Where CISOs Rely on AI and Machine Learning to Strengthen Cybersecurity

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Faced with an avalanche of non-malware attacks that are increasingly difficult to identify and stop, CISOs face a threat landscape where malicious actors innovate faster than security and IT teams do. can follow. However, artificial intelligence (AI) and machine learning (ML) are proving effective in enhancing cybersecurity by increasing the volume of data analysis while increasing response speeds and securing digital transformation projects by construction course.

"AI is incredibly, incredibly good at processing large amounts of data and classifying that data to determine what's good and what's bad. At Microsoft, we process 24 trillion signals every day, and this, across identities, endpoints, devices and collaboration tools, and more. And without AI, we simply couldn't solve this problem," said Vasu Jakkal, Microsoft vice president for security, compliance, identity and privacy to his audience at the RSA conference earlier this year.

2022 is a pivotal year for AI and ML in cybersecurity. Both technologies enable cybersecurity and IT teams to improve knowledge, productivity, and the economies of scale they can achieve with smaller teams. 93% of IT leaders are already using or planning to implement AI and ML to strengthen their cybersecurity technology stacks. Of these, 64% of IT leaders have implemented AI for security in at least one of their security lifecycle processes, and 29% are evaluating vendors.

CISOs tell VentureBeat that one of the biggest drivers of adoption is the need to do more revenue-related projects with fewer staff. Additionally, AI and ML-based applications and platforms are helping address cybersecurity skills shortages that put organizations at higher risk of breaches. According to the (ISC)² Cybersecurity Workforce Study, "An additional 3.4 million cybersecurity workers are needed to effectively secure assets".

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CISOs also need the real-time insights provided by AI and ML-based systems to refine predictive models, gain a holistic view of their networks, and continue to execute their framework and strategy. zero trust security. As a result, enterprise spending on AI and ML-based cybersecurity solutions is expected to grow at a compound annual growth rate (CAGR) of 24% by 2027 and reach a market value of $46 billion. Top Use Cases for AI in Cybersecurity

It's common to find companies that don't track up to 40% of their endpoints, which complicates the task because many IT teams don't know how many endpoints their internal processes create during of a given year. More than a third, or 35%, of enterprises using AI today to power their technology stacks say that endpoint discovery and asset management are their top use case. Enterprises plan to increase their use of endpoint discovery and asset management by 15% in three years, which will eventually be installed in nearly half of all enterprises.

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