Technology is advancing at breakneck speed, effectively making life easier, more convenient and more efficient for us all. But each new technological development brings with it new waves of cyber threats. There are always those who are looking for new ways to exploit vulnerabilities within the system for their own benefit. Gone are the days of simple malware - developed by amateurs who were just looking to make mischief. Organised crime lies behind much of today's malware, with increasing frequency, severity and impact. The focus is on making money and all businesses of all sizes are potentially vulnerable to targeted attacks.
Attacks are becoming more and more sophisticated. Whilst traditional attacks have mainly involved malware, recently the industry is looking at more script based attacks, using system admin tools like PowerShell, as well as in memory attacks. As attackers are progressively equipped with better tools and techniques at their disposal to exploit system vulnerabilities, cybersecurity companies have had to adapt and come up with new ways and technologies to combat them. One approach that has been increasingly common in recent years is the use of artificial intelligence (AI) and machine learning (ML). Most of the major cybersecurity companies claim to have some form of AI or ML as a feature in their security offerings. But it isn’t just a buzzword. Artificial intelligence and machine learning technologies may be spearheading the next evolution in the cybersecurity industry. In fact, ABI Research forecasts machine learning in cybersecurity will boost big data, intelligence, and analytics spending to $96 billion by 2021.
Cybersecurity companies are investing heavily in machine learning in hopes of providing a more dynamic deterrent to cyber threats. With this methodology, the machine is trained to take on the role of an analyst, able to make decisions and take appropriate actions in real time based on data presented to it. Automated analysis of threat intelligence is being used to identify attack patterns, which is critical for security teams to identify the root cause of cyberattacks. The Cb Collective Defense Cloud, for example, is Carbon Black’s next-generation attack analytics engine that leverages ML to analyse big data related to attacks, threats, behaviours, and changes, with the singular purpose of identifying malicious activity. Cb Collective Defense Cloud is the intelligence and analytics that powers Carbon Black’s Cb Endpoint Security Platform.
In light of the new wave of security threats, enterprises today must utilise a combination of technologies that incorporate prevention, detection and response, as well as use machine learning, behavioural intelligence and threat intelligence to safeguard their data and network and ensure zero-gap protection.