Keep your friends close, but your threats closer.

Cybersecurity breaches happen more often than we want to believe. 

In 2021 alone, there were 623.3 million ransomware attacks. At least 30,000 websites are hacked every day, and 64% of companies in the world have encountered at least one form of cyber attack.

The increasing number of remote workers has exacerbated cybersecurity challenges as they might be easy targets for cybercriminals. Phishing emails, shadow IT, bring your own device (BYOD) programs, and insecure home wi-fi networks can put organizations at tremendous risk.

Cybersecurity requires more strengthening

The shortage of skilled cybersecurity professionals adds to this distress as well. Albeit, the rise of software as a service (SaaS) has made things easier for small businesses without the resources to hire a full-time cybersecurity employee. For instance, security orchestration, automation, and response (SOAR) software lets companies defend against web-based attacks without requiring dedicated onsite security professionals. And the SaaS model makes it reasonably affordable for small-sized businesses as it requires lower investment and is easily scalable.

But even if an organization manages to hire a dozen cybersecurity professionals, there’s still a lot beyond their reach. Cybersecurity involves a lot of tedious, repetitive tasks, and going through thousands of log entries every day is inhumane for the workforce. Such challenges have made it necessary to utilize techniques such as machine learning that automates several cybersecurity tasks.

Time for newer technologies—enter machine learning

Today’s cybersecurity technology and tools heavily rely on machine learning—a subset of artificial intelligence (AI)—to eliminate or reduce monotonous and time-consuming tasks. 

What does machine learning bring to the table exactly?

With the power of machine learning, cybersecurity software solutions can analyze patterns in huge amounts of (log) data and find correlations, helping businesses detect threats and prevent attacks. The following are some ways machine learning is used to strengthen cybersecurity:

Anomaly detection

Although humans are pretty adept at spotting patterns or detecting anomalous behavior (thanks to millions of years of evolution), the scale of data generated by most modern applications, networks, and websites is beyond our level of comprehension. Here’s where machine learning shines the most.

Machine learning models can scan millions of files, identify anomalies and potential threats, and automatically eliminate them before they turn into catastrophes. Along with detecting threats, machine learning can help scan networks for vulnerabilities and automate responses. Many of the bot detection and mitigation software products utilize machine learning to spot abnormalities.

Some cyberattacks like fileless malware—malware that doesn’t require any file downloads, making it harder to detect and remediate—may find ways to stay undetected or dynamically change their behavior to avoid detection. In such instances, a type of machine learning called unsupervised learning, that identifies patterns in datasets containing unlabeled data points, can help uncover hidden patterns and ultimately detect such sophisticated attacks.

Real-time response

Machine learning can help cybersecurity systems automatically detect and respond to cyberattacks with little to no human intervention. This makes threat response almost real time, eliminating or reducing the impact a security attack can have on a business.

Cybersecurity tools like SOAR software utilize machine learning to build and automate response workflows and reduce the amount of human intervention required to handle security incidents. The traffic to G2's Security Orchestration, Automation, and Response (SOAR) Software category is steadily increasing since 2019.

The rise in demand for these software products can be attributed to their time-saving characteristics, including automation and how they help cut down costs by not requiring full-time cybersecurity professionals. At the same time, since SOAR software lets businesses automate response tasks and incident management, their remediation measures will be effective around the clock.

Behavioral analysis

Humans are usually creatures of habit. Machine learning can help understand the trends and behavior of customers and employees and create a baseline. Deviation from this baseline could mean a malicious attack.

This application of machine learning is called behavioral analysis and helps companies identify malicious behavior or users by analyzing how the usage is different from normal, everyday activities. User and entity behavior analytics (UEBA) software products perform this analysis and alert security staff. Behavior analysis is also one of the many parameters used by fraud detection software to detect incidents of fraud.

Cloud data protection

Machine learning can help organizations detect suspicious cloud application login attempts, conduct IP reputation analysis, and detect location-based anomalies. Machine learning is a common ingredient among cloud access security broker (CASB) software products that secure connections and act as a layer of protection between end users and cloud-based software.

Network risk scoring

Machine learning can be utilized to analyze datasets of previous cyberattacks and identify the areas of networks that were primarily involved in specific attacks. This can further help determine the impact and likelihood of an attack on a specific network area. This will empower organizations to channel their resources into areas that need more reinforcement.

In a nutshell, machine learning makes cybersecurity less expensive, more proactive, and less daunting. This is especially important because freeing up cybersecurity professionals from monotonous tasks can help focus their efforts on more impactful tasks. These tasks include improving the organization's security posture, learning more about the newly found threats and vulnerabilities, and educating other non-technical employees about how to reduce cybersecurity risks.

Is machine learning the kryptonite?

Machine learning can do wonders in terms of reducing cybersecurity risks. But it’s not the kryptonite (or the doomsday device or the silver bullet) for all cybersecurity threats. In fact, there isn’t a specific tool, technology, or system that can entirely annihilate every threat out there. That’s because the hackers, malicious actors, and exploiters behind these attacks are trying rigorously to find ways to exploit vulnerabilities in security systems. 

Along with having a robust cybersecurity system and team in place, companies can utilize vulnerability management software solutions like attack surface management software to identify and remediate vulnerabilities. Staying up-to-date with cybersecurity risks and trends and using threat intelligence software can help reinforce the cybersecurity ecosystem as well.

Investing in physical security software is also crucial as getting physical access to devices may allow malicious actors to bypass certain cybersecurity measures.

Human errors cause 95% of cybersecurity breaches. In other words, the majority of cybersecurity breaches can be prevented if employees are properly trained. Using security awareness training software is a great way to do this. This is mainly because a threat can’t be avoided if it isn’t recognized in the first place.

Nevertheless, cybersecurity training should be an ongoing process. As most companies have new employees joining every month, security awareness training should be a part of their onboarding process. Such training should also be conducted regularly as breaches can be avoided only if employees remember the best practices and are consciously on the lookout for abnormal behavior or events.

Businesses should also invest in insider threat management (ITM) software as insiders (employees) are responsible for 22% of security incidents. Investing in employee identity theft protection software can help combat cyber threats as well as these tools alert employees whose credentials have been compromised. To state the obvious, the sooner it’s known that credentials are compromised, the easier it is to remediate.

It’s also interesting to note that companies can reduce cybersecurity risks by investing more time and resources into improving employee satisfaction and happiness. That’s because stressed and exhausted employees are more likely to make cybersecurity mistakes that can lead to breaches and other unfortunate events.

Machine learning can do a lot more

While many cybersecurity solutions still follow the rule-based approach, the majority of software products have begun to utilize machine learning to spot anomalies quickly and efficiently.

Machine learning has a lot more to offer to strengthen the cybersecurity of businesses. For example, adaptive bot detection filters can adapt to the latest trickeries of bot attackers, making detection and mitigation easier. The introduction of newer methods of machine learning such as reinforcement learning—a training method that rewards AI agents for desired behavior and punishes undesired ones—can make the cybersecurity tools more adept when it comes to detecting anomalous events and activities.