Associate Director - Application Security

spgi; Greater London; On-site; GBP 93,000 - 122,000; Full time

Job summary

S&P Global is seeking a DevSecOps Engineer focused on artifact management and software supply chain security in a UK-based or hybrid environment. The role spans securing build artifacts, enforcing trust models, and integrating governance across CI/CD pipelines built on GitHub, Jenkins, and Azure DevOps.

You will partner with AppSec and engineering teams to implement secure-by-design AI workflows. Responsibilities include designing repository architectures, enforcing immutability and

Qualifications

• 3-6 years of experience in DevSecOps, platform security, or software supply chain security.
• Strong hands-on experience with JFrog Artifactory, including deployment and enterprise architecture.
• Experience designing package curation and promotion models.
• Foundational understanding of AI/ML and Generative AI concepts, including LLMs and model lifecycle.
• Knowledge of AI/ML security risks such as prompt injection, data poisoning, model evasion, and data leakage.
• Experience integrating AI or ML components into applications or pipelines (preferred hands-on exposure).
• Familiarity with Responsible AI principles and AI governance frameworks.
• Experience implementing waiver and approval workflows for dependencies and artifacts.
• Strong understanding of application security principles and dependency risk management.
• Hands-on experience integrating repositories with GitHub, Jenkins, and Azure DevOps pipelines.
• Experience working in cloud environments (Azure preferred; AWS/GCP acceptable).
• Proficiency with automation and scripting (Python, Groovy, Terraform, etc.).
• Knowledge of modern SDLC and DevSecOps operating models.

Responsibilities

• Design, deploy, and operate enterprise artifact repository platforms supporting cloud and hybrid environments.
• Define and enforce package curation, promotion, and trust models aligned with application security and compliance requirements.
• Implement and govern waiver and approval workflows for dependency and artifact usage, ensuring risk-based decision‑making.
• Partner with AppSec, platform, and engineering teams to standardize secure dependency and artifact consumption patterns.
• Define and maintain repository architectures supporting multiple environments, teams, and trust boundaries.
• Enforce policies ensuring artifact immutability, provenance, versioning, and trusted sourcing.
• Integrate artifact repositories into CI/CD pipelines built on GitHub, Jenkins, and Azure DevOps.
• Embed security controls for AI/ML and GenAI workloads within CI/CD pipelines and developer workflows.
• Define and enforce secure usage patterns for LLMs and AI services, including prompt handling, data protection, and model access controls.
• Implement safeguards against AI‑specific threats, including prompt injection, model poisoning, data leakage, and insecure model outputs.
• Integrate AI security scanning and validation into build pipelines, ensuring safe model usage and dependency integrity.
• Collaborate with engineering teams to establish secure‑by‑design AI application architectures.
• Ensure compliance with enterprise Responsible AI policies (data privacy, bias management, model governance).
• Secure AI‑related secrets, tokens, and API access used in pipelines and applications.
• Monitor and respond to security risks introduced by AI/ML components, including third‑party models and APIs.
• Contribute to AI risk governance, auditability, and traceability across the SDLC.
• Stay current on emerging AI security threats, vulnerabilities, and regulatory expectations.
• Align artifact and dependency controls with cloud security best practices for deployed applications.
• Monitor usage, risk posture, and effectiveness of artifact controls and drive continuous improvement.
• Develop automation and policy‑as‑code for artifact lifecycle management, approvals, and governance.
• Support security incident investigations related to software supply chain integrity or dependency risk.
• Create documentation, standards, and enablement materials for secure developer adoption.

Skills

DevSecOps

JFrog Artifactory

AI/ML security

CI/CD security

Cloud platforms

Automation scripting

Python

Groovy

Terraform

GitHub/Jenkins/Azure DevOps

Tools

GitHub & GitHub Actions

Jenkins

Azure DevOps

Job description

About the Role:

Grade Level (for internal use): 12 The DevSecOps Engineer - Artifact Management & Software Supply Chain Security focuses on securing and governing enterprise artifact and dependency management platforms. This role combines DevSecOps, application security, and cloud security to ensure that build artifacts and dependencies are trusted, curated, and consumed securely across CI/CD pipelines and cloud environments.

Key Responsibilities

• Design, deploy, and operate enterprise artifact repository platforms supporting cloud and hybrid environments.
• Define and enforce package curation, promotion, and trust models aligned with application security and compliance requirements.
• Implement and govern waiver and approval workflows for dependency and artifact usage, ensuring risk-based decision‑making.
• Partner with AppSec, platform, and engineering teams to standardize secure dependency and artifact consumption patterns.
• Define and maintain repository architectures supporting multiple environments, teams, and trust boundaries.
• Enforce policies ensuring artifact immutability, provenance, versioning, and trusted sourcing.
• Integrate artifact repositories into CI/CD pipelines built on GitHub, Jenkins, and Azure DevOps.
• Embed security controls for AI/ML and GenAI workloads within CI/CD pipelines and developer workflows.
• Define and enforce secure usage patterns for LLMs and AI services, including prompt handling, data protection, and model access controls.
• Implement safeguards against AI‑specific threats, including prompt injection, model poisoning, data leakage, and insecure model outputs.
• Integrate AI security scanning and validation into build pipelines, ensuring safe model usage and dependency integrity.
• Collaborate with engineering teams to establish secure‑by‑design AI application architectures.
• Ensure compliance with enterprise Responsible AI policies (data privacy, bias management, model governance).
• Secure AI‑related secrets, tokens, and API access used in pipelines and applications.
• Monitor and respond to security risks introduced by AI/ML components, including third‑party models and APIs.
• Contribute to AI risk governance, auditability, and traceability across the SDLC.
• Stay current on emerging AI security threats, vulnerabilities, and regulatory expectations.
• Align artifact and dependency controls with cloud security best practices for deployed applications.
• Monitor usage, risk posture, and effectiveness of artifact controls and drive continuous improvement.
• Develop automation and policy‑as‑code for artifact lifecycle management, approvals, and governance.
• Support security incident investigations related to software supply chain integrity or dependency risk.
• Create documentation, standards, and enablement materials for secure developer adoption.

Required Qualifications

• 3-6 years of experience in DevSecOps, platform security, or software supply chain security.
• Strong hands‑on experience with JFrog Artifactory, including deployment and enterprise architecture.
• Experience designing package curation and promotion models.
• Foundational understanding of AI/ML and Generative AI concepts, including LLMs and model lifecycle.
• Knowledge of AI/ML security risks such as prompt injection, data poisoning, model evasion, and data leakage.
• Experience integrating AI or ML components into applications or pipelines (preferred hands‑on exposure).
• Familiarity with Responsible AI principles and AI governance frameworks.
• Experience implementing waiver and approval workflows for dependencies and artifacts.
• Strong understanding of application security principles and dependency risk management.
• Hands‑on experience integrating repositories with GitHub, Jenkins, and Azure DevOps pipelines.
• Experience working in cloud environments (Azure preferred; AWS/GCP acceptable).
• Proficiency with automation and scripting (Python, Groovy, Terraform, etc.).
• Knowledge of modern SDLC and DevSecOps operating models.

Compensation/Benefits Information:

(This section is only applicable to US candidates) S&P Global states that the anticipated base salary range for this position is $125,000 to $165,000. Final base salary for this role will be based on the individual's geographic location, as well as experience level, skill set, training, licenses and certifications.

In addition to base compensation, this role is eligible for an annual incentive plan. This role is not eligible for additional compensation such as an annual incentive bonus or sales commission plan. This role is eligible to receive additional S&P Global benefits. For more information on the benefits we provide to our employees, please click here.

What's In It For You?

Our Mission:

Advancing Essential Intelligence.

Our People:

We're more than 35,000 strong worldwide-so we're able to understand nuances while having a broad perspective. Our team is driven by curiosity and a shared belief that Essential Intelligence can help build a more prosperous future for us all.From finding new way