The use of artificial intelligence in cybersecurity


The digital age has created several opportunities for us, and at the same time, we have been exposed to a whole new level of cyber threats. It is undeniable that cybersecurity has become an integral part of any business that wants to avoid falling victim to identity theft, data breach and other cyber risks.

Cybercriminals are constantly looking for ways to compromise networks and steal sensitive information. These techniques are increasingly advanced and can be difficult to detect by humans or traditional defense solutions.

For this reason, organizations are looking to adopt AI techniques to bolster their cybersecurity defense plan. Artificial intelligence in cybersecurity can provide organizations with information to help them understand and defend against these threats.

Read on to learn more about how to use and benefits of artificial intelligence in cybersecurity.

But first, we are going to introduce you to artificial intelligence and its types.

What is Artificial Intelligence?

Artificial intelligence simulates the human intellect in machines, especially computer systems. AI aims to program devices to think like humans and mimic their actions. Also, we can attribute this term to any machine that can exhibit traits associated with the human mind, such as problem solving and learning.

The term “artificial intelligence” was coined at a summer conference at Dartmouth University. Today, the application of AI has a significant impact on people’s lives. We now have machines that can drive cars, understand verbal commands, distinguish images and play games.

One of the impressive characteristics of artificial intelligence is its ability to reason and take the best actions to achieve a particular goal.

Types of artificial intelligence

There are several types of artificial intelligence, and familiarizing yourself with them will help you understand. Below are some popular types of artificial intelligence.

Weak (narrow) AI (ANI)

This is also known as narrow artificial intelligence, and it is one of the most frequently experimented with forms of AI. Every AI machine we use every day is weak and narrow AI. This type of AI operates under limited restrictions. For example, a voice-recognition artificial intelligence machine predicts people’s voices based on the dataset used to train it. Weak or narrow AI comes in two types.

Reactive machines

This type of AI machine sees the world directly and acts on what it sees. Artificial intelligence is completely reactive, with no memory or actions based on past experience. Rodney Brooks, an AI researcher, argued that people should only design this kind of AI in a seminal.

A great example of a reactive machine is Deep Blue, a chess-playing supercomputer from IBM that beat grandmaster Garry Kasparov in 1997. The supercomputer didn’t act on a preconceived data set or research previous matches.

Limited memory

This is similar to reactive machines but with a historical data set, improving decision making. Most AI machines available today are limited-memory machines powered by datasets. AI machines use deep learning and are trained to use a huge amount of data stored in their memory to act as a reference model to solve problems.

A typical example is an AI object detection machine designed to identify and label certain things like a car or a house from a photo. It can identify a car or a house from the previously formed historical data set.

Strong AI (general) (AGI)

Another category of AI machines is strong AI or artificial general intelligence. This design allows an AI machine to apply skills and knowledge in different contexts. These machines are closer to human intelligence as they provide opportunities for problem solving and self-directed learning.

You will find this type of AI in sci-fi movies. Nowadays, AGI can show superhuman performance on a limited number of tasks. (For example, object detection, noise detection or task automation). We can classify AGI machines into two main forms:

theory of mind

It’s an advanced class of AI technology, but it still only exists as a concept. Theory of Mind AI machines need to understand the behavior and feelings of people in an environment. This advancement in AI simulates people’s emotions, thoughts and feelings. A good example is Kismet, designed in the 1990s with the ability to mimic human emotions and identify them.


This type of AI can be seen in AI movies. AI machines that can think independently and outrun humanity are the ideals of self-aware AI. Most people believe that the the future of AI in cybersecurity may be closely related to humans.

Artificial Superintelligence (ASI)

This form of AI is more advanced than strong AI, with the potential to play a key role in the evolution of human beings. These AI machines will not only replicate human intelligence, but will be better at everything. UPS machines will be designed with better decision-making capabilities, faster data analysis and processing, and better memory.

Popular AI models in cybersecurity

The cybersecurity issues facing businesses today are enormous. Detecting and avoiding these attacks can be daunting. Cybersecurity researchers have recently designed security models and made predictions using AI models to counter these issues.

There are a wide variety of AI models, and some are more effective than others at solving specific cybersecurity problems. That said, it’s essential to understand the AI ​​models that can help strengthen your cybersecurity plan. Below are some popular cybersecurity AI models.

Logistic regression

This AI model can provide binary results. It is a statistical method used to predict the probability of events by considering historical data points. For example, logistic regression can predict whether a person will win an election or whether it will rain. This AI model also provides benefits in the area of ​​cybersecurity as it can help predict whether an email is spam or not.


Decision trees

This is the simplest AI model used in cybersecurity. It is a binary tree consisting of a “Yes” or “No” decision with each division until it reaches the resulting node. The AI ​​model is easy to implement and does not require data normalization to address multiple cybersecurity issues. This algorithm can help intrusion detection systems understand signatures and classify events on the network. 2d


naive bayes

It is a simple yet robust model for solving a wide range of complex problems. This AI model can help determine two types of probabilities:

  • A chance that each class appears.
  • A conditional likelihood for a standalone class, given an additional x modifier.

The model gets its name from the fact that it operates based on the assumption that all input values ​​are distinct from each other.

Although not an actual event, cybersecurity experts can apply the AI ​​model to normalized data streams to predict outcomes with high accuracy. A Naive Bayes classifier can be used for intrusion detection to classify whether an attack is present or not.


Support vector machines

This AI model is widely used by data scientists as it provides robust data classification capability.


random forest

It is a tree-like AI model that uses multiple decision trees to achieve a single outcome. It can be used for both regression and classification problems. This model can also be used as a classifier in network intrusion detection.


How is AI used in cybersecurity?

There are many benefits of artificial intelligence in cybersecurity. First, AI can help analyze large amounts of data to provide IT security personnel with better insights into what is happening on the network.
Additionally, the AI ​​systems feature predictive intelligence with advanced algorithms that help detect abnormal activity before it harms your system.
Cybercriminals are constantly looking for vulnerabilities in your network that can be compromised. But with AI, you can learn more about the network and detect the weakest link in the system.

Final Thoughts

The cyberattack space is increasingly advanced and cybercriminals are finding more creative ways to execute their evil plans. For this reason, organizations are turning to AI to bolster their defenses and help mitigate cyber risks. AI offers many cybersecurity benefits, including but not limited to vulnerability management, risk prediction, threat detection, and network traffic monitoring. We hope this article has given you some insight into the use of AI in cybersecurity.

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*** This is a syndicated blog from the Security Bloggers Network of EasyDMARC written by EasyDmarc. Read the original post at:


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