Fighting AI-powered Cyber Attacks with AI

There is simply no end to the number of cyber attacks and the consequences for the companies attacked are often dramatic. Therefore, companies are constantly looking for ways to improve the resilience of their data. The battle between the good guys and the bad guys is a constant back and forth, and recently cybercriminals have once again taken a step forward. They are using AI to increase the frequency and penetration of their attacks. Worse, there are now more and more novices trying their hand at cybercrime: script kiddies with no programming experience. They use off-the-shelf AI tools to create and deploy malware.

Today, anyone with bad intentions can develop and circulate malware in a very short time and wreak havoc in businesses of all sizes. With readily available AI tools, even inexperienced actors can carry out denial-of-service attacks, create phishing emails or deploy ransomware. These attacks can then be carried out simultaneously from numerous systems around the world, making it nearly impossible for responsible staff to identify all the systems under attack in time.

Fighting back against hackers with AI

But it’s not all bad news: AI and deep-learning technologies also offer a powerful tool in the fight against cybercrime. AI-driven security solutions with self-learning capabilities can proactively respond to emerging threats and in this way protect businesses from the multitude of attacks, giving them back power over their data.

AI-powered security tools, for example, can detect anomalies and patterns that indicate malicious behaviour and stop attacks before they cause damage. This intelligent approach to data protection reduces reliance on reactive measures and enables companies to stay one step ahead of cybercriminals.

AI and deep learning protection systems are also able to adapt and evolve to meet new threats. They learn from previous incidents and thus continuously improve their defence mechanisms. Through techniques such as transfer learning, these systems can constantly enrich their knowledge base with the latest threat intelligence and develop increasingly greater resilience against attacks.

These systems also proactively initiate automatic actions based on predefined rules or learned behaviours. For example, if a system detects a security breach or an anomaly, it can automatically initiate actions such as isolating the affected systems or blocking suspicious traffic. This automatic response shortens the time between detection and remediation of a cyber attack, minimising the potential impact.

Risk Remote Administration Tool

Here’s an example of how AI works in practice: There is a well-known threat in the cybersecurity industry called a Remote Administration Tool (RAT). A RAT can be embedded in a simple email attachment, such as a JPEG image, allowing cyber attackers to gain unauthorised access to a system. Anti-virus programs usually detect RATs based on their signatures and alert all endpoints to identify and remove them. However, attackers can easily modify their RATs to create a different signature and bypass traditional detection.

To defend against this, AI and deep learning technologies are crucial. Instead of relying only on static signature matching, AI-powered cybersecurity tools can analyse the behaviour of files and processes. They observe whether a file performs certain actions or installs software. AI security tools detect suspicious behaviour and prevent potentially malicious actions by learning and identifying patterns in these activities. With this approach, threats can be better detected and, more importantly, prevented.

Attackers are constantly developing new methods to circumvent traditional cybersecurity measures, making it imperative that businesses keep up. AI and Deep Learning can play an important role in analysing current threats as well as predicting potentially malicious actions based on observed patterns. Such a proactive approach improves companies’ security posture and helps them protect themselves from constantly evolving cyber threats.

AI is not 100 per cent secure

When implementing AI and deep learning tools, it is important to consider the challenges involved. For example, mistakes can always occur because AI is still in development and is not 100 per cent secure. This means that sometimes there can be misinterpretations that affect the availability of data or systems. Such disruptions occur mainly when AI detects what it thinks is an illegal activity. For example, AI tools often work with a reliability assessment. A company can therefore specify that preventive measures be taken if the score falls below a certain threshold. But beware: such a preventive measure may not only be unnecessary, but also lead to unplanned downtime.

Since AI technology is constantly evolving, it cannot guarantee absolute perfection. Therefore, the risk of errors will always exist now and in the future. However, as more people use the technology and are confronted with different threats, AI systems will become more reliable in distinguishing real threats from perceived threatening situations.

First steps with AI

Many companies are intrigued by the potential of AI, but don’t know how or where to start using the technology. The easiest way is to work with trusted security solution providers who are familiar with Deep Learning and AI and have already integrated the technology into their existing products. This approach allows end users to apply AI and use it effectively for data security and cybersecurity.

As this technology continues to evolve, we can expect to see more in-house AI and deep learning solutions being constructed and deployed. However, it will take a few more years for AI to become mainstream. Until then, the easiest way for companies to do so is to partner with solution providers that have out-of-the-box AI-powered tools to neutralise cyberattacks and protect against data breaches.

René Claus

is EMEA MSP Sales Director at Arcserve.