AI Governance Security Engineer

Zoom

2d•$98,900 - $228,700•Remote

About The Position

You'll own risk assessment across four AI governance domains: GenAI Security, Agentic AI Security, Shadow AI detection, and AI Gateway/Model Context Protocol governance, identifying where emerging AI use cases create exposure. Working with AI/ML teams and security engineering, you'll define the controls, tooling, and standards that enable Zoom to adopt AI safely at scale. You'll operationalize the AI Governance Risk Charter while contributing your security engineering judgment across network, endpoint, cloud, and data security domains.

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.

Nice To Haves

Experience securing GenAI, agentic AI, or LLM-based applications in enterprise environments
Familiarity with AI gateways, Model Context Protocol (MCP), CASB/SWG, or DLP tooling.
Experience detecting and governing shadow IT / shadow AI usage
Threat modeling experience applied to AI or novel technology environments.
Experience contributing to incident response, detection engineering, or purple team exercises

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.