privileged credential
AI Weekly: With RPA on the rise, security challenges remain
For example, San Jose-based RPA firm Automation Anywhere recently worked with a pharmaceutical company in Europe to accelerate the research and approval of COVID-19 vaccines by augmenting reporting. RPA startup UiPath has also assisted with efforts around the pandemic, for instance helping the U.S. Department of Homeland Security use software bots to perform coronavirus-related data analysis. Deloitte reports that organizations that have implemented and scaled RPA see a return on investment within 12 months. And according to Everest Group, top performers earned nearly four times on their RPA investments while other enterprises earned nearly double. This isn't to suggest that RPA is without its challenges.
How machine learning is helping to stop security breaches with threat analytics
Bottom line: Machine learning is enabling threat analytics to deliver greater precision regarding the risk context of privileged users' behavior, creating notifications of risky activity in real time, while also being able to actively respond to incidents by cutting off sessions, adding additional monitoring, or flagging for forensic follow-up. A commonly-held misconception or fiction is that millions of hackers have gone to the dark side and are orchestrating massive attacks on any and every business that is vulnerable. The facts are far different and reflect a much more brutal truth, which is that businesses make themselves easy to hack into by not protecting their privileged access credentials. Cybercriminals aren't expending the time and effort to hack into systems; they're looking for ingenious ways to steal privileged access credentials and walk in the front door. According to Verizon's 2019 Data Breach Investigations Report, 'Phishing' (as a pre-cursor to credential misuse), 'Stolen Credentials', and'Privilege Abuse' account for the majority of threat actions in breaches (see page 9 of the report).
Machine Learning Is Helping To Stop Security Breaches With Threat Analytics
Bottom Line: Machine learning is enabling threat analytics to deliver greater precision regarding the risk context of privileged users' behavior, creating notifications of risky activity in real time, while also being able to actively respond to incidents by cutting off sessions, adding additional monitoring, or flagging for forensic follow-up. A commonly-held misconception or fiction is that millions of hackers have gone to the dark side and are orchestrating massive attacks on any and every business that is vulnerable. The facts are far different and reflect a much more brutal truth, which is that businesses make themselves easy to hack into by not protecting their privileged access credentials. Cybercriminals aren't expending the time and effort to hack into systems; they're looking for ingenious ways to steal privileged access credentials and walk in the front door. According to Verizon's 2019 Data Breach Investigations Report, 'Phishing' (as a pre-cursor to credential misuse), 'Stolen Credentials', and'Privilege Abuse' account for the majority of threat actions in breaches (see page 9 of the report).