Despite, or perhaps because of, the abundance of information and commentary regarding artificial intelligence (AI), specifically as it applies to cybersecurity, it can be difficult to understand how AI – or more specifically, machine learning (ML) – is actually augmenting endpoint protection for individuals and organisations.
Fundamentally, the difference between traditional and AI-based cybersecurity systems is a matter of “teaching” versus “learning.” Older antivirus software has been programmed to recognise specific security threats – phishing emails or ransomware, for instance – based on heuristics and specific digital signatures. The software has been instructed on exactly what to look for as it scans through files. Of course, this means it’s powerless against new threats that it doesn’t recognise, which is why the system’s maintainers are constantly adding new instructions to reflect the latest cyber threats. You know those notifications you constantly get reminding you to update your antivirus software? That means programmers have updated the system to protect a newly discovered vulnerability (which is why it’s important to keep your antivirus up to date).
Simple enough. At least it was for most of the internet’s history. The problem with the traditional approach is that in recent years cyber threats have become too numerous and too sophisticated for legacy antivirus to keep up. For example, in 2017 experts discovered over 7.4 million new malware specimens, leading some to call it the “Nightmare Year” for cybersecurity. To put that figure in perspective, it represents a 5,600% increase over the past decade.
Thankfully, recent advancements in machine learning – the building of algorithms that use statistical techniques to improve their own code – are enabling a more effective approach for endpoint security. Rather than trying to “teach” software all of the individual threats to look for, engineers can instead feed machine learning algorithms millions and millions of examples of cyber threats, allowing the systems to “learn” for themselves how to distinguish friend from foe. Thus, when hackers produce new variants of viruses and malware to avoid the signature-detection of traditional antivirus, machine-learning-based endpoint security, understanding the patterns and anomalies that characterise malware, is rarely fooled. One recent study found that an AI-powered system identified zero-day threats correctly 98.88% of the time, compared to the 71.16% success rate of traditional AVs.
So, does this mean machine learning is the silver bullet of endpoint protection? Not quite. The technology has limitations, most notably that it takes enormous amounts of data and computing power to make it work properly. But machine learning is so effective that even hackers are using the technology, training their programs to evade even the most sophisticated defenses. Increasingly, cybersecurity is becoming a matter of AI versus AI.
As HBR recently put it, “AI is the future of cybersecurity, for better or for worse.” Stay tuned for more on AI in endpoint protection in the coming weeks.
Learn more about how Ziften is expanding its Zenith endpoint protection platform with artificial intelligence: https://ziften.com/zenith/
