We outline some of the more popular use-cases for Al in cybersecurity in this article, where there is some indication of practical business application. In particular, we discuss:

  • Al for Email Monitoring and Network Threat Identification
  • Al-based User Behavior Modeling
  • Al-based Antivirus Software
  • Al for Combating
  • Al Threats

We start off by explaining why Al is such a good fit for cybersecurity before diving into our analysis of AI in the field.

It may take a few days or weeks for human experts to comprehend these policies completely and ensure that the security implementation is carried out successfully.

Difficulty and repetition are inherent to cybersecurity. This is due to the fact that identifying and evaluating cyber threats necessitates going through a lot of data and searching for unusual data points. Companies can train Al algorithms to detect new cyberthreats by feeding it data gathered by their rules-based network security software.

Recognizing the attack’s effects and the necessary response from the business also needs more data analysis. With input from cybersecurity subject-matter experts, all algorithms can be trained to execute specific predefined actions in the event of an attack, and they can eventually learn what the best course of action should be.

Al in software development can perform these data analysis tasks at a speed and scale that human security experts cannot match. Furthermore, cybersecurity data analysis software based on Al can consistently perform the task with a higher level of accuracy than human analysts.

Anomaly detection and large-scale data analysis are two areas where artificial intelligence (AI) may be useful in cybersecurity today.

One technique to identify cybersecurity threats is to monitor the data entering and leaving the network, as this is where most cybersecurity intrusions typically occur in enterprise networks. For human analysts, accurately monitoring every single “packet” of data that is a part of the enterprise network’s communications is nearly impossible.

A variety of methods, including statistical analysis, keyword matching, and anomaly detection, may be employed by machine learning-based software to assess whether a particular data packet differs sufficiently from the baseline of data packets included in the training dataset.

All of this seems to suggest that Artificial intelligence & Machine learning is beginning to be recognized as a useful tool for gaining significant advantages over hackers and scammers.

Al for Network Threat Identification

For the majority of businesses, enterprise network security is essential. The most challenging aspect of developing effective network cybersecurity procedures is comprehending the many components that make up the network topography. This means that tracking every communication entering and leaving the company network will take a lot of time for human cybersecurity experts.

It is necessary to distinguish between connection requests that are valid and those that attempt unusual connection behaviour, like sending and receiving large amounts of data or having unusual programs run after connecting to an enterprise network, in order to manage the security of these networks.

Finding the potentially malicious components of an application, whether it be on the web, a mobile device, or one that is being developed or tested, is a challenge for cybersecurity specialists. In a large-scale enterprise network, sifting through thousands of similar programs to find the malicious applications takes a lot of time, and human experts are not always reliable.

All incoming and outgoing network traffic may be monitored by al-based network security software, which can then use this information to spot any unusual or suspicious patterns in the traffic data. Typically, the amount of data involved is too great for human cybersecurity specialists to correctly categorise threat incidents.

The startup ShieldX Networks provides a real-world example of how they use AI to expedite the process of determining which security policies apply to each application. Furthermore, according to the company, its software can analyse network communications data for individual applications over time and then produce recommendations for security policies specific to those applications.

In addition, Al vendors in the banking industry, like the one that eSentire recently acquired, provide enterprise cybersecurity Al software that employs anomaly detection to find threats to network security. According to the company, its software can assist banks and financial institutions with cybersecurity threat management and adversary detection.

The VSE Versive Security Engine, an enterprise cybersecurity Al software product from an Al vendor that eSentire has since acquired, is advertised as having the ability to assist banks and other financial institutions in applying machine learning to analyse sizable datasets of transactional and cybersecurity-related data.

According to Versive, the Versive Security Engine receives inputs in the form of proxy, DNS (computer network data), and NetFlow (a network protocol created by Cisco for gathering IP traffic information and monitoring network traffic). The software can then use anomaly detection to monitor enterprise networks and notify human officers of any data deviations that may resemble previous cyber threat events.

Al Email Monitoring

Businesses recognize the value of keeping an eye on email correspondence to thwart cybersecurity hacking attempts like phishing. Cyber threat detection speed and accuracy are now being increased with the use of machine learning-based monitoring software.

For this use-case, a variety of AI technologies are being employed. To “view” emails, for example, some software employs computer vision to look for elements that could be signs of a threat, like specific-sized images. In certain instances, natural language processing is employed to scan emails entering and leaving the company and spot words or formatting patterns linked to spear phishing attempts. Finding out if the sender, recipient, body, or attachments of an email pose a threat can be facilitated by utilising anomaly detection software.

This use-case demonstrates once more how adept Al is at analysing massive amounts of data. While it is not hard for a human employee to scan through an email and spot suspicious content, it is just not feasible to do so for the millions of emails that are sent and received on a daily basis within large organisations. Alternatively, Al software can scan all incoming and outgoing emails and notify security staff of the cybersecurity threats that are most likely to occur.

For example, makes the claim to offer email monitoring software that can assist financial institutions in preventing phishing attacks, data breaches, and misdirected emails. The company’s software probably employs anomaly detection and natural language processing in separate stages to determine which emails pose a risk to cybersecurity.

Al-based Antivirus Software

The issue with this strategy is that it depends on antivirus software security updates whenever new viruses are found. Furthermore, this technique makes deploying a scalable system difficult and slows down traditional antivirus software’s ability to detect threats in real time.

On the other hand, anomaly detection is frequently used by Al-based antivirus software to examine program behaviour. Al-based antivirus systems pay more attention to identifying odd program behaviour than they do to matching known malware signatures.

Traditional antivirus software is not very good at detecting and eliminating new threats; however, it does a good job of defending against known threats that have been seen and verified by its public signature. According to Steve Grobman, SVP at McAfee, the majority of conventional antivirus programs can detect threats with 90% accuracy. Al’s contribution to this use-case is that it raises the threat detection rate to 95% or higher, which is an additional advantage.

Blackberry purchased Cylance, which asserts that Al is used in their Smart Antivirus product line to anticipate, identify, and address cybersecurity threats. The company asserts that Cylance’s Al-enhanced Smart Antivirus, in contrast to conventional antivirus software, learns to recognize patterns that point to malicious programs over time instead of requiring virus signature updates.

Al-based User Behavior Modeling

Certain enterprise system Cybersecurity attacks have the ability to compromise individual users within the company by gaining access to their login credentials without their knowledge. Because they can enter an enterprise network using technically sound methods, cyberattackers who have stolen a user’s credentials are difficult to identify and stop. AI-based cybersecurity systems can be used to find behavioural patterns for specific users and then alert users when those patterns change. They can then notify security personnel when that pattern is broken.

All vendors offer cybersecurity software that purports to analyse raw network traffic data using machine learning to determine the baseline of typical behaviour for each user and device within an organisation. The software learns to recognize what constitutes a significant departure from the typical baseline behaviour and promptly notifies the organisation of cyber threats using training datasets and inputs from subject-matter experts.

Al For Fighting Al Threats

Businesses must accelerate their cyber threat detection processes because hackers are now using Al to potentially find points of entry into corporate networks. As a result, in the future, implementing Al software to prevent Al-augmented hacking attempts may be required as part of cybersecurity defence protocols.

These attacks propagate quickly and impact a significant number of computers. These kinds of attacks are probably going to be carried out by people who will eventually use AI technology. AI may provide these hackers with a benefit akin to what Al provides for companies: quick scalability.

The cybersecurity vendor Crowdstrike asserts that AI is used by Falcon Platform, its security software, to thwart ransomware attacks. Anomaly detection is reportedly used by the software for end-point security in business networks.