Data security is a growing concern in the digital age, and with the rapid expansion of stringent data privacy regulations across the globe, such as the GDPR, it is becoming increasingly important for businesses to protect their data from malicious actors.
However, one problem we can’t overlook is the serious shortage of IT security professionals, with many high-paying positions going unfilled. And it doesn’t seem like this problem will go away anytime soon. In fact, according to research by the Information Systems Security Association (ISSA), 44% cybersecurity professionals believe the skills gap has gotten worse, and there’s also the risk of a vicious cycle unfolding.
Many cybersecurity experts have been leaving the profession due to the large amounts of pressure they are placed under. This in turn could increase the pressure on existing experts, who then decide to follow suit. Sure, companies can always train existing employees to fill cybersecurity roles, and to be fair, they probably should; however, this will obviously cost them a lot of money, and so it’s probably not a robust long-term strategy.
Another option would be to focus on automation or to be more precise, leverage intelligent systems to perform tasks that would otherwise be performed by humans.
What Is Machine Learning?
Machine learning (ML) is a branch of artificial intelligence (AI) that uses algorithms and statistical models to enable computers to “learn” from existing data in order to make predictions or decisions. Machine learning technology is used in many areas, such as fraud detection, image recognition, natural language processing, and autonomous vehicles.
In the context of data security, ML can be used to detect suspicious activity in a variety of ways. For example, ML algorithms can analyze user behavior to detect patterns that may indicate malicious activity.
This can include analyzing user logins, file activity, configuration changes, and network traffic to detect any anomalies that could indicate an attack. In addition, machine learning can be used to detect malware and other malicious software, as well as detect attempts to breach a system’s parameters.
What are the Main Benefits of Using Machine Learning for Data Security?
As mentioned above, for companies, ML can help to cut costs and circumnavigate the ongoing IT security skills shortage. ML can be used to perform a wide range of tasks including applying security updates, penetration testing, bug fixing, vulnerability scanning, and other mundane/repetitive tasks, thus allowing security professionals to focus on more pressing issues.
However, where ML is most useful for data security is monitoring systems for suspicious activity. By analyzing past data, ML can figure out which activity patterns pose the biggest threats, and prioritize accordingly. Such techniques can help to identify a wide range of attack vectors; however, they are particularly effective when it comes to spotting anomalous user behavior.
Many advanced Data Security Platforms use machine learning models to learn typical usage patterns for each user, as well as the system as a whole. If an event or trend occurs that deviates too far from the baseline, it can send a real-time alert to the administrator’s inbox or mobile app, or initiate a response to stop the attack in its tracks.
These solutions can also detect and respond to events that match a pre-defined threshold condition, such as multiple failed logon attempts, or when multiple files have been encrypted within a given time frame
It’s worth bearing in mind, however, that the use of machine learning techniques cannot make up for the human error. After all, if an employee falls victim to a targeted social engineering attack, and in turn discloses a set of legitimate credentials to the attacker, there’s little that ML can do about it. As such, regular security awareness training must still be carried out.
If you’d like to see how the Lepide Data Security Platform can help to keep your data secure, schedule a demo with one of our engineers.