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Analyze » Pick n Pay » PIC1780050597

Incident Score: Analysis & Impact (PIC1780050597)

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

Rankiteo Score Impact Analysis

Rankiteo Incident Impact-72
Company Score Before Incident771 / 1000
Company Score After Incident699 / 1000
INCIDENT NUMBERPIC1780050597
Type of Cyber IncidentBreach
ATTACK VECTORNA
DATA EXPOSEDPersonal information, partial payment card...
INCIDENT DATE31/12/2021
STATUSOngoing (forensic investigation)

Key Highlights From The Incident Analysis

  • Timeline of Pick n Pay'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 Pick n Pay 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 Pick n Pay breach identified under incident ID PIC1780050597.

The analysis begins with a detailed overview of Pick n Pay's information like the linkedin page: https://www.linkedin.com/company/pick-'n-pay, the number of followers: 462382, the industry type: Retail and the number of employees: 31490 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 771 and after the incident was 699 with a difference of -72 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 Pick n Pay and their customers.

Pick n Pay recently reported "Pick n Pay Confirms Data Breach Affecting Former *asap!* App Users", a noteworthy cybersecurity incident.

South African retail giant Pick n Pay has acknowledged a data breach exposing personal information of users from its former on-demand app, *Pick n Pay asap!* (previously known as *Bottles*).

The disruption is felt across the environment, affecting Former *Pick n Pay asap!* (Bottles) app, and exposing Personal information, partial payment card details, encrypted passwords, Smart Shopper numbers.

In response, teams activated the incident response plan, and began remediation that includes Reviewing data retention practices, strengthening security protocols, and stakeholders are being briefed through Email notification to customers, dedicated helpline and email support.

The case underscores how Ongoing (forensic investigation), and recommending next steps like Monitor communications for suspicious activity, avoid sharing sensitive details like PINs or one-time passwords, with advisories going out to stakeholders covering Information Regulator and law enforcement engaged.

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 Exploit Public-Facing Application (T1190) with moderate confidence (60%), supported by evidence indicating records dating back to 2022, which were recently discovered online and Trusted Relationship (T1199) with moderate confidence (50%), supported by evidence indicating former on-demand app, *Pick n Pay asap!* (previously known as *Bottles*). Under the Credential Access tactic, the analysis identified Credentials from Password Stores (T1555) with moderate to high confidence (70%), supported by evidence indicating encrypted passwords included in leaked dataset. Under the Collection tactic, the analysis identified Data from Local System (T1005) with moderate to high confidence (80%), supported by evidence indicating names, contact details, dates of birth, delivery addresses compromised and Data from Information Repositories (T1213) with moderate to high confidence (70%), supported by evidence indicating smart Shopper numbers (where linked) exposed. Under the Exfiltration tactic, the analysis identified Exfiltration Over C2 Channel (T1041) with moderate to high confidence (70%), supported by evidence indicating records dating back to 2022, which were recently discovered online and Exfiltration Over Web Service (T1567) with moderate confidence (60%), supported by evidence indicating leaked dataset includes personal information of users. Under the Impact tactic, the analysis identified Data Manipulation: Stored Data Manipulation (T1565.001) with moderate confidence (50%), supported by evidence indicating potentially enable phishing or social engineering attacks and Defacement: Internal Defacement (T1491.001) with lower confidence (40%), supported by evidence indicating brand reputation impact due to phishing/social engineering risks. These correlations help security teams understand the attack chain and develop appropriate defensive measures based on the observed tactics and techniques.

Initial Access
Exploit Public-Facing Application (60%)
Trusted Relationship (50%)
Credential Access
Credentials from Password Stores (70%)
Collection
Data from Local System (80%)
Data from Information Repositories (70%)
Exfiltration
Exfiltration Over C2 Channel (70%)
Exfiltration Over Web Service (60%)
Impact
Data Manipulation: Stored Data Manipulation (50%)
Defacement: Internal Defacement (40%)

Sources & References