AI and machine learning have become game changers in so many different areas of business. From logistics to finance to marketing, the possible uses of AI are endless. Recently, AI has earned a spotlight in cybersecurity as well. Keep reading to learn the main applications of AI and machine learning in cybersecurity as well as some of its advantages and disadvantages.
What is AI?
Artificial intelligence refers to a computer program that aims to mimic human intelligence while performing various tasks. The purpose of such software is to automate tasks and maximize productivity. With machine learning (the ability to use accumulated data to improve performance), AI can become increasingly advanced over time, often even more so than with human intervention and constant reprogramming.
How AI is different from a traditional approach to cybersecurity
As you might expect, traditional and AI-based cybersecurity solutions differ significantly in their approaches to handling various threats, especially AI cyber attacks.
Let’s start with the simple fact that traditional solutions rely heavily on signature-based detection systems, which means that they can only recognize previously identified threats. In contrast, AI-based solutions use machine learning to analyze patterns in data sets, and that enables them to identify both known and unknown threats.
The above also relates to the fact that traditional solutions use predefined rules and behaviors to identify threats and that makes them often miss anomalies or deviations. AI-based solutions, on the other hand, learn patterns and identify unusual activities with ease, which is what makes them so great at detecting insider threats and new attack methods.
What is more, traditional solutions can generate a lot of false positives, straining security teams and creating vigilance fatigue. Due to the fact that they analyze and thus better understand the context of each activity, AI-based tools can not only reduce the number of false positives, but also increase the accuracy of threat alerts.
Of course, we must also mention here that traditional solutions can have difficulties handling large amounts of real-time data, especially in large networks, due to their low processing power. AI-based solutions excel in processing huge amounts of data quickly — and that makes them a perfect match for enterprise-level cybersecurity.
Advantages of AI in Cybersecurity
The ability of AI to work independently and improve over time has proven to be immensely beneficial to businesses of all industries. The advantages can be especially seen in the cybersecurity industry. Unfortunately, cyber attacks have become increasingly advanced in recent years. Missing any crucial bit of cybersecurity-related information can be detrimental to a business.
Conventional cybersecurity measures based on regular non-AI algorithms aren’t always enough to deter cyber attacks. Here are the main benefits of AI in cybersecurity:
Detecting New Threats
Hackers are rapidly thinking of new ways to breach systems and cause damage. Plenty of great cybersecurity tools aim to identify these threats and eliminate them immediately. However, it’s nearly impossible to keep up with every new threat without help from AI.
Artificial intelligence doesn’t rely on a fixed database of known cybersecurity threats like traditional software often does. By analyzing past malware, scanning the web for news on emerging threats, and thoroughly analyzing all activity on a given system, it can identify even previously unknown threats and inform the user of the system immediately.
Better vulnerability management
The larger the organization and the more devices and users connected to a given database, the more difficult it is to determine the most pressing vulnerabilities. Artificial intelligence can continuously analyze all devices, authorizations, user habits, and software. It can then combine this information with its knowledge on the most urgent cybersecurity threats.
In doing so, AI can determine which systems, devices, or users are most susceptible to a cyber attack. Advanced AI solutions may even be able to indicate the potential monetary losses associated with a given vulnerability.
Improving endpoint security
An endpoint refers to any device that is connected to a given network. For example, businesses often have hundreds of devices connected to their network. Every one of these devices has the potential to be hacked. Many businesses have implemented various cybersecurity solutions, such as antivirus software and firewalls.
Most conventional cybersecurity software relies on signatures to detect threats. These signatures continue to be updated as new threats are detected and registered within the system. However, users failing to update software or developers failing to spot new threats means that many devices continue to rely on outdated cybersecurity software.
Endpoint protection based on AI and machine learning rather than signatures is significantly more secure. AI-based software can spot suspicious behavior based on patterns and subtle signs. As mentioned previously, it can discover even completely new threats and vulnerabilities by using its neural network. This ability makes it much more autonomous and less dependent on developers and the willingness of users to install updates.
Learning over time
Traditional cybersecurity software requires updates to function properly. In a way, such software becomes less advanced with time if the user fails to install updates. AI-based software is quite the opposite: with time, as it is exposed to more data, it becomes smarter. It does so through machine learning – AI can identify patterns by analyzing new data and use this information to continuously build its neural network, making it increasingly advanced.
Enhancing user experience
AI can be extremely helpful in improving user experience. For example, here at NordPass, we’ve integrated machine learning into our autofill option, making the new and improved autofill far more accurate and faster than any signature-based option.
Downsides of AI in Cybersecurity
There’s no denying the advantages of AI in cybersecurity. However, here are some cybersecurity challenges that must also be considered:
It can be used by malicious actors as well
Hackers can also use AI. More specifically, they can manipulate it. When creating new malware and writing malicious code, hackers can analyze how AI reacts to it. They can then tweak the malware so that the AI will no longer be able to detect it properly. Instead, the AI will act in favor of the hacker and even aid in the attack.
It requires significant resources
AI is complicated, and it requires a tremendous amount of resources. Such resources include money, talent, and as much data on cybersecurity and malware as possible. (The more data that AI is based on, the more accurately it works). AI that lacks crucial data and is not implemented at an expert level will likely fail to detect threats. Ultimately, poorly executed AI will cause more harm than good.
Of course, not every cybersecurity business has the means to implement AI into its practices. There’s no saying that businesses that lack AI are inherently bad and unreliable. However, when choosing any AI-based cybersecurity solution for business, make sure that the company knows what it’s doing and that the tool will allow you to address the aforementioned cybersecurity challenges.
Types of AI cyber security tools
Cybersecurity AI solutions can be divided into several different types, including:
- Anomaly detection tools, which can identify deviations from normal network or user behavior. They analyze patterns to detect unusual activities that might indicate a breach, insider threat, or other malicious activity.
- Behavioral analytics tools that learn the typical behaviors of users, devices, and entities within a network. They can identify abnormal actions, helping to detect advanced threats that may not have recognizable signatures.
- Predictive analysis tools, which use historical data and machine learning algorithms to predict potential cyber threats, vulnerabilities, or attacks. This proactive approach allows organizations to take preventive measures in advance.
- Network security tools that monitor network traffic in real-time, identifying abnormal patterns that might indicate a breach or intrusion. They enable quick detection and response to threats.
- Deep learning-based threat detection tools that can analyze and understand complex relationships within data to detect sophisticated threats that might be too challenging for traditional solutions.
- Security information and event management tools, which analyze security data from various sources to provide a comprehensive view of an organization's security posture and identify potential threats.
Bottom line
New cyber threats keep emerging every day, and companies are more vulnerable to hackers than ever. AI and machine learning may be the best solution to help keep companies in the loop at all times. With enhanced threat prediction, better endpoint security, and several other benefits, AI-based cybersecurity solutions are certainly the way to go.