AI Security Governance Principal

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Job Description Your Role We are seeking an experienced AI Security Governance Lead – Principal to establish and operate the AI security governance capability as a hands-on individual contributor. The role combines governance leadership with direct analyst execution, including AI use-case intake, inventory management, risk assessments, architecture and control reviews, governance documentation, evidence collection, issue tracking, metrics, and committee support. The individual will build structure in an evolving environment, independently manage the day-to-day workload, and influence decisions across all levels of the organization.

You Will Be Responsible For:

Governance Operations and Delivery Own and operate the end-to-end AI security governance workflow, from use-case intake and risk classification through review, approval, remediation, deployment, periodic reassessment, and retirement.

Create and maintain the enterprise inventory of AI use cases, models, agents, platforms, vendors, owners, data classifications, deployment environments, approval status, and residual risks.

Configure and administer intake forms, risk questionnaires, decision criteria, approval workflows, risk registers, exception records, and remediation trackers using Jira, Confluence, GRC platforms, or similar tools.

Manage the daily review queue, prioritize requests based on risk and business need, monitor service levels, and follow up directly with stakeholders to resolve gaps and overdue actions.

Lead security deliverables within the AI Foundations program from requirements definition through implementation and operationalization, including roadmaps, milestones, procedures, metrics, and sustainable ownership.

AI Model Risk Review Perform AI security and risk assessments in alignment with the enterprise model risk framework, and automate repeatable portions of the assessment process where practical.

Assess foundation models, local large language models, AI agents, AI platforms, retrieval-augmented generation solutions, and third-party AI services across their lifecycle.

Review solution architectures, data flows, prompts, integrations, identities, permissions, APIs, plugins, tool access, logging, monitoring, and agent actions to identify security and control weaknesses.

Execute structured control reviews and coordinate specialized testing for prompt injection, sensitive data exposure, insecure output handling, excessive agency, hallucination, jail breaking, supply-chain compromise, bias, and misalignment.

Define security requirements and compensating controls for AI systems of all types and levels of criticality; document findings, risk decisions, exceptions, control owners, and residual risk.

Shadow AI Risk Management Establish and operate controls for detecting, assessing, and governing shadow AI, including unauthorized models, applications, browser services, plugins, agents, and…