Dmitriy N. Full-Stack Developer

4 ways of how AI changes cybersecurity


In today's ever-evolving, high-tech, business environment, business leaders need reliable information to set high standards, improve security protocols, and secure sensitive information. Decision-making with artificial intelligence techniques helps companies find threats, prevent risks, and take appropriate action. Moreover, artificial intelligence (AI) and machine learning (ML) techniques play a crucial role in improving cybersecurity protocols.

1# Timely response

Implementing artificial intelligence and machine learning in cybersecurity allows suspicious actions to be identified and effective action to be taken, thereby preventing disruptions to business systems. Organizations seek to provide a secure environment by automating security functions and minimizing, or eliminating, human assistance.

Artificial intelligence algorithms also help track employee actions. AI-based cybersecurity tools generate instant alerts when suspicious activity is detected, creating an effective fraud prevention system. Thus, thanks to AI and ML, the cybersecurity infrastructure has improved significantly.

2# Analysis of network security

For the most part, traditional network security practices focus on two main aspects that are addressed by AI-based cybersecurity measures:

Developing a security policy

Security policies help identify legitimate and malicious network connections. However, creating, managing, and maintaining this kind of policy for a huge number of networks is difficult.

Developing an understanding of the network environment

Many organizations do not have precise naming conventions for workloads and applications, resulting in security services having to spend a lot of time and energy determining which group of workloads belongs to which applications.

By learning network traffic patterns and recommending functional workloads, artificial intelligence plays an important role in improving network security protocols. In addition, AI optimizes and controls critical data center processes.

3# Vulnerability Management

Traditional vulnerability management techniques tend to react and negate high-risk vulnerabilities only after cybercriminals attempt to exploit them.

There is no doubt that machine learning and artificial intelligence can improve vulnerability management and database security. Innovative tools such as user behavior analysis, when integrated with AI algorithms, can analyze user behavior on servers as well as on endpoints to detect anomalies that indicate an unknown attack.

4# Identity Theft Prevention

Artificial intelligence-based identity verification solutions and the implementation of security measures play an important role in combating cybercriminals. Since the technological revolution, identity theft has become one of the most common security issues. Cybercriminals use many ways to gain unauthorized access to individuals' sensitive data. And as the number of hackers increases very quickly, more and more network outages and unauthorized access attempts occur. Undoubtedly, AI-based solutions can help companies in any field, whether it's finance, healthcare, education or tourism.


Because hackers will never stop, organizations must stay one step ahead in the fight against tech-savvy criminals. Companies today need to invest and improve their cybersecurity practices to detect identity theft by implementing artificial intelligence-based cybersecurity protocols.

"The third wave" of AI provides effective real-time protection for network resources. Consequently, it's time for organizations to start implementing all mandatory identity verification solutions to combat illegal money transfers, unauthorized access and fraudulent activities.


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