Comparison Overview

PJL Group

VS

Great Lakes Filters

PJL Group

27 Leewood Drive, Orange, NSW, 2800, AU
Last Update: 2025-11-27
Between 750 and 799

PJL Group are a mechanical and engineering business keeping equipment on the job, improving processes & providing solutions and specialty services to the mining, earth moving, drilling & construction industries. Our vision, innovation and commitment to quality and customised solutions has resulted in a highly skilled labour force, in excess of 200 personnel who have built a proud reputation as one of the most productive, efficient and safe teams in their industry sector. Our size also enables repairs or installation work on any scale. Operating 24 hours a day Australia-wide combined with our flexibility, quick decision making and fast turnaround times are measured by our clients in tonnes. PJL - Engineering Opportunities into Solutions

NAICS: None
NAICS Definition: Others
Employees: 171
Subsidiaries: 0
12-month incidents
0
Known data breaches
0
Attack type number
0

Great Lakes Filters

Bloomfield Hills Pkwy., Bloomfield Hills, US
Last Update: 2025-11-27

Since 1951, Great Lakes Filters has been a leader in process filtration innovation and the conversion of technical textiles. We specialize in high-performance filters, filter fabrics, and comprehensive filtration solutions for a wide range of industries, including chemical, pharmaceutical, food & beverage, and industrial applications. As a division of Acme Mills, a pioneer in technical textile conversion since 1917, we have access to one of the largest assortments of woven and nonwoven fabrics. Our deep expertise allows us to provide customized, high-quality, and cost-effective solutions tailored to the unique needs of our clients. At Great Lakes Filters, we are committed to innovation, quality, and customer satisfaction. We work closely with our clients and suppliers to develop efficient, durable, and reliable filtration products, ensuring seamless operation and enhanced performance across industries.

NAICS: None
NAICS Definition: Others
Employees: 6
Subsidiaries: 0
12-month incidents
0
Known data breaches
0
Attack type number
0

Compliance Badges Comparison

Security & Compliance Standards Overview

https://images.rankiteo.com/companyimages/pjl-group.jpeg
PJL Group
ISO 27001
ISO 27001 certification not verified
Not verified
SOC2 Type 1
SOC2 Type 1 certification not verified
Not verified
SOC2 Type 2
SOC2 Type 2 certification not verified
Not verified
GDPR
GDPR certification not verified
Not verified
PCI DSS
PCI DSS certification not verified
Not verified
HIPAA
HIPAA certification not verified
Not verified
https://images.rankiteo.com/companyimages/great-lakes-filters.jpeg
Great Lakes Filters
ISO 27001
ISO 27001 certification not verified
Not verified
SOC2 Type 1
SOC2 Type 1 certification not verified
Not verified
SOC2 Type 2
SOC2 Type 2 certification not verified
Not verified
GDPR
GDPR certification not verified
Not verified
PCI DSS
PCI DSS certification not verified
Not verified
HIPAA
HIPAA certification not verified
Not verified
Compliance Summary
PJL Group
100%
Compliance Rate
0/4 Standards Verified
Great Lakes Filters
0%
Compliance Rate
0/4 Standards Verified

Benchmark & Cyber Underwriting Signals

Incidents vs Mechanical Or Industrial Engineering Industry Average (This Year)

No incidents recorded for PJL Group in 2025.

Incidents vs Mechanical Or Industrial Engineering Industry Average (This Year)

No incidents recorded for Great Lakes Filters in 2025.

Incident History — PJL Group (X = Date, Y = Severity)

PJL Group cyber incidents detection timeline including parent company and subsidiaries

Incident History — Great Lakes Filters (X = Date, Y = Severity)

Great Lakes Filters cyber incidents detection timeline including parent company and subsidiaries

Notable Incidents

Last 3 Security & Risk Events by Company

https://images.rankiteo.com/companyimages/pjl-group.jpeg
PJL Group
Incidents

No Incident

https://images.rankiteo.com/companyimages/great-lakes-filters.jpeg
Great Lakes Filters
Incidents

No Incident

FAQ

PJL Group company demonstrates a stronger AI Cybersecurity Score compared to Great Lakes Filters company, reflecting its advanced cybersecurity posture governance and monitoring frameworks.

Historically, Great Lakes Filters company has disclosed a higher number of cyber incidents compared to PJL Group company.

In the current year, Great Lakes Filters company and PJL Group company have not reported any cyber incidents.

Neither Great Lakes Filters company nor PJL Group company has reported experiencing a ransomware attack publicly.

Neither Great Lakes Filters company nor PJL Group company has reported experiencing a data breach publicly.

Neither Great Lakes Filters company nor PJL Group company has reported experiencing targeted cyberattacks publicly.

Neither PJL Group company nor Great Lakes Filters company has reported experiencing or disclosing vulnerabilities publicly.

Neither PJL Group nor Great Lakes Filters holds any compliance certifications.

Neither company holds any compliance certifications.

Neither PJL Group company nor Great Lakes Filters company has publicly disclosed detailed information about the number of their subsidiaries.

PJL Group company employs more people globally than Great Lakes Filters company, reflecting its scale as a Mechanical Or Industrial Engineering.

Neither PJL Group nor Great Lakes Filters holds SOC 2 Type 1 certification.

Neither PJL Group nor Great Lakes Filters holds SOC 2 Type 2 certification.

Neither PJL Group nor Great Lakes Filters holds ISO 27001 certification.

Neither PJL Group nor Great Lakes Filters holds PCI DSS certification.

Neither PJL Group nor Great Lakes Filters holds HIPAA certification.

Neither PJL Group nor Great Lakes Filters holds GDPR certification.

Latest Global CVEs (Not Company-Specific)

Description

Angular is a development platform for building mobile and desktop web applications using TypeScript/JavaScript and other languages. Prior to versions 19.2.16, 20.3.14, and 21.0.1, there is a XSRF token leakage via protocol-relative URLs in angular HTTP clients. The vulnerability is a Credential Leak by App Logic that leads to the unauthorized disclosure of the Cross-Site Request Forgery (XSRF) token to an attacker-controlled domain. Angular's HttpClient has a built-in XSRF protection mechanism that works by checking if a request URL starts with a protocol (http:// or https://) to determine if it is cross-origin. If the URL starts with protocol-relative URL (//), it is incorrectly treated as a same-origin request, and the XSRF token is automatically added to the X-XSRF-TOKEN header. This issue has been patched in versions 19.2.16, 20.3.14, and 21.0.1. A workaround for this issue involves avoiding using protocol-relative URLs (URLs starting with //) in HttpClient requests. All backend communication URLs should be hardcoded as relative paths (starting with a single /) or fully qualified, trusted absolute URLs.

Risk Information
cvss4
Base: 7.7
Severity: LOW
CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:N/VC:N/VI:N/VA:N/SC:H/SI:N/SA:N/E:X/CR:X/IR:X/AR:X/MAV:X/MAC:X/MAT:X/MPR:X/MUI:X/MVC:X/MVI:X/MVA:X/MSC:X/MSI:X/MSA:X/S:X/AU:X/R:X/V:X/RE:X/U:X
Description

Forge (also called `node-forge`) is a native implementation of Transport Layer Security in JavaScript. An Uncontrolled Recursion vulnerability in node-forge versions 1.3.1 and below enables remote, unauthenticated attackers to craft deep ASN.1 structures that trigger unbounded recursive parsing. This leads to a Denial-of-Service (DoS) via stack exhaustion when parsing untrusted DER inputs. This issue has been patched in version 1.3.2.

Risk Information
cvss4
Base: 8.7
Severity: LOW
CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:N/VC:N/VI:N/VA:H/SC:N/SI:N/SA:N/E:X/CR:X/IR:X/AR:X/MAV:X/MAC:X/MAT:X/MPR:X/MUI:X/MVC:X/MVI:X/MVA:X/MSC:X/MSI:X/MSA:X/S:X/AU:X/R:X/V:X/RE:X/U:X
Description

Forge (also called `node-forge`) is a native implementation of Transport Layer Security in JavaScript. An Integer Overflow vulnerability in node-forge versions 1.3.1 and below enables remote, unauthenticated attackers to craft ASN.1 structures containing OIDs with oversized arcs. These arcs may be decoded as smaller, trusted OIDs due to 32-bit bitwise truncation, enabling the bypass of downstream OID-based security decisions. This issue has been patched in version 1.3.2.

Risk Information
cvss4
Base: 6.3
Severity: LOW
CVSS:4.0/AV:N/AC:L/AT:P/PR:N/UI:N/VC:N/VI:L/VA:N/SC:N/SI:N/SA:N/E:X/CR:X/IR:X/AR:X/MAV:X/MAC:X/MAT:X/MPR:X/MUI:X/MVC:X/MVI:X/MVA:X/MSC:X/MSI:X/MSA:X/S:X/AU:X/R:X/V:X/RE:X/U:X
Description

Suricata is a network IDS, IPS and NSM engine developed by the OISF (Open Information Security Foundation) and the Suricata community. Prior to versions 7.0.13 and 8.0.2, working with large buffers in Lua scripts can lead to a stack overflow. Users of Lua rules and output scripts may be affected when working with large buffers. This includes a rule passing a large buffer to a Lua script. This issue has been patched in versions 7.0.13 and 8.0.2. A workaround for this issue involves disabling Lua rules and output scripts, or making sure limits, such as stream.depth.reassembly and HTTP response body limits (response-body-limit), are set to less than half the stack size.

Risk Information
cvss3
Base: 7.5
Severity: LOW
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H
Description

Suricata is a network IDS, IPS and NSM engine developed by the OISF (Open Information Security Foundation) and the Suricata community. In versions from 8.0.0 to before 8.0.2, a NULL dereference can occur when the entropy keyword is used in conjunction with base64_data. This issue has been patched in version 8.0.2. A workaround involves disabling rules that use entropy in conjunction with base64_data.

Risk Information
cvss3
Base: 7.5
Severity: LOW
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H