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Artificial Intelligence and Machine Learning – The digital sentinels

Artificial Intelligence and Machine Learning – The digital sentinels

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Tamer Odeh, Regional Director at SentinelOne in the Middle East, explains why AI and Machine Learning are invaluable tools to ensuring the workforce environment is secure, as the workforce relies heavily on automation as the skills gap widens. He discusses how, by using AI, organisations can take a proactive approach to secure the data that is so vital to their very existence.

Artificial Intelligence (AI) and Machine Learning are some of the most discussed technologies in the cyberworld. With applications in a wide-range of industries, including construction, finance, retail, healthcare and cybersecurity, AI and its subset technology, Machine Learning, are transforming operations, leading to smarter ways of working. Recognising the efficiency and automation benefits of AI, governments and businesses in the region are increasingly adopting these technologies. For instance, UAE’s Artificial Intelligence Strategy 2031 and Saudi Arabia’s Vision 2030 emphasize AI and plan to integrate AI as part of their long-term economic visions. Additionally, AI is increasingly gaining ground in the region’s enterprises. According to research by BCG and MIT, 58% of surveyed Middle East businesses are implementing or planning to initiate AI deployment plans.

AI and Machine Learning are essential to enterprise cybersecurity too. Businesses of every variety and every industry are facing a constant influx of bad actors trying to storm their digital fences and jeopardise their network integrity. Rarely a day goes by that the headlines aren’t filled with at least one story of another global conglomerate suffering under the weight of an all-out attack on their data and networks. Suffice it to say, if the largest and most technologically advanced organisations in the world still fall victim to these threats, every business with any digital footprint is susceptible.

That isn’t to say that your business, no matter its size or industry, is completely vulnerable to these cyberthreats. In fact, with some deliberate planning and a well-organised game plan, companies can efficiently and effectively defend themselves and their data from intrusion. As is often the case in such matters, technology can play a potent and powerful role in maintaining network integrity and enterprise data.

By its very nature, AI and Machine Learning are an inherently good fit to constantly and relentlessly patrol the perimeter of your digital fortress. The complex algorithms and computing power that serve as the foundation for these innovations naturally lend themselves to the responsibility of monitoring a company’s networks.

AI is uniquely able to refine and expand its capabilities with further use and a constant influx of new data. Therefore, it is well-equipped to keep lockstep with an ever-evolving landscape of ransomware, trojans, theoretical anarchists and a host of random bad guys simply trying to wreak havoc wherever and whenever possible. With its ability to constantly learn and hone its performance, an AI-based platform specifically built to monitor your networks for possible intrusion and any weaknesses can be an invaluable addition to your security protocol.

AI, Machine Learning and humans

The vast majority of companies today have more potentially vulnerable endpoints than they can realistically monitor or manage, owing to various reasons ranging from lack of qualified staff to expensive security programmes or policies.

While estimates vary, it’s well established that organisations are struggling to find and retain the talent needed to defend themselves. The skills gap is getting wider as organisations adopt more complex technologies and expand the threat surface. Security will always be an arms race between attackers and defenders. That said, AI is a potent tool because it allows us to create software that does a malware analyst’s job.

It takes many human years of experience and training to develop the skills and intuition to sniff out malware. Now we can train a program to do the same thing in just a few hours with AI learning algorithms and a huge amount of data. To be fair, there’s nothing better than a human analyst, but the gap is closing all the time and AI models only take seconds to analyse a file where a human analyst could take hours or days.

Thus, cybersecurity provides an excellent forum for applying AI. Well-trained and constantly learning models are far more predictive and effective than humans or legacy methods, such as individual signatures or heuristic rules that require updates multiple times per day.

Securing the endpoint with AI and ML

As you might expect, the cybersecurity industry benefits greatly from AI as we are using it for everything – from detecting threats to unusual network activity.

As quickly as technology evolves in this dynamic marketplace, organisations can adopt innovations before their network security can maintain data integrity. With companies rapidly integrating such connected technologies as IoT and countless cloud-based platforms designed to infuse significant efficiencies across their operations, network security can often lag behind those advanced systems. The result, of course, is networks filled with security gaps just waiting to be exploited.

Additionally, when it comes to next-generation cybersecurity, traditional on-premises signature database protection models are ineffective and lack administrator visibility.

Most traditional and next-gen approaches rely on scanning files to detect attacks, making them extremely vulnerable to new attack techniques.

However, with an AI-based system tasked with the sole responsibility of constantly monitoring a company’s network for weak spots and possible entry points, organisations can take a proactive approach to secure the data that is so vital to their very existence.

Thus, AI and Machine Learning are essential for endpoint security. If you think about it, a machine works at machine speed, processing data is fast and the decision-making aspect of it is also fast.

To adequately defend the business while adopting innovative technologies such as cloud containers, IoT, and more, organizations need dynamic Artificial Intelligence-driven (AI) next-generation endpoint protection platforms that defend every endpoint against all types of attacks and at every stage in the threat lifecycle.

Overall, machine learning and other additions are also making AI even more intelligent. But with more capabilities comes more code, and with more code comes more bugs. Coupled with the fact that AI is a relatively new technology, which makes it inherently less secure, it’s easy to see why cybercriminals are taking advantage of this problematic new tool. The question is how they will continue to do so, and on what scale.

AI shows no signs of slowing down; it’s effective and addictive, which is why we have adopted it with open arms. Clearly, there’s no going back now. As defenders, our next step has to be building the tools, security models and processes to combat the wave of deep fakes and beyond, securing a bright future with AI by our side, not against us.

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