5 ways AI is changing cybersecurity

How artificial intelligence (AI) and machine learning will change the world we hear about almost daily. It’s sometimes fun to speculate and play the game of “what ifs.” However, as one famous character said, “The future is now, old man.

So, where is cybersecurity going? If we examine today’s trends, the answer is somewhat repetitive – automation, automation, and more automation.

What is artificial intelligence?

Artificial intelligence is a simulation of human intelligence in computers and machines. In other words, AI seeks to mimic (or even advance) our problem-solving and decision-making abilities.

This process aims to automate certain tasks so humans don’t have to do them constantly. For example, if you teach a machine to sort out your email – you’ll have more time for other projects. 

However, you can also build a code that automatically spams other people’s inboxes with ads. On top of that, you can also program it to learn how to bypass email filters. The best (or worst) part – you may not need to lift a finger afterward to keep generating ad revenue. The code will more or less do it by itself.

Essentially, AI is a double-edged sword. You can use it to automate almost any type of task. Whether it’s good or bad depends entirely on your intentions.

Top 5 most interesting cases of AI in cybersecurity

Top 5 most interesting cases of AI in cybersecurity

Likewise, people use AI in cybersecurity for different purposes. On one side, we have hackers who use AI to improve their attacks. And on the other side, cybersecurity experts use deep learning to build defenses against cyber threats.

Below, I picked out what I find the five most interesting AI uses in cybersecurity. Let’s get started!

1. AI for creating and fighting phishing scams

Phishing attacks are some of the most deadly and costly cyberthreats today. Hackers use them to persuade people into giving away their credentials, financial and personal information.

For example, spear phishing scams use people’s personal information to seem more authentic. They are the most deadly phishing scam of the bunch. 

These days, hackers use AI and machine learning to automate these attacks. The majority of them are carried out via email.  And these emails are now filled with AI-powered malware that can fill your device with untraceable applications. 

To stop these hackers, experts have begun using AI systems to combat phishing attacks.

Let’s say you install this AI app on your computer. Over time, it will learn about your mailbox and the way you communicate. The AI will then look for inconsistencies and block any phishy emails. 

It also scans your inbox for: 

  1. Malware links.
  2. Fake login pages.
  3. Tracking pixels.
  4. Virus-ridden attachments.
  5. Forged signatures.

It’s one AI against another, and you can use that to gain an advantage!

2. Generative adversarial networks: how AI learns by competing against itself

A generative adversarial network (GAN) is AI built to learn without supervision. 

A GAN is based on a game where two machine learning algorithms compete. One of them is named generator, and the other – discriminator.

The generator constantly simulates content, and the discriminator tries to spot its opponent’s mistakes. You can look at it as a game between cops and forgers.

The cops are trying to catch the forgers and ensure the use of legitimate money. On the other hand, the forgers are trying to make the fake money seem as real as possible.

The GAN strategy can be applied to cybersecurity as well. One AI is simulating threats and attack vectors, while the other is trying to spot them. This way, you can protect yourself from threats that hackers haven’t figured out yet themselves!

3. Biometrics and AI to change passwords

Another thing that GANs are very useful for is image recognition. This has allowed the marriage of AI with biometrics and facial recognition.

Biometrics is a simple way to say “body measurements and human characteristics.” And yes, devices use biometrics. A camera can detect a person’s face and features while the software processes the data. It’s a very convenient way to identify someone.

Passwords are weak. We often hear how you should never use the same password for a different service. But let’s be real – having dozens of different passwords is just too inconvenient.

Biometrics promise to solve this problem. A single glance at the camera lens will soon be your credentials. But take this “soon” with a bit of salt – biometrics still have a way to go.

The problem with current biometric technology is that hackers can easily exploit it, and the systems themselves are very inaccurate. 

AI and machine learning show a lot of promise for the field. And experts hope that biometrics will advance rapidly in the coming years. However, biometrics as identifiable data will raise many concerns and issues about our personal privacy.

4. Antivirus use AI to improve their security

Antivirus systems are as old as Madonna’s “Who’s that girl?” (seriously). Traditionally, antivirus developers used data signatures and files to look for patterns in threats to stop them.

This method is old and time-tested – it just works. However, recently there has been a great spike in advanced malware and ransomware scams. As I mentioned before, hackers have begun using AI, among other tools, in their attacks.

For these reasons, traditional antiviruses simply can’t hope to keep up with the game. Security tools like Avast and Windows defender use AI and machine learning to improve their security.

They automate their detection and identification systems to give your security the oompf it needs. However, there’s an even better way to approach antivirus software. It’s called deep learning.

5. Deep learning to identify and predict cyber threats

Deep learning is a subset of machine learning that is based on artificial neural networks. 

Such a network system has multiple layers inside of it. First, the network receives raw data. This information travels between multiple layers and keeps changing to allow the network to make predictions. Using this method, AIs can learn through processing data on their own.

Cybersecurity systems like Deep Instinct use deep learning to teach themselves using threats. They are programmed to take raw data from malicious files and analyze it in so many ways that a human never could.

This results in systems that constantly learn and can spot new viruses and malware in almost zero time. They have generally higher detection rates and fewer false positives.

The future promises to be wild – don’t stay out of the loop!

Like most fields, cybersecurity’s future screams uncertainty and excitement. This amazing technology seems to be just around the corner. But what does that mean for us?

The need for automation shows that our lives are growing more complex. That’s a scary thought. But we need to stay vigilant and informed about what’s going on in the tech world. Subscribe to our newsletters if you want to keep in touch with everything cybersecurity!