AI Governance Security Engineer

Dice is seeking an AI Governance Security Engineer to own risk assessment across four AI governance domains at Zoom. The role involves working with AI/ML teams and security engineering to define controls and standards that enable safe AI adoption, operationalizing the AI Governance Risk Charter.

Responsibilities:

• Conducting risk evaluations of AI use cases across GenAI, agentic AI, shadow AI, and AI gateway/MCP domains, scoring exposure using Likelihood and Impact methodology and maintaining the AI governance risk register
• Identifying security gaps in GenAI deployments including prompt injection, data leakage, model misuse, and output handling, and recommending targeted controls
• Assessing agentic AI systems for risks around autonomy, tool access, privilege escalation, and unintended action, and defining monitoring requirements
• Detecting and evaluating shadow AI and local/unsanctioned AI usage, quantifying risk and recommending detection and enforcement approaches
• Evaluating AI security tooling including AI gateways, CASB/SWG, Model Context Protocol governance solutions, and agentic monitoring platforms for functional requirements and integration
• Contributing to security frameworks, architectural standards, and policies that translate the AI Governance Risk Charter into concrete technical requirements and control implementations
• Applying security engineering judgment across network, endpoint, cloud, and data security domains to support architecture reviews, risk assessments, and incident response
• Partnering with AI/ML, engineering, and enterprise teams through collaborative working groups to identify security solutions and tracking execution

Requirements:

• 5+ years of experience in security engineering, cybersecurity, or a closely related field
• Demonstrated understanding of cybersecurity frameworks, compliance requirements, and emerging threat landscapes
• Hands-on experience with risk assessment and risk management methodologies, including impact-based prioritization
• Working knowledge of modern AI/ML systems and the security risks they introduce (LLMs, generative AI, agentic systems)
• Working knowledge of core security domains beyond AI (e.g., network, endpoint, cloud, or data security) sufficient to contribute to architecture reviews and risk assessments
• Experience securing GenAI, agentic AI, or LLM-based applications in enterprise environments would be a bonus
• Familiarity with AI gateways, Model Context Protocol (MCP), CASB/SWG, or DLP tooling
• Experience detecting and governing shadow IT / shadow AI usage would be advantageous
• Threat modeling experience applied to AI or novel technology environments
• Experience contributing to incident response, detection engineering, or purple team exercises would be a bonus