Research Examines Use of Credential Theft Foresight in Detecting and Preventing Predictable Cyber Security Risk

CyberArk released new research from CyberArk Labs introducing credential theft foresight as an effective approach to identifying network weak spots likely to expose privileged credentials to compromise

The report, “Predicting Risk: Credential Theft Foresight,” examines how privilege escalation can be detected and neutralized, and how future risk can be prevented. This approach enables organizations to minimize the attack surface and improve their overall security posture.

The research identifies “HotSpots” and “ColdSpots” as indicators of weak areas on a network that are likely to be attacked. Organizations have an average 5.5 HotSpots, which are areas predictably vulnerable to attack that act as bottlenecks for dozens of potential attack vectors, on their networks at any given time. They also have an average 37 ColdSpots, which are machines hosting privileged accounts that could be targeted by attackers in an attempt to escalate privileges.

The research details:

Credential theft foresight as a significant defensive advantage over traditional security tools like vulnerability scanners and intrusion detection systems
The multi-step process for identifying and mitigating HotSpots and ColdSpots
Two use cases in applying credential theft risk mitigation

To easily identify HotSpots in real time as they are created, CyberArk Labs also released a new tool – PreCog – that’s available now on GitHub: https://github.com/cyberark/PreCog.

CyberArk Labs researchers will be available at RSA Conference to discuss the research. They’ll also unveil new research in the RSA Conference session, “Sneak Your Way to Cloud Persistence – Shadow Admins Are Here to Stay,” on Thursday, April 19, at 9:15 a.m. PDT. To learn more, members of the CyberArk Labs team will be available at booth #4201. 

Additional Resources

Blog: Predicting Risk: Credential Theft Foresight
Research paper: Predicting Risk: Credential Theft Foresight
Research paper: CyberArk Labs: Kerberos Decryption
Research paper: CyberArk Labs: Pass-the-Hash Detection Using Windows Events