Incident Score: Analysis & Impact (FIDLPLAMEMER1779194480)
The details regarding individual company incidents & reports gives you full view from every side.
Rankiteo Score Impact Analysis
Key Highlights From The Incident Analysis
- Timeline of Ameriprise Financial Services, LLC'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 Ameriprise Financial Services, LLC 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 Ameriprise Financial Services, LLC breach identified under incident ID FIDLPLAMEMER1779194480.
The analysis begins with a detailed overview of Ameriprise Financial Services, LLC's information like the linkedin page: https://www.linkedin.com/company/ameriprise-financial-services-llc, the number of followers: 218937, the industry type: Financial Services and the number of employees: 17552 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 734 and after the incident was 669 with a difference of -65 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 Ameriprise Financial Services, LLC and their customers.
Fidelity Investments recently reported "Fidelity Investments Data Breach and Fine", a noteworthy cybersecurity incident.
A recent data breach at Fidelity Investments has resulted in a $1.25 million fine for failing to adequately protect customer data.
The disruption is felt across the environment, and exposing Sensitive client information, Social Security numbers, client financial records, plus an estimated financial loss of $1.25 million (fine).
Formal response steps have not been shared publicly yet.
The case underscores how teams are taking away lessons such as Stronger security measures and compliance protocols are needed to address evolving cyber risks in the financial sector, and recommending next steps like Enhance cybersecurity measures, improve regulatory compliance, and implement robust incident response plans.
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 Phishing (T1566) with high confidence (90%), supported by evidence indicating lPL Financial Incident such as A phishing attack led to unauthorized transactions and Exploit Public-Facing Application (T1190) with moderate confidence (50%), supported by evidence indicating fidelity failed to adequately protect customer data (implied vulnerability). Under the Credential Access tactic, the analysis identified Brute Force (T1110) with lower confidence (40%), supported by evidence indicating sensitive client information, including SSNs, was exposed (implied weak auth) and Unsecured Credentials (T1552) with moderate confidence (60%), supported by evidence indicating inadequate protection of customer data (root cause analysis). Under the Collection tactic, the analysis identified Data from Local System (T1005) with moderate to high confidence (80%), supported by evidence indicating sensitive client information, SSNs, and financial records compromised. Under the Exfiltration tactic, the analysis identified Exfiltration Over C2 Channel (T1041) with moderate to high confidence (70%), supported by evidence indicating 48,000 Ameriprise clients impacted (data exposure implied) and Transfer Data to Cloud Account (T1537) with moderate confidence (50%), supported by evidence indicating financial sector breaches often involve cloud exfiltration (implied). Under the Impact tactic, the analysis identified Data Destruction (T1485) with lower confidence (30%), supported by evidence indicating no details on malware or encryption, but fines/lawsuits suggest impact and Data Manipulation (T1565) with lower confidence (40%), supported by evidence indicating unauthorized transactions at LPL Financial (phishing incident). These correlations help security teams understand the attack chain and develop appropriate defensive measures based on the observed tactics and techniques.
Sources & References
- Ameriprise Financial Services, LLC Rankiteo Cyber Incident Details: https://www.rankiteo.com/company/ameriprise-financial-services-llc/incident/FIDLPLAMEMER1779194480
- Ameriprise Financial Services, LLC CyberSecurity Rating page: https://www.rankiteo.com/company/ameriprise-financial-services-llc
- Ameriprise Financial Services, LLC Rankiteo Cyber Incident Blog Article: https://blog.rankiteo.com/fidlplamemer1779194480-fidelity-investments-lpl-financial-mercer-ameriprise-financial-breach-january-2020/
- Ameriprise Financial Services, LLC CyberSecurity Score History: https://www.rankiteo.com/company/ameriprise-financial-services-llc/history
- Ameriprise Financial Services, LLC CyberSecurity Incident Source: https://www.thinkadvisor.com/2026/05/18/fidelitys-25m-data-breach-settlement-what-clients-should-know/
- Rankiteo A.I CyberSecurity Rating methodology: https://www.rankiteo.com/Images/rankiteo_algo.pdf
- Rankiteo TPRM Scoring methodology: https://static.rankiteo.com/model/rankiteo_tprm_methodology.pdf