Why does everyone keep mislabeling machine learning - a proven technique for helping organizations to improve their security posture - as artificial intelligence? "I'm so tired of the AI buzzword bingo," says John Matthews, CIO of ExtraHop Networks.
Defending organizations against attackers is more challenging than ever. "The complexity and sophistication of the threats has increased," says Cisco's Mark Weir. "What we're seeing a lot of at the moment as well is intellectual property theft."
Visibility, or a lack thereof, continues to challenge organizations as they attempt to protect their businesses by knowing which systems, applications and data they have, says AlgoSec's Jeffrey Starr. He discusses how centralized visibility, control and automation can help.
As organizations pursue digital transformation initiatives backed by new application deployment techniques, they must ensure that security, operations and development teams fully coordinate, says Marco Rottigni of Qualys.
After years of organizations being stuck in a reactive security posture, proactive prevention is finally possible thanks to machine learning backed by AI math models, says BlackBerry Cylance's John McClurg.
With the volume of data breaches and cyberattacks continuing to rise, organizations are increasingly relying on breach and attack simulation tools to provide more consistent and automated validation of controls, says Cymulate's Tim Ager.
Some federal agencies inappropriately continue to rely on knowledge-based authentication to prevent fraud and abuse even though this method is no longer trustworthy because so much personal information that's been breached is readily available to fraudsters, a new
U.S. Government Accountability Office report notes.
A urology practice in Ohio and an eye care provider in Indiana are among the latest victims of ransomware attacks in the healthcare sector. Some security experts suspect that such attacks are still underreported to regulators.