Gaps in software systems are slowing down security teams who are estimated to spend 10 hours a week dealing with the inefficiencies.
More a third of IT decision-makers estimated that their security staff spent at least three hours daily on tasks that otherwise could have been handled by better software, revealed a study commissioned by LogRhythm. Conducted by Widmeyer, the study polled 751 respondents from Asia-Pacific, the US, and UK, including 251 from four Asia-Pacific markets: Singapore, Hong Kong, Australia, and Malaysia.
The majority believed a security administrator, on average, spent up to 10 hours a week dealing with the lack of software capabilities.
And yet, in Asia-Pacific, 56 percent of IT decision-makers said they depended on software to help them prioritise cybersecurity threats.
This reliance would be increasingly important since 88 percent across the global sample regarded insider threats as a growing concern in their ability to safeguard the organisation.
“The proliferation and innovation of business-enabling technology, combined with the speed of today’s advanced hackers to adopt and adapt to the latest technology, is making it increasingly difficult–if not impossible–for security teams to evolve their rapid threat detection and response capabilities as quickly as their adversaries,” said James Carder, LogRhythm Labs’ chief information security officer and vice president.
The security vendor touts the merits of artificial intelligence (AI) in dealing with this evolving landscape. It noted, however, that less than half of the survey respondents currently used AI to fight cyberthreats.
According to Gartner, AI would help businesses regain 6.2 billion hours in employee productivity by 2021, generating US$2.9 trillion in business value.
The research firm’s research vice president Mike Rollings said: “AI can take on repetitive and mundane tasks, freeing up humans for other activities, but the symbiosis of humans with AI will be more nuanced and will require reinvestment and reinvention instead of simply automating existing practices.
“Rather than have a machine replicating the steps that a human performs to reach a particular judgment, the entire decision process can be refactored to use the relative strengths and weaknesses of both machine and human to maximise value generation and redistribute decision making to increase agility,” Rollings said.