- Global spending on security and risk management technology is expected to reach $150.4 billion in 2021, according to data from Gartner released Monday. Overall, analysts expect security spending to increase 12.4% from 2020 to 2021.
- Cloud security spending is projected to have the most growth, up 41.2% between 2020 and 2021. Cloud security was the "smallest, but fastest growing" market segment, expected to reach $841 million this year. Gartner credits cloud access security brokers as driving segment growth.
- Security services top corporate spending and Gartner expects the segment to reach $72.5 billion this year, up 11.4% from the $65 billion spent in 2020. Spending in the second-largest segment, infrastructure protection, is forecast to reach $24 billion this year, up from $20.5 billion in 2020.
Technology spending is expected to rebound this year, with overall IT spending projected to hit $3.9 trillion globally in 2021, according to Gartner. It's a 6.2% increase from 2020, after global spending dipped 7.3% during the pandemic.
Cloud security spending benefited from the tumultuous year. Cloud security spending beat out Gartner's 2020 estimations of $585 billion, instead reaching $595 billion.
Two market segments are expected to rebound in 2021. Last year, spending on network security equipment fell 12.6% and consumer security software dropped 0.3%. This year, the segments reemerged with 8.9% and 7.4% growth, respectively.
Without a central location in offices, endpoint visibility is at the forefront of security, behind employee behavior and training. The pandemic helped shape what technologies companies are interested in, including emerging technologies like zero trust network access, secure access services edge and software defined perimeter, which predominantly address remote work.
Gartner sees indications of adoption of automation and machine learning increasing to support AI-based security. However, not all security professionals are as welcoming to advanced technologies in security.
Companies considering deploying ML systems have to know what they don't know. Where or how can the ML fail? ML, to a certain extent, is a technology people may not understand how to control at scale, panelists at the RSA Conference questioned.
"Complexity is the enemy of security," said Ronald Rivest, professor at the Massachusetts Institute of Technology, during the RSA Conference Monday. "The more complicated you make a system, the more vulnerable it becomes to all kinds of faults and machine learning is nothing but complicated."
Machine learning and adversarial environments are not always conducive to cybersecurity. It's theoretically easy for ML to pick up on anomalous activity in regards to insider threats. However, preparing an ML model for real-life scenarios is more challenging. Organizations need to know if their ML is learning from unbiased and untainted data.