Blog: Five worthy reads: AI and ML: Keys to the next layer of endpoint protection
When businesses first started using mobile devices, there was a dynamic shift in workplace culture that gave employees more flexibility and provided new methods for completing work. While this shift made most business operations more efficient, it also presented the challenge of managing and securing endpoints and their data to comply with company policies and regulatory standards.
Endpoint security has been a major concern for IT teams across the globe. Often, organizations take a reactive approach, which leaves them a few steps behind cybercriminals. This is where machine learning (ML) and artificial intelligence (AI) come into play. Using ML and AI, organizations can not only stay ahead of attacks, but successfully fend them off as well.
What sets ML and AI-enabled solutions apart is their ability to identify patterns and learn for themselves to accurately predict attacks and provide recommendations, rather than rely on a fixed set of pre-programmed policies. This helps with eliminating false alarms which, in turn, allows threat intelligence and endpoint detection and response (EDR) teams to perform optimally.
That said, here are five worthy reads on infusing ML and AI in your endpoint management strategy to help your organization find the most strategic way forward.
Apart from the fact that AI provides a huge advantage by identifying completely new threats and file-less attack vectors, it has also become affordable and easily accessible. AI today only consumes a small amount of CPU space, which makes it a great time to get started with an AI-integrated UEM solution.
Believe it or not, hackers are way ahead of most organizations. They’ve already implemented AI and ML capabilities in their attack vectors and can recognize and combat any hurdles that come their way. Experts predict a huge increase in the number of AI-enabled phishing attacks in the coming year, and most organizations aren’t ready. Is yours?
Businesses today need to shift their focus from malware to data protection. AI can handle the detection of malware and potential attacks, which frees up experts to work on guarding data. Another problem organizations across the globe face is a shortage of qualified security experts; while AI-enabled solutions can’t replace security experts, they can assist teams with threat identification and EDR to help lighten the workload for understaffed workforces.
ML is still relatively new which means there are few ML experts to consult, and the experts that are available aren’t usually well-versed in cybersecurity. Meanwhile, security experts lack experience working with algorithms that analyze real-time data. This gap definitely needs to be bridged to achieve a foolproof layer of security. For this reason, organizations may hold back on adopting AI into their UEM strategy.
Scanning through vast amounts of risk indicators is a tedious process for security teams and could lead to teams overlooking potential attacks amidst the false alarms. Using AI, teams can gather relevant segregated information easily and concentrate on making quicker decisions. AI cuts down on human error while skimming though tons of threats, and anticipates future risk patterns while enhancing security.
Not only can AI help reduce false positives and human error, but it can help a single security expert effectively manage the endpoints of a thousand employees with precision. Consequently, AI can compare a huge number of potential outcomes in the time it takes a human to open a single record. AI can’t replace humans, but it can serve as an incredibly helpful tool that has the potential to reshape endpoint security.