Cybersecurity, also known as computer security, refers to protecting computers, servers, electronic systems, mobile devices, private networks, and everything in between. Yes, the antivirus you have installed on your computer is a part of the cybersecurity paradigm.
End users typically don’t think too much about security concerns these days. It’s mostly an enterprise-level ballgame. And this is why the surface of attack is very large!
Risk assessment is a critical part of modern-day software deployment. The enormous amount of data that needs processing to assess risks and find vulnerabilities is often mind-blowing to the uninformed.
In fact, security incidents have gone so far and beyond that most experts agree this caliber of data processing is no longer a human-scale problem. This brings me to the topic of discussion for this post.
AI cybersecurity. It shouldn’t come as a surprise to any of you. Artificial intelligence is everywhere these days. Security teams at large organizations as well as small business offices deserve to use them just as much as other industries.
What Is Artificial Intelligence?
Artificial intelligence is a branch of computer technology that works to simulate human intelligence in machines. The goal is to see if machines can be intelligent and do the tasks typically reserved for skilled humans. When I say machine, I don’t mean sentient robots that look like humans. Rather, any electronic machine can be equipped with AI, including agricultural robots.
The primary difference between AI algorithms and regular computer programs is the training data. AI systems are trained to analyze datasets. The purpose is to derive patterns that the system can use to make meaningful decisions. The most important distinction here is the fact that Ai can learn and take decisions independently.
For us, artificial intelligence AI is a new concept. Especially, the buzz is going stronger than ever since OpenAI launched ChatGPT and the subsequent GPT-4.
But the idea of intelligent machines was derived way back in the 1950s. It was Alan Turning who thought of it. For this reason, the test of a machine’s ability to mimic human intelligence is called the Turning Test.
What is AI Cybersecurity?
There’s not much AI can do that humans cannot. It actually comes down to the size of the data sets and the speed they’re processed at. The same data set that takes a team of humans one week to analyze, AI can do it within seconds.
This is why artificial intelligence is such a prominent factor in cyber threat mitigation. The cybersecurity paradigm itself is very complex, requiring billions of variables to work and sustain.
Cybersecurity also has a very unique set of problems that require solving.
For starters, the attack surface. In tech terms, attack surface simply means the sum of security vulnerabilities of the infrastructure to cyber threats. If you think about it, every computer and mobile device on planet Earth falls into this category.
Compared to the number of computer systems that require protection, the security teams are like a drop in the ocean. It also takes a very long time to master the intricacies of critical systems, making the learning curve very steep for newcomers.
To solve this scalability problem as well as to mitigate cyber threats, AI technologies are being used.
How AI Can Benefit Security Teams?
So, why cybersecurity AI? Why not any other technology?
Well, it again comes down to AI’s ability to analyze data faster than all human analysts combined. When put into action, AI comes ahead in detecting attacks faster and more accurately.
This automatically means security teams don’t have to deal with as many false positives.
This is just one example of what AI in cybersecurity can do. Let me illustrate some other points to help you understand how AI Technologies can benefit the security infrastructure online.
To this day, malware infestation is one of the largest threats to cybersecurity professionals. The hackers that make these programs are always one step ahead of security professionals.
If you’re not aware, a malware is a “software that is specifically designed to disrupt, damage, or gain unauthorized access to a computer system”.
Protection against malware is one of the best use cases for harnessing AI power. With help from big data and machine learning, security experts can now analyze more data than ever before to identify useful patterns.
At the end of the day, all malware is bound to share some characteristics. The data set has just been too big for human intervention. With AI-based solutions in place, it’s a lot faster and easier to detect malware nowadays.
A phishing attack is perhaps the oldest trick hackers play to get users to spill sensitive information. We’ve all had experiences with phishing where we thought we were logging into a new site, only to realize that it was a data breach.
Hackers try to get your passwords, bank account credentials, credit card numbers, social security numbers, etc., by giving you something so lucrative that you can’t ignore it. This is very similar to the psychology of fishing. Hence, the name.
It might be hard to believe for many of you but phishing cyber attacks are still at large! We don’t encounter them very often simply because the hackers have changed their target demographic. It’s mostly uninformed communities like the elderly with social security, a group not well-versed in modern technology.
The good news is that data breaches with phishing attacks can be neutralized with AI-based systems. The predictive analysis power of artificial intelligence can anticipate phishing attempts in advance.
At their core, these attacks try to be as unsuspecting as possible. They often clone a well-known platform to trick the users into logging in. But with threat intelligence, AI models have access to the security signatures of the original site.
So, when a user is exposed to such a threat, AI-based cybersecurity systems will block the attempt.
Security Log Analysis
Did you know that your operating system is logging events as you’re reading this post? Security logs are an essential part of modern-day threat detection for cybersecurity teams.
But it also suffers from the same problem as the rest of the cybersecurity teams. The sheer volume of data in the logs. You can test this theory by visiting the events log on Windows or Mac devices. You’ll see hundreds of logs within a few seconds of turning the machine on.
The good thing is that humans can now delegate the tedious work of analyzing security logs to AI. Machine learning algorithms have come long enough to automate threat detection.
There are more private networks in the world than ever before and the number is only rising by the day. More devices are connecting to the internet which also means the attack surface is expanding.
Having all of these devices connected to the same network also means traffic from all kinds of devices is entering your network. The typical cybersecurity defenses are no longer enough to keep this vast amount of traffic safe.
AI-powered systems can make the job a lot easier for cybersecurity teams. The same principle of training AI systems comes into play here. And there is an abundance of network data in the world to train reliable AI.
Essentially, AI in cybersecurity can monitor all the incoming traffic for threats like malware and phishing attempts, protecting your internet browsing experience.
Here, the “end” is a device. The phone or computer you’re using right now to read this post is an endpoint. You might argue that you have an expensive antivirus installed and you get security alerts if something goes wrong.
You’re right. Antivirus is still a very good investment for personal security. The problem, however, is the inherent working of these tools.
Most antivirus and VPN encryption protocols work based on a signature system. It’s very much possible that the hacker creates a new type of malware before the security provider can update their signature.
But cybersecurity artificial intelligence redefines threat detection. Instead of relying on signatures, it performs by analyzing user behavior on particular devices. As a result, when something acts out of the ordinary or other security incidents happen, the AI system kicks in to protect the user.
This is fascinating because most malware stays in the system for very long, becoming insider threats without you catching a drift. By the time you realize it, it’s too late.
How AI is Changing the Cybersecurity Landscape
The topic I’m discussing here, AI systems for cyber threats is not a brand-new concept. Many organizations have already deployed machine learning algorithms to achieve this. They work in harmony with security analysts to prevent data breaches and identify patterns.
Here are some ways AI is changing the landscape forever.
You already know that artificial intelligence AI can do weeks’ worth of work within a fraction of a second. If you ask me, this is one of the biggest strengths of this technology.
But that’s not it. See, many of us confuse AI with data analytics. Analyzing massive data sets and finding patterns does not make a model an AI. It’s simply data analytics.
What sets AI apart is its ability to understand the data and learn from it. It’s the thorough understanding that allows AI to offer a more efficient approach to zero-day vulnerabilities.
Modern threat identification systems can not only recognize suspicious IP addresses or threat actors quickly, but they can also take meaningful action to prevent any downfalls by optimizing incident response.
The accuracy of any artificial intelligence AI program depends on the size and quality of training data. It also depends on how the trainers have processed it before feeding the models.
The improved accuracy of natural language processing and machine learning results in fewer false positives and better breach risk prediction. False alarms have long been a pain point for information security professionals as it diverts attention from real threats.
IBM Security is one of the largest players when it comes to enterprise-grade threat intelligence. It also has some of the most accurate automatic investigators. The component that harnesses the power of AI from IBM Security right now is the QRadar series.
The cost-cutting aspect of cybersecurity AI doesn’t come into play until later. It’s simply because the initial setup for organizations is quite expensive.
However, in the long run, it’s a great savings opportunity for security teams and companies. When you have automated AI systems monitoring network traffic and device health 24/7, you don’t need a big team of security analysts for incident response.
Also, you can expect fewer false positives meaning you get to prioritize true security threats over false ones. Patch management is automatically handled. Even log analysis keeps happening in the background.
But the cost savings don’t come from human resources alone. As an AI algorithm can mitigate threat exposure much faster, the overall efficiency of the system improves. Don’t you think more efficiency means more margin on the profits?
Real-Time Threat Detection and Response
Speaking of 24/7 monitoring, it’s a very important element of any successful cybersecurity infrastructure. The existing systems in place often need human intervention to take action against emerging threats. But when AI in cybersecurity is used, the monitoring process becomes fully autonomous.
When paired with AI’s inhumane processing power, the security posture for any company becomes insanely strong. The system in place can immediately identify patterns and threats to take prompt action.
Of course, at the core of everything is machine learning. Threat hunting is one of the newest elements of these algorithms and they’ve proved to be very efficient.
The best part of AI in cybersecurity is that it doesn’t need constant updates to detect new threats. The decision-making ability of these systems allows them to detect new and emerging threats based on historical data.
This can dramatically reduce the time needed for response actions for big organizations that are more vulnerable to attacks anyways.
The last thing AI security tools can do for organizations is scale the volume of protection. Most organizations don’t have enough manpower to process the large volume of data that comes through the network every single day.
As it happens, AI systems are known for their scalability. Whether the network admin wants to monitor security logs, data logs, traffic logs, or any other kind of log, a toolkit like IBM Security should be able to handle it pretty easily.
This s more important than ever in today’s world with more IoT devices, remote workers, and cloud computing at large. In landscapes where the variables are constantly changing, there is practically no alternative to using AI-powered tools.
How AI is used in cybersecurity?
AI and machine learning are primarily used in cybersecurity to analyze risk data in real-time to derive quick action. Artificial intelligence is one of the most scalable technologies we have right now that can be effectively used by enterprise-grade organizations.
What is cybersecurity AI?
The term “cybersecurity AI” simply refers to an AI system working in the cybersecurity space to assess breach risk, evolve rapidly, and provide protection against emerging threats in real time.
Are cybersecurity and AI different?
Yes, they’re very different branches of computer science. Cybersecurity is the branch that aims to develop infrastructure for protecting computer systems. Artificial intelligence, on the other hand, is the branch that tries to use machine learning techniques to mimic human intelligence.