The threat landscape is evolving quickly
Cybersecurity vendors have long relied on file signatures to detect malware thanks to its simplicity and widespread use. Professionals across the world can accurately describe live malware threats and share their findings, a practice that has been incredibly useful in the fight against malware.
But the threat landscape is evolving quickly. Hundreds of thousands of new pieces of malware are detected every day. Does signature-based detection still provide the necessary level of protection for modern firms, especially those in regulated industries like law, finance, and healthcare, who are often targeted specifically by threat actors?
Machine learning
At Appurity we always emphasise to our customers the importance of a proactive approach to cybersecurity. Bad actors are becoming more sophisticated, and new attack methods are constantly being introduced. With this in mind, those businesses that still take a prevention-second approach to their cybersecurity – they act once an attack is already in play – will find themselves on the back foot. How can your businesses keep up with all the new malware threats detected every day if your approach is to tackle an attack only once it’s already happened?
Some innovative vendors got ahead of the game, realising that signature-based methods of fighting malware didn’t do enough to fully protect all a business’s endpoints. There’s been a positive shift towards cybersecurity measures that leverage the power of machine learning and AI models to examine what a file does or will do upon execution.
These vendors are flipping traditional malware detection methods on their heads. The BlackBerry Cybersecurity portfolio of products, deeply rooted in Cylance AI, leverages the power of machine learning to see and understand the majority of a threat (about 80% of it). By being able to identify the majority of a threat’s footprint, the technology moves quickly to flag it, shut it down or quarantine it. This is all because the AI recognises a part of the threat, and isn’t just relying on signatures. This eliminated the need to add in a human element, slowing down the process, to manually approve the shutdown of the threat.
A prevention-first approach
If your business wants to protect its endpoints from attackers, the best way to do this is to identify and stop an attack before it’s had an opportunity to take hold. That’s the principle behind BlackBerry Cylance Endpoint Security’s identification capability.
The prevention model analyses and sorts millions of characteristics within a file to determine whether it is safe or malicious. This approach prevents malware from executing on endpoints. It’s a proactive, forward-thinking approach which leverages machine learning techniques to stop malware, ransomware, bots, and even zero-day attacks in their tracks. All of this happens at speed and scale.
Proactive, preventative cybersecurity strategies are beginning to take hold. It’s simply not enough anymore for businesses to take a post-execution approach to their malware prevention. Why would your business want to risk exposing its critical data to bad actors?
Efficiency gains
We recognise that to truly be effective, cybersecurity tools or technologies must improve a business’s overall efficiency or user productivity. Utilising AI to prevent malware attacks provides a distinct operational benefit. By allowing machine learning to do the work in finding and shutting down malicious files and threats, your business can effectively prioritise its resources. You can focus on fixing the most critical vulnerabilities. You can also use the time and resource gains to focus on more business-critical IT projects. Eliminating the human element inherent in relying on file signatures gives back time and resources to your people.
Cyber Essentials
We already work with several technology partners to help customers navigate the Cyber Essentials Plus framework, delivering security and mobility solutions that satisfy various elements of Cyber Essentials. BlackBerry Cybersecurity’s products fit within the Cyber Essentials framework. A core objective for malware protection within Cyber Essentials is to “restrict execution of known malware and untrusted software, to prevent harmful code from causing damage or accessing sensitive data.” Advanced malware protection must be installed on all devices in scope to prevent malware from running and prevent the execution of malicious code. This focus on prevention is crucial, and is why AI tools are increasingly recommended for fighting malware and preventing attacks.