Impact of Artificial Intelligence and Machine Learning on Cyber Security

Impact of Artificial Intelligence and Machine Learning on Cyber Security

Authored By admin

December 31, 2021

Nowadays, the whole world is on the internet. From children to adults, everyone finds themselves indulging in tasks that are only possible through the internet. In the coming years, this dependency on the internet will only increase. There is a vast amount of data on the internet, which poses numerous risks to users and profoundly affects their security. 

Artificial Intelligence (AI) and machine learning (ML) technologies have ushered in a new era of automation and convenience. While these technologies are beneficial for the users, they can also be used by hackers to orchestrate cyber-attacks. 


Cybersecurity is defined as defending networks, systems, and any other digital infrastructure from an invasive, malicious attack like information theft, software or hardware damage, and other intellectual properties. 

Today, people store vast amounts of data on computers and other devices connected to the internet. Many of these are sensitive, such as passwords and financial information. Cybercriminals who gain access to such data could share sensitive data, steal funds, or even modify information for their benefit. Therefore, the concern about cyber security is at the top of everyone’s mind on the internet.

Companies and individuals can protect themselves from a full spectrum of cyberattacks by implementing cybersecurity measures. A company can manage its cybersecurity on its own or with the help of an outside company specialized in the field.

What are AI and ML

AI is a field of computer science that aims to build intelligent machines that can solve problems without needing human intelligence. Chess-playing computers, self-driving cars, SIRI all are heavily reliant on AI. AI allows computers to recognize patterns in large volumes of data and accomplish specific tasks. An AI algorithm uses real-time information to make decisions. 

Many tech giants like Alibaba, Amazon are fighting for the best position in AI worldwide.

We can call ML a subset of AI. It is possible to make predictions and make decisions using historical data without explicitly programming it through machine learning. 

Effects of AI and ML on Cyber Security

Researchers and security program manufacturers are attempting to harness the potential of Artificial Intelligence to detect, fix cyber threats, and control data breaches.

Security systems can use AI, machine learning to identify data patterns to learn from experience. Additionally, AI and ML enable businesses to respond to security incidents faster and comply with best practices.

Applications of AI and ML in cybersecurity

Vulnerability management:  As organizations encounter new vulnerabilities every day, they experience difficulty prioritizing and managing them. By analyzing baseline behavior of users, endpoints, and servers, AI and ML techniques, such as User and Event Behavioral Analytics, can identify abnormal behavior that might indicate a zero-day unknown attack. 

Organizations can benefit from this by protecting themselves even before vulnerabilities are reported and patched.

Improves Authentication: Login security has become highly secure and convenient with the new methods of biometric authentication as face recognition and iris recognition. The use of AI in biometric systems has made them impenetrable to cybercriminals.

Behavioral Analysis: Artificial intelligence develops patterns based on users’ working methods. The AI would detect abnormalities when malware is introduced into the system so that it would notify authorities.


Artificial intelligence is the future of cybersecurity as cyber threats become more and more complex both in terms of their number and complexity. An organization focused on strengthening cybersecurity and minimizing sensitive data loss can benefit greatly from machine learning and AI.

Similarly, hackers will have an easier time infiltrating systems without human intervention; hence considering its drawbacks is essential before implementing it.

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