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Analyze » Equifax » EXPEQUTRA1772490489

Incident Score: Analysis & Impact (EXPEQUTRA1772490489)

The details regarding individual company incidents & reports gives you full view from every side.

Rankiteo Score Impact Analysis

Rankiteo Incident Impact-144
Company Score Before Incident637 / 1000
Company Score After Incident493 / 1000
INCIDENT NUMBEREXPEQUTRA1772490489
Type of Cyber IncidentBreach
ATTACK VECTORNA
DATA EXPOSEDPersonal information, Social Security numbers
INCIDENT DATE31/12/2024
STATUSpublished

Key Highlights From The Incident Analysis

  • Timeline of Equifax's Breach and lateral movement inside company's environment.
  • Overview of affected data sets, including SSNs and PHI, and why they materially increase incident severity.
  • How Rankiteo’s incident engine converts technical details into a normalized incident score.
  • How this cyber incident impacts Equifax Rankiteo cyber scoring and cyber rating.
  • Rankiteo’s MITRE ATT&CK correlation analysis for this incident, with associated confidence level.

Full Incident Analysis Transcript

In this Rankiteo incident briefing, we review the Equifax breach identified under incident ID EXPEQUTRA1772490489.

The analysis begins with a detailed overview of Equifax's information like the linkedin page: https://www.linkedin.com/company/equifax, the number of followers: 286823, the industry type: Financial Services and the number of employees: 18053 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 637 and after the incident was 493 with a difference of -144 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 Equifax and their customers.

Maine residents recently reported "Maine Consumers Lose Over $33 Million to Fraud as Data Breaches Fuel Identity Theft Risks", a noteworthy cybersecurity incident.

During National Consumer Protection Week, cybersecurity experts highlighted the growing threat of identity theft after Maine residents lost more than $33 million to fraud in 2023.

The disruption is felt across the environment, and exposing Personal information, Social Security numbers, plus an estimated financial loss of $33 million (Maine residents in 2023).

In response, moved swiftly to contain the threat with measures like Credit freezes, E-Verify Self Lock, SSA Account Block, and stakeholders are being briefed through Public advisories during National Consumer Protection Week.

The case underscores how teams are taking away lessons such as Proactive measures like credit freezes and SSN protection tools (E-Verify Self Lock, SSA Account Block) are critical to mitigating identity theft risks, and recommending next steps like Freeze credit with Experian, Equifax, and TransUnion, Use E-Verify’s Self Lock to protect SSNs and Enable SSA Account Block for online access restrictions, with advisories going out to stakeholders covering Cybersecurity experts and fraud prevention advocates.

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.

MITRE ATT&CK® Correlation Analysis

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 Compromise Accounts (T1586) with moderate confidence (60%), supported by evidence indicating data breaches exposing personal information including Social Security numbers and Trusted Relationship (T1199) with moderate confidence (50%), supported by evidence indicating major credit bureaus (Experian, Equifax, TransUnion) implicated in breaches. Under the Credential Access tactic, the analysis identified Gather Victim Identity Information: Credentials (T1589.001) with moderate to high confidence (80%), supported by evidence indicating social Security numbers exposed in data breaches and Unsecured Credentials: Credentials In Files (T1552.001) with moderate to high confidence (70%), supported by evidence indicating personal information compromised via data breaches. Under the Collection tactic, the analysis identified Data from Local System (T1005) with moderate to high confidence (70%), supported by evidence indicating data breaches exposed personal information, Social Security numbers and Data from Information Repositories (T1213) with moderate to high confidence (80%), supported by evidence indicating credit bureaus (Experian, Equifax, TransUnion) targeted for PII. Under the Exfiltration tactic, the analysis identified Exfiltration Over C2 Channel (T1041) with moderate confidence (60%), supported by evidence indicating data breaches fueling identity theft risks for Maine residents and Automated Exfiltration (T1020) with moderate confidence (50%), supported by evidence indicating large-scale exposure of SSNs and personal information. Under the Impact tactic, the analysis identified Data Destruction (T1485) with lower confidence (40%), supported by evidence indicating identity theft risks leading to financial and reputational harm and Defacement: Internal Defacement (T1491.001) with lower confidence (30%), supported by evidence indicating brand reputation impact implied by public advisories. These correlations help security teams understand the attack chain and develop appropriate defensive measures based on the observed tactics and techniques.

Initial Access
Compromise Accounts (60%)
Trusted Relationship (50%)
Credential Access
Gather Victim Identity Information: Credentials (80%)
Unsecured Credentials: Credentials In Files (70%)
Collection
Data from Local System (70%)
Data from Information Repositories (80%)
Exfiltration
Exfiltration Over C2 Channel (60%)
Automated Exfiltration (50%)
Impact
Data Destruction (40%)
Defacement: Internal Defacement (30%)