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Cis Benchmark Jobs (NOW HIRING)

... CIS Benchmark Cisco ISE NAC Network Segmentation Vulnerability Management CrowdStrike EDR ​Should Have SOC analysts Skills Data security (DLP, Encryption) Microsoft Intune (Security Policy ...

Senior Manager Product Security

$129K - $170K/yr

Manage AWS and GCP cloud security governance through various tools which implement CIS benchmark scans, WAF policies, and IaC standards control * Skills in documenting work and deliverables in a ...

Ensure all infrastructure solutions adhere to NIST 800-series and CIS Benchmark v4.0 security standards. * Implement secure configurations, identity management, and access control policies.

Endpoint hardening & macOS/Windows fleet management - configuration profiles, CIS benchmark enforcement, macOS scripting, software deployment, and patch management * Security automation - turn manual ...

Coordinate and oversee STIG/CIS benchmark validation activities and remediation efforts. Support internal and external audits including FedRAMP, PCI DSS, SOC 2, ISO 27001, and customer assessments.

... CIS benchmark enforcement, macOS scripting, software deployment, and patch management • Security automation -- turn manual security tasks into automated workflows: access reviews, alert triage ...

CIS Benchmark & Compliance Validation Assess client environments against CIS benchmarks and PCI DSS control requirements, tracking findings through to remediation closure. Attack Path Analysis ...

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Cis Benchmark information

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How much do cis benchmark jobs pay per hour?

As of Jul 8, 2026, the average hourly pay for cis benchmark in the United States is $28.86, according to ZipRecruiter salary data. Most workers in this role earn between $24.04 and $33.41 per hour, depending on experience, location, and employer.

What are CIS Benchmarks?

CIS Benchmarks are a set of best practices and guidelines developed by the Center for Internet Security (CIS) to help organizations secure their IT systems and data. These benchmarks provide detailed configuration recommendations for a wide range of technologies, including operating systems, cloud platforms, and applications. By following CIS Benchmarks, organizations can reduce vulnerabilities, meet compliance requirements, and strengthen their overall security posture.

What are the key skills and qualifications needed to thrive as a CIS Benchmark Security Analyst, and why are they important?

To thrive as a CIS Benchmark Security Analyst, you need a strong understanding of information security principles, system hardening, and compliance frameworks, often supported by a degree in cybersecurity or related fields. Familiarity with CIS Benchmarks, security configuration assessment tools (such as CIS-CAT or Nessus), and relevant certifications like CISSP or CompTIA Security+ is typically required. Attention to detail, analytical thinking, and effective communication are crucial soft skills for interpreting results and collaborating with IT teams. These capabilities are vital to ensure organizational systems are properly secured, compliant, and resilient against cyber threats.

What is the difference between Cis Benchmark vs Security Analyst?

AspectCis BenchmarkSecurity Analyst
CredentialsNone specific, but familiarity with security standardsCertifications like CompTIA Security+, CISSP often preferred
Work EnvironmentSecurity configuration and compliance settingsMonitoring, analyzing security threats, incident response
Industry UsageUsed by organizations to establish security benchmarksEmployed in cybersecurity teams to protect systems

The Cis Benchmark focuses on creating security configuration standards, while a Security Analyst actively monitors and responds to security threats. Both roles are essential in cybersecurity, with the Cis Benchmark providing guidelines and the Security Analyst implementing and managing security measures.

What are some common challenges faced by professionals responsible for implementing CIS Benchmarks in an organization?

Professionals tasked with implementing CIS Benchmarks often face challenges such as balancing security with operational needs, ensuring consistent configuration across diverse systems, and managing resistance to change from stakeholders. Additionally, staying up to date with evolving benchmarks and remediating legacy systems to meet strict security controls can be complex. Collaboration with IT, security, and compliance teams is crucial to address these challenges effectively and maintain ongoing compliance.
Infographic showing various Cis Benchmark job openings in the United States as of July 2026, with employment types broken down into 83% Full Time, and 17% Contract. Highlights an 66% In-person, 17% Hybrid, and 17% Remote job distribution, with an average salary of $60,039 per year, or $28.9 per hour.
Data Architect (Lead Data Modeler (Cybersecurity Canonical Model))

Data Architect (Lead Data Modeler (Cybersecurity Canonical Model))

Zenith services

Charlotte, NC

Contractor

Posted 21 days ago


Job description

JOB DESCRIPTION:

Lead Data Modeler (Cybersecurity Canonical Model)

Role Summary

The Lead Data Modeler will own the design and enhancement of the enterprise canonical data model for cybersecurity baselines, ensuring the model is asset‑agnostic, extensible, and aligned to CIS benchmarks. This role bridges security domain knowledge and enterprise data architecture.

Key Responsibilities

  • Design and enhance the Canonical Data Model (CDM) for enterprise security baselines
  • Define asset‑agnostic entities, attributes, relationships, and metadata standards
  • Perform detailed mapping of baseline parameters from Excel, Word, and JSON sources to the CDM
  • Integrate CIS Benchmark structures to create draft and reference baselines
  • Partner with engineers to ensure the model is optimized for MongoDB (document design, nesting, indexing)
  • Define logical lineage, business definitions, and governance metadata
  • Support transformation rules and data standardization decisions

Skills

  • Canonical / enterprise data modeling
  • Cybersecurity baseline concepts, CIS Benchmarks
  • Document & semi‑structured data modeling (JSON, hierarchical models)
  • MongoDB data modeling best practices
  • Metadata management and governance