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Comparison Overview

SF Fire Credit UnionSF Fire Credit Union
VS
Marsh McLennanMarsh McLennan
SF Fire Credit Union

SF Fire Credit Union

3201 California Street, San Francisco, California, US, 94118

Last Update: 04/04/2026

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Between 650 and 699
http://www.sffirecu.org
690/1000Weak

Founded in 1951 by firefighters for firefighters, we are a credit union dedicated to building financial success in our community. Today, we continue to serve firefighters while also extending membership to those who live, work, or attend school in San Francisco, San Ma...

NAICS:52
NAICS Definition:Finance and Insurance
Employees:115
Subsidiaries:0
12-month incidents
0
Known data breaches
3
Attack type number
1
Marsh McLennan

Marsh McLennan

1166 Avenue of the Americas, New York, NY, US, 10036

Last Update: 18/07/2026

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810/1000Good

Marsh (NYSE: MRSH) is a global leader in risk, strategy and people, advising clients in 130 countries across four businesses: Marsh Risk, Guy Carpenter, Mercer and Oliver Wyman. With annual revenue over $24 billion and more than 90,000 ...

NAICS:52
NAICS Definition:Finance and Insurance
Employees:45,730
Subsidiaries:0
12-month incidents
0
Known data breaches
1
Attack type number
2

Compliance Ranges Comparison

Based On Specific Ai Models Category
SF Fire Credit Union

SF Fire Credit Union

-
ISO 27001Not verified
ISO 27001
-
SOC2 Type 1Not verified
SOC2 Type 1
-
SOC2 Type 2Not verified
SOC2 Type 2
-
GDPRNot verified
GDPR
-
PCI DSSNot verified
PCI DSS
-
HIPAANot verified
HIPAA
Marsh McLennan

Marsh McLennan

-
ISO 27001Not verified
ISO 27001
-
SOC2 Type 1Not verified
SOC2 Type 1
-
SOC2 Type 2Not verified
SOC2 Type 2
-
GDPRNot verified
GDPR
-
PCI DSSNot verified
PCI DSS
-
HIPAANot verified
HIPAA

Benchmark & Cyber Underwriting Signals

Incidents vs Financial Services Industry Avg (This Year)

No incidents recorded for SF Fire Credit Union in 2026.

Incidents

Incidents vs Financial Services Industry Avg (This Year)

No incidents recorded for Marsh McLennan in 2026.

Incidents

Incident History - SF Fire Credit Union (X = Date, Y = Severity)

SF Fire Credit Union cyber incidents detection timeline including parent company and subsidiaries.

R - Ransomware
C - Cyber Attack
D - Data Breach
V - Vulnerability

Incident History - Marsh McLennan (X = Date, Y = Severity)

Marsh McLennan cyber incidents detection timeline including parent company and subsidiaries.

R - Ransomware
C - Cyber Attack
D - Data Breach
V - Vulnerability

Notable Incidents

Last Cyber / HR Incidents / Global...
SF Fire Credit Union

SF Fire Credit Union

Incidents
🔒 Incident : Breach
SFF1912151122
🔒 Incident : Breach
SF-922072525
🔒 Incident : Breach
SF-457072725
Marsh McLennan

Marsh McLennan

Incidents
🔒 Incident : Data Leak
MAR18242223
🔒 Incident : Breach
MAR307072525

FAQ

Between SF Fire Credit Union company and Marsh McLennan company, which one has the best AI Cybersecurity Score ?
Between SF Fire Credit Union company and Marsh McLennan company, which one has experienced more cyber incidents in the past ?
Between SF Fire Credit Union company and Marsh McLennan company, which one has experienced more cyber incidents this year ?
Between SF Fire Credit Union company and Marsh McLennan company, which one has experienced at least one ransomware attack ?
Between SF Fire Credit Union company and Marsh McLennan company, which one has experienced at least one data breach ?
Between SF Fire Credit Union company and Marsh McLennan company, which one has experienced at least one targeted cyberattack ?
Between SF Fire Credit Union company and Marsh McLennan company, which one has experienced at least one vulnerability ?
Between SF Fire Credit Union company and Marsh McLennan company, which one holds the most compliance certifications ?
Between SF Fire Credit Union company and Marsh McLennan company, which one holds the fewest compliance certifications ?
Between SF Fire Credit Union company and Marsh McLennan company, which one has the most subsidiaries ?
Between SF Fire Credit Union company and Marsh McLennan company, which one has the largest number of employees ?
Between SF Fire Credit Union and Marsh McLennan, which company holds both SOC 2 Type 1 certifications ?
Between SF Fire Credit Union and Marsh McLennan, which company holds both SOC 2 Type 2 certifications ?
Which company is ISO 27001 certified - SF Fire Credit Union or Marsh McLennan ?
Which company is PCI DSS compliant - SF Fire Credit Union or Marsh McLennan ?
Between SF Fire Credit Union and Marsh McLennan, which company complies with HIPAA regulations for healthcare data ?
Between SF Fire Credit Union and Marsh McLennan, which company complies with GDPR requirements ?

Latest Global CVEs

CVE-2026-63261
SUMMARY

Uncontrolled Resource Consumption (CWE-400) in Kibana can lead to denial of service via Excessive Allocation (CAPEC-130). A low-privileged authenticated user can send a specially crafted request to a Kibana machine learning feature, causing the server to exhaust available memory and become unavailable to all users.

PUBLISHED
Date2026-07-21
UPDATED
Date2026-07-21
RISK INFORMATION (Score: 6.5)
CVSS3
Base Score: 6.5
Complexity: LOW
CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H
IMPACT SCORE
3.6
EXPLOITABILITY
2.8
CVE-2026-63260
SUMMARY

Uncontrolled Resource Consumption (CWE-400) in Kibana can lead to denial of service via Excessive Allocation (CAPEC-130). An authenticated attacker with low-privilege access can trigger a denial of service condition in Kibana by sending a specially crafted, oversized request payload. Processing this user-supplied input requires resource-intensive memory allocation that can exhaust the available heap memory in the Kibana process, causing it to crash and become unavailable to all users.

PUBLISHED
Date2026-07-21
UPDATED
Date2026-07-21
RISK INFORMATION (Score: 6.5)
CVSS3
Base Score: 6.5
Complexity: LOW
CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H
IMPACT SCORE
3.6
EXPLOITABILITY
2.8
CVE-2026-63259
SUMMARY

Authorization Bypass Through User-Controlled Key (CWE-639) in Kibana can lead to information disclosure via user-supplied identifiers that reference scheduled query result data from Kibana Spaces the requester is not authorized to access.

PUBLISHED
Date2026-07-21
UPDATED
Date2026-07-21
RISK INFORMATION (Score: 4.3)
CVSS3
Base Score: 4.3
Complexity: LOW
CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:L/I:N/A:N
IMPACT SCORE
1.4
EXPLOITABILITY
2.8
CVE-2026-63145
SUMMARY

Incorrect Authorization (CWE-863) in Kibana can lead to integrity compromise of Machine Learning audit and notification records via Accessing Functionality Not Properly Constrained by ACLs (CAPEC-1). A vulnerability exists in Kibana's Machine Learning functionality where a Machine Learning management endpoint performs an insufficient authorization check. The endpoint validates only a coarse privilege level but does not verify that the requesting user has access to the specific Machine Learning job or notification resources provided in the request. As a result, a low-privileged user with Machine Learning access in any Kibana space can manipulate Machine Learning audit and notification records for arbitrary jobs—including jobs in other spaces or belonging to other users—by leveraging Kibana's internally elevated credentials to write to restricted Machine Learning system indices that the user cannot access directly.

PUBLISHED
Date2026-07-21
UPDATED
Date2026-07-21
RISK INFORMATION (Score: 4.3)
CVSS3
Base Score: 4.3
Complexity: LOW
CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:L/A:N
IMPACT SCORE
1.4
EXPLOITABILITY
2.8
CVE-2026-63144
SUMMARY

Uncontrolled Recursion (CWE-674) in Elasticsearch can lead to denial of service via a specially crafted search request submitted by a low-privileged authenticated user. A user with read-level index access can submit a request that triggers unbounded recursive processing within the Elasticsearch query evaluation component, causing a fatal error that terminates the affected node. In single-node deployments, this results in complete service outage; in multi-node clusters, it causes repeated node restarts and sustained availability degradation.

PUBLISHED
Date2026-07-21
UPDATED
Date2026-07-21
RISK INFORMATION (Score: 6.5)
CVSS3
Base Score: 6.5
Complexity: LOW
CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H
IMPACT SCORE
3.6
EXPLOITABILITY
2.8