digital attacks. Cybersecurity is an important aspect of websites and cloud-based applications. It ensures safe transactions and sharing of data and secure confidentiality of messages. 

Artificial Intelligence and Machine Learning

Ai refers to the machine’s intellect or brainpower, which is different from those of humans and non-humans. When professionals use artificial intelligence for cybersecurity and other processes, it is called machine learning. Machine learning technology utilizes data and algorithms to collect data and improve accuracy. 

These technologies can learn and improve performance by gathering a tremendous amount of information and analyzing them. AI and machine learning can draw accurate conclusions based on past experiences and results. 

AI and Machine Learning Impact

AI has the ability to collect millions of events and data that may come from structured and unstructured sources. Regardless of the source’s origin and number (which can reach up to billions), AI can consume these data and analyze them using machine learning techniques. By using machine learning techniques, AI learns and adapts quickly and uses logical reasoning to identify threats, attacks, and other malicious files. 

Cybersecurity experts will then use these analyses to deal with digital threats and make more informed decision efficiently. 

Threat Detection Automation 

The use of AI and ML allows cyber security officers to detect new threats at record speed. This is something that traditional cybersecurity is not capable of doing Because the detection is quite fast, and AI and ML can deal with a high volume of attacks. 

Aside from speed, AI and ML can also detect even the smallest risks or suspicious behavioral patterns. These technologies can be used to follow a tight screening process that filters threats even before they enter the system.

AI can also be a cybersecurity tool that can execute predictive analysis. This means that AI and machine learning can detect a pattern of behavior and can conclude if the security team needs to address cyberattacks and anomalies. 

Managing Bots

A large portion of internet traffic today comes from bots. There may be some uses for bots connecting to websites, but many have malicious intent. Every day, millions of bots attack websites, and some of them are sophisticated enough to dodge standard website security. They can steal data and take over accounts. For this reason, cybercriminals are using bots to infiltrate the security system of websites. 

AI is crucial in managing the large volume of bots coming into websites. This number can be too much for a manual response. Furthermore, machine learning strategies can analyze website traffic and detect threats from suspicious bots.   

Stopping credit card fraud

Banks have been using AI and ML to keep their customers’ information safe to prevent credit card fraud. In this case, AI can study purchases and transactions made from different devices. AI can then detect unusual activities based on the data gathered. In addition, AI systems, and machine learning procedures can assist in verifying credit-card holders to decrease the risks of fraudulent transactions. 

Predicting Risk

AI is useful not only when managing incoming threats, but it is also capable of accurate predictions that provide maximum protection to organizations. AI can keep asset inventory and calculate a system’s exposure to threats. In other words, AI technology can predict how and where security breaches can occur. It will help organizations determine weak security spots. With such information, the security team can organize tools and resources to strengthen these weak security spots. 

Predictive insights are valuable as they will improve resilience to cyber attacks. 

AI and Machine Learning Challenges

There is no doubt that cybersecurity software models will integrate AI and ML in the near future. Large companies like IBM/Watson, Google, and Juniper Network have been using AI and machine learning in their fight to maintain cybersecurity. However, there will always be challenges when it comes to using these technologies.

One of them is cost. AI systems are expensive to install and implement because they use a lot of computer power, memory, and data. Organizations must gather many data sets like codes and anomalies to feed to their new AI system, so the system can distinguish and analyze data. 

AI and machine learning offer many advantages to combating cybercrimes and threats. However, cybercriminals can also use these technologies to carry out their attacks. AI and machine learning can increase the speed and accuracy of attacks. They can cause the system to misinterpret incoming data. These enhancements make it harder for cybersecurity teams to detect and eliminate the threats. 

AI is powerful enough to unlock face recognition security, conceal malware, and increase attacks on cloud-based services.  Here are other cases of data breaches 

The rise of AI can also make cybersecurity a difficult task. The use of this technology for cybercrimes is something the world has to expect and prepare for.

Conclusion

Artificial intelligence and machine learning offer several benefits to cybersecurity. Experts can use these technologies to recognize threats, anomalies, and suspicious behavior that may have unknown patterns.  

There have been concerns that our heavy reliance on these two technologies will lead to the replacement of humans in the cybersecurity sector. But the current sophistication of AI and machine learning still requires the help of human experts. Even with AI, data engineers are still necessary to build pipelines to collect data from various source systems. Cybersecurity experts still need to manage security protocols, software and hardware audits.