AI Enablement and Security Analyst

GE HealthCare•Chicago, IL

1d•Hybrid

About The Position

The AI Security Analyst will help oversee GE HealthCare’s AI security governance, risk, and compliance processes to help defend against cyber threats, malicious actors, and emerging risks associated with AI systems, agents, models, and integrations. This role will support the secure design, deployment, and operation of AI capabilities across the enterprise, while helping ensure compliance with internal policies, external regulations, and security standards. GE HealthCare is a leading global medical technology and digital solutions innovator. Our mission is to improve lives in the moments that matter. Unlock your ambition, turn ideas into world-changing realities, and join an organization where every voice makes a difference, and every difference builds a healthier world.

Requirements

Bachelor’s Degree in Computer Science or “STEM” Majors (Science, Technology, Engineering and Math) with 4 +experience

Nice To Haves

Experience in cybersecurity, security governance, security architecture, or AI/ML security
Knowledge of AI systems, LLMs, agentic workflows, model deployment patterns, and AI-related security risks
Understanding of MCP, A2A protocols, API security, identity and access management, and secure system integration concepts
Experience assessing risks in AI pipelines including prompts, tools, plugins, model endpoints, data flows, and orchestration frameworks
Knowledge of cloud and hybrid environments, including AWS and/or Azure services, is an advantage
Experience with locally hosted or self-managed AI models, model serving infrastructure, GPU-based environments, and on-premise AI deployments is a plus
Familiarity with governance, privacy, and compliance considerations related to AI usage and data handling
Ability to write clear guidance documents, standards, and training materials for technical and non-technical stakeholders
Ability to work across global teams and influence multiple stakeholders
Ability to travel 10% of the time as needed

Responsibilities

Ensure compliance of AI solutions, platforms, and model deployments with internal and external security requirements
Guide application, platform, and product owners on AI security architecture, configuration, and governance requirements
Assess and follow up on non-conformances related to AI models, agents, model APIs, prompt workflows, vector databases, inference endpoints, and orchestration layers
Investigate and further assess potential AI security risks including prompt injection, data leakage, insecure tool use, model misuse, identity and access weaknesses, and supply chain risks
Support security reviews of AI systems including foundation models, locally hosted models, retrieval-augmented generation (RAG) solutions, agentic workflows, and model-serving infrastructure
Lead and/or aid security assessments and access reviews for AI platforms, model environments, and related cloud or on-premise services
Identify non-conforming AI deployments, integrations, and usage patterns across business and technical environments
Support governance of AI security tooling, controls, and monitoring processes
Ensure feasibility of AI security requirements set for engineering and product teams
Work in a global role with multiple stakeholders from different domains and different regions
Provide regular program updates to the CISO, CIOs, and other stakeholders
Establish and maintain relationships with stakeholders including CIOs, and global IT, IT security, engineering, legal, privacy, and compliance leaders
Ensure proper alignment of AI security requirements to GE HealthCare’s IT Security Framework and enterprise AI governance standards
Maintain exception processes for AI platforms, model usage, and supporting infrastructure
Support communication and awareness efforts related to secure AI adoption
Draft communications and training materials
Write guidance documents, standards, and secure implementation patterns for AI solutions
Review AI integration patterns involving MCP, A2A protocols, tool-calling frameworks, and agent communication mechanisms to ensure secure design and operation
Support risk assessments for third-party AI services, open-source models, and locally hosted AI model deployments
Partner with engineering teams to promote secure AI development lifecycle practices, including threat modeling, validation, testing, monitoring, and incident response preparedness