Companies still struggle with ransomware, phishing, data breaches and other attacks that bypass their security and affect their budgets. Enterprises know they are in dire need of technology that will safeguard their infrastructures from known, unknown, and undisclosed vulnerabilities.
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In 2015, Russian hackers shut down Ukraine’s electrical grid after infecting the infrastructure with malware. It was only a matter of time until they would target the US power system. As of 2016, US critical operational infrastructures have been under siege by "Russian government cyber actors," as described by the Department of Homeland Security and the FBI.
It doesn’t matter what discipline within cybersecurity one looks at, nearly everywhere one looks machine learning and artificial intelligence are changing how security data are analyzed, security tools deployed, and threats identified. I know there’s a difference between machine language and AI, but so many use the terms interchangeably now that the difference is blurring in the minds of many.
Many cybersecurity organizations are of the opinion that threat intelligence can prevent, or if not prevent entirely at least lessen, the impact of successful breaches.
There’s been a continuous increase in the use of Machine Learning but, despite the recent hype, the technology is not new. While researchers have been playing with artificial neural networks from as early as the 1950s, machine learning is not new even in the context of cybersecurity.
Demand for cybersecurity experts will increase and become a priority for enterprises, leading to an estimated need for over 1 million cybersecurity professionals in India by 2020 and over 500,000 in the US. The US currently employs 780,000 specialists in cybersecurity, while 350,000 posts are still unfilled, reports CyberSeek, project supported by the U.S. Department of Commerce.