Cybersecurity AI Risk and Governance Engineer, Global

About Vantage Data Centers

Vantage Data Centers powers, cools, protects and connects the technology of the world’s well-known hyperscalers, cloud providers and large enterprises. Developing and operating across North America, EMEA and Asia Pacific, Vantage has evolved data center design in innovative ways to deliver dramatic gains in reliability, efficiency and sustainability in flexible environments that can scale as quickly as the market demands.
Cybersecurity Department The AI Cybersecurity Engineer is responsible for the technical security, risk management, and governance enforcement of artificial intelligence (AI), machine learning (ML), and large language model (LLM) systems deployed across Vantage Data Centers’ operational, OT, and enterprise environments.

This role serves as the technical and security authority for AI security, ensuring AI systems are architected, deployed, and operated with appropriate controls for data protection, model integrity, access governance, monitoring, and human‑in‑the‑loop decision enforcement. The AI Cybersecurity Manager ensures AI technologies deliver business value without introducing unacceptable cyber, operational, safety, workforce, or regulatory risk, in alignment with the Global Policies and Standards.

Essential Functions
• Perform technical security testing and reviews of AI‑enabled applications, agents, and workflows, including architecture, design, and implementation, under established governance and Director direction.
• Implement approved security architecture patterns for AI, ML, and LLM systems across cloud, hybrid, on‑prem, and OT‑adjacent environments.
• Engineer secure inference paths, APIs, service identities, authentication flows, and segmentation boundaries aligned with least privilege and zero trust principles.
• Implement technical safeguards to mitigate prompt injection, unauthorized context expansion, data leakage, hallucination risk, and unsafe output handling.
• Configure and maintain controls for limiting, monitoring, logging, and managing AI usage across platforms, models, and agents.
• Implement and validate technical controls supporting model explainability, traceability, and output validation where AI impacts operational, workforce, safety, or compliance decisions.
• Review and validate LLM usage patterns, including prompt design, retrieval‑augmented generation (RAG), context window constraints, and output handling mechanisms.
• Implement controls preventing unauthorized external model training, reuse, or retention of enterprise data by third‑party AI platforms.
• Validate encryption, access logging, retention, and deletion controls for data ingested, processed, or generated by AI systems.
• Execute AI‑specific threat modeling activities and contribute findings to enterprise and OT cybersecurity risk assessments.
• Ensure AI systems produce security telemetry, logs, and audit trails sufficient to detect misuse, drift, policy violations, or anomalous behavior.
• Integrate AI security signals into SOC, SIEM, and incident response tooling and workflows.
• Support investigation and response to AI‑related incidents, including data exposure, model failure, unsafe outputs, or control breakdowns.
• Conduct technical security reviews of vendor‑provided and embedded AI capabilities, assessing model behavior, data handling, and control alignment.
• Enforce approved security requirements for AI vendors and prevent activation of AI features without required security validation and governance approval.
• Drive alignment with ISO 42001 and related AI governance standards across applicable teams.

Required Qualifications
• Bachelor’s degree in Cybersecurity, Computer Science, Data Science, Engineering, or related field, or equivalent experience.
• Minimum 5+ years of experience in cybersecurity, security architecture, or risk engineering roles.
• Hands‑on experience securing data pipelines, APIs, cloud platforms, and analytics or ML‑enabled systems.
• Strong understanding of identity, access management, encryption, logging, and secure system design.

Preferred Qualifications
• Direct experience securing AI/ML platforms, LLMs, or analytics pipelines.
• Experience with cloud security (Azure, AWS, GCP) and SaaS‑based AI platforms.
• Familiarity with OT, critical infrastructure, or safety‑critical environments.
• Security certifications such as Security+, SecurityAU+, CISM, or cloud security certifications.

Key Skills & Competencies
• AI and machine learning security
• LLM and generative AI risk management
• Security architecture and threat modeling
• Data protection and access governance
• Incident

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