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Principal Cybersecurity Architect – Generative AI Security

GE Vernova
Schenectady, United StatesRemotefull_timeVerifiedPosted 8 Jul 2026
💰 $242,400/yr($145,400/yr$242,400/yr)

About the role

Job Description Summary

We are seeking a Principal Cybersecurity Architect – Generative AI Security to lead the design, governance, and secure adoption of Generative AI (GenAI) technologies across the enterprise.
This role will act as the primary security partner to the AI Foundry and AI platform teams, ensuring AI systems—including LLMs, agentic frameworks, and autonomous workflows—are designed with security, compliance, and resilience from the ground up.
The ideal candidate will define security strategy, establish enterprise controls, and create reusable patterns for GenAI solutions. You will operate at the intersection of AI innovation and cybersecurity—enabling rapid adoption of GenAI while managing emerging risks such as prompt injection, data leakage, model manipulation, and autonomous agent misuse.

Job Description

Key Responsibilities

GenAI Security Architecture & Strategy

  • Define and own the enterprise security architecture for Generative AI systems, including LLM-based applications, agentic AI, and AI platforms
  • Partner with the AI Foundry to design secure GenAI platforms, tools, and workflows
  • Establish architectural standards and guardrails for AI system design, deployment, and operation
  • Ensure alignment with enterprise cybersecurity policies, regulatory requirements, and risk frameworks

Secure-by-Design AI Systems

  • Embed secure-by-design principles into AI pipelines, including model access, data ingestion, inference, and output handling
  • Support Business Application Solutions Engagement (BASE) reviews and partner with the AI foundry to design secure GenAI systems.
  • Conduct architecture reviews to identify risks across AI systems, including data exposure, model misuse, and integration vulnerabilities
  • Publish Risk Assessment Reports that include threats and mitigating controls aligned to GEV policies and standards
  • Define required security controls across the AI lifecycle (training, fine-tuning, inference, and retrieval-augmented workflows)

AI Threat Modeling & Risk Management

  • Lead threat modeling and risk assessments for GenAI systems using frameworks such as:
    • OWASP Top 10 for LLM Applications
    • OWASP Top 10 for Agentic Applications
    • MITRE ATT&CK / ATLAS (for AI threats)
  • Identify threats including:
    • Prompt injection and indirect prompt manipulation
    • Model inversion and data exfiltration
    • Agent autonomy abuse and unsafe tool execution
  • Define mitigating controls and risk treatment strategies

Zero Trust for AI & Agent Security

  • Apply Zero Trust principles to AI systems, including:
    • Identity and authentication for AI agents and services
    • Least-privilege access to tools, APIs, and data sources
    • Continuous validation of agent actions and interactions
  • Define security models for agent-to-agent, agent-to-system, and agent-to-data interactions
  • Implement guardrails to constrain agent autonomy and enforce policy compliance

Model Context Protocol & AI Integration Security

  • Define secure patterns for Model Context Protocol (MCP) and agent orchestration frameworks
  • Ensure secure handling of context, memory, and tool invocation across AI workflows
  • Establish governance for data access and context injection into LLMs and agents
  • Evaluate and secure integrations with internal APIs, SaaS platforms, and data services

Reusable AI Security Patterns & Frameworks

  • Create and publish enterprise-approved GenAI security patterns and reference architectures
  • Standardize controls for common GenAI use cases (chatbots, copilots, autonomous agents, RAG systems)
  • Enable rapid adoption through reusable patterns that align to enterprise policies

Governance, Policy & Compliance

  • Establish enterprise policies, standards, and frameworks for AI security and governance
  • Ensure AI systems meet requirements for data privacy, compliance, and auditability
  • Partner with GRC, Legal, and Risk teams to define AI governance models
  • Develop controls for monitoring, logging, and incident response for AI systems

Technical Leadership & Enterprise Influence

  • Serve as the enterprise subject matter expert for GenAI security architecture
  • Influence AI strategy and adoption through security leadership
  • Mentor architects and engineers on AI security best practices
  • Represent the organization in AI security initiatives, vendor discussions, and standards bodies

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Company

GE Vernova

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