Kaplan Breach Incident Score: Analysis & Impact (KAP1774391709)

In this Rankiteo incident briefing, we review the Kaplan breach identified under incident ID KAP1774391709.

The analysis begins with a detailed overview of Kaplan's information like the linkedin page: https://www.linkedin.com/company/kaplan, the number of followers: 436701, the industry type: Education Administration Programs and the number of employees: 11288 employees

After the initial compromise, the video explains how Rankiteo's incident engine converts technical details into a normalized incident score. The incident score before the incident was 793 and after the incident was 714 with a difference of -79 which is could be a good indicator of the severity and impact of the incident.

In the next step of the video, we will analyze in more details the incident and the impact it had on Kaplan and their customers.

Kaplan North America LLC recently reported "Kaplan North America Data Breach", a noteworthy cybersecurity incident.

Kaplan North America LLC is facing a proposed class-action lawsuit after a 2023 data breach compromised the personal information of thousands of users and employees.

The disruption is felt across the environment, and exposing Personal information, including names, Social Security numbers, and driver’s license numbers, with nearly Over 19,000 (Maine residents) records at risk.

Formal response steps have not been shared publicly yet.

Overall, the incident is a reminder of why proactive monitoring and strong governance matter.

Finally, we try to match the incident with the MITRE ATT&CK framework to see if there is any correlation between the incident and the MITRE ATT&CK framework.

The MITRE ATT&CK framework is a knowledge base of techniques and sub-techniques that are used to describe the tactics and procedures of cyber adversaries. It is a powerful tool for understanding the threat landscape and for developing effective defense strategies.

Rankiteo's analysis has identified several MITRE ATT&CK tactics and techniques associated with this incident, each with varying levels of confidence based on available evidence. Under the Initial Access tactic, the analysis identified Exploit Public-Facing Application (T1190) with moderate confidence (50%), supported by evidence indicating breach compromised the personal information via unspecified attack vector and Valid Accounts (T1078) with moderate confidence (60%), supported by evidence indicating thousands of users and employees data exposed (implies account misuse). Under the Credential Access tactic, the analysis identified Modify Authentication Process (T1556) with lower confidence (40%), supported by evidence indicating failed to adequately protect sensitive data (implies weak auth controls) and Brute Force (T1110) with lower confidence (30%), supported by evidence indicating no specific details, but high-value data suggests possible credential attacks. Under the Collection tactic, the analysis identified Data from Local System (T1005) with moderate to high confidence (80%), supported by evidence indicating names, Social Security numbers, and driver’s license numbers compromised and Data from Information Repositories (T1213) with moderate to high confidence (70%), supported by evidence indicating education companys internal systems likely targeted for PII. Under the Exfiltration tactic, the analysis identified Exfiltration Over C2 Channel (T1041) with moderate to high confidence (70%), supported by evidence indicating over 19,000 Maine residents data exposed (implies data exfiltration) and Transfer Data to Cloud Account (T1537) with lower confidence (40%), supported by evidence indicating no specifics, but modern breaches often use cloud storage for exfil. Under the Impact tactic, the analysis identified Data Destruction (T1485) with lower confidence (20%), supported by evidence indicating no evidence of destruction, but included due to breach severity and Stored Data Manipulation (T1565.001) with lower confidence (30%), supported by evidence indicating high-risk PII exposure suggests potential for identity theft. These correlations help security teams understand the attack chain and develop appropriate defensive measures based on the observed tactics and techniques.