Is AI changing the cybersecurity landscape?

The future is very uncertain, but there have been a lot of developments in the technology industry. Now the question that arises is, “Is AI changing the cyber security landscape?”. The question to this answer is yes. One of the significant game-changers is that the tools and techniques are developed and supported using artificial intelligence in cyber security, and machine learning is a subset of this. 

The term artificial intelligence is no longer a buzzword. Customer service, education, and automation are used in many sectors, and AI has instigated the advancements by leaps and bounds. 

Can artificial intelligence negatively affect cyber security?

It is a high probability that the attackers can weaponize with the help of artificial intelligence and use it to expand their attacks. One of the major concerns is that attackers can use artificial intelligence considerably. The enemies are focused on relying on human resources to organize their attacks. Crime and cybersecurity will change when they learn machine learning and AI to do their work. The students of IT can save their time by using AI to connect with people and protect the projects. 

Another critical issue of machine learning and artificial intelligence is to complement human resource storage and save cybersecurity costs. The enemies can also use it similarly. The resources and finances needed to coordinate and launch such attacks will go down quickly. Additionally, the a lower investment for cyber-attacks, and there is a significant threat to cyber security. 

The AI advancements can also result in different types of cyberattacks threats. Artificial intelligence can also take advantage of the system’s vulnerability faster and in a better way than human beings. Additionally, the attackers use AI to disguise their attacks. The victim might know their device and the network has been affected. 

Uses of AI in cybersecurity:

During these years, there has been a lot of interest in exploring AI to improve cybersecurity practices. There are a lot of machine learning cases that are supervised based on the strategies of machine learning. Unsupervised machine learning strategies are in the experimentation phase. Keeping these in mind, there are some of the examples of artificial intelligence to improve cyber security:

1. Detecting intrusion:

Machine learning helps to detect and defend against intrusions. Besides that, simple rules of logic should be based on this. Once the specific set of behaviors is learned with the help of a machine, it is based on the frequency of queries, the number of access attempts, and the amount of data in the question. The outliers get flagged automatically as suspicious without any kind of human intervention. 

1. Detection of malware:

In most cases, there is new malware created by cyber attacks. Once these people have achieved their goals, creating a subsequent variant also becomes automatic. The integrated machine learning techniques with the signature-based systems can help identify the future variants and the versions of malware to prevent the spread. 

1. Discovery of areas of vulnerability:

This is the very latest area of application where the human resources and the developers can scan a massive amount of code. It also helps to automate identifying vulnerabilities with the help of machine learning before the cyber attackers do. 

1. Detection of fraud:

Many fraudulent activities are carried out in different industries. The fraudulent transactions can be noticed to be prevented by identifying deviations and detecting patterns from unexpected behaviors. Anomaly detection is one of the best machine learning applications that sift through many event logs. Additionally, the use cases are not something new because there are things that the IT experts have had expertise in for many years. One of the significant differences is that artificial intelligence is used to make them much more secure and robust. Enterprises can also reduce the total time it takes to respond or identify any kind of threat that extends to the AI approach. 

1. Enhancement of threat intelligence 

With the help of machine learning and threat intelligence techniques, there has been a lot of improvement in threat detection rates. It also reduced the number of threats. 

1. Password protection and authentication

When there are problems with security, passwords there are always issues regarding the fragile control. And many cases, there are many barriers between the accounts and the cybercriminals, and saving these passwords as their draft messages on your devices is a huge security risk. 

The automation of biometrics has also been an excellent alternative to passwords. This is not convenient for the hackers, and they can not bypass it that quickly. A face recognition system also makes it much more difficult to access your devices, significantly when you change your hairstyle or wear any kind of glasses. The attackers can also take advantage of this. 

It is essential to use advanced technology such as AI as it has changed a lot over recent years. Its development in the security sector is considered very helpful.

You can also check thee relevant articles:

Artificial Intelligence (AI) and Intelligent Automation – An “ATM” for IT?

Continuous Threat Exposure Management: Using AI to Protect Organizations