AI Governance Manager and AI Compliance Officer Job Descriptions

Why enterprise AI roles need a clean split

Enterprises deploying AI agents often collapse strategy, security oversight, policy enforcement, and audit evidence into a single vague “AI governance” title. That blurs accountability. Frameworks such as the NIST AI Risk Management Framework, ISO/IEC 42001, and the EU AI Act emphasize clear roles, leadership accountability, risk management across the lifecycle, human oversight, logging, and continual improvement. They do not prescribe a universal org chart, but they do require named ownership that is embedded in operating processes rather than treated as a one-time checklist.

A practical design is to separate the AI Governance Manager from the AI Compliance Officer. The Governance Manager designs and steers the management system: policies, risk classification, decision rights, residual-risk acceptance frameworks, and collaboration models. The Compliance Officer tests whether the system is working: conformity to policy and regulation, control effectiveness, evidence completeness, exception reporting, and readiness for internal and external audit. Keeping these duties distinct prevents the same function from both approving risk and declaring it controlled.

AI Governance Manager vs AI Compliance Officer

AI Governance Manager job description essentials

The AI Governance Manager establishes how the enterprise governs AI systems and agents end to end. Typical responsibilities include drafting and maintaining AI policies aligned to an AI management system; defining risk classification and assessment methods; assigning and communicating role authorities with leadership support; chairing or enabling cross-functional governance forums; and embedding Govern, Map, Measure, and Manage activities into intake, design, release, and retirement processes.

Decision rights should be explicit. The Governance Manager should own the frameworks for residual-risk acceptance, standards for when agent tool access or data-domain access is permitted, human-oversight breakpoints for elevated or irreversible actions, and the rules that product and platform teams must meet before production release. The role should not own day-to-day security control operation; that remains with the CISO organization. It also should not own final legal interpretation of regulatory text; that remains with legal and privacy counsel.

Required competencies include fluency with AI lifecycle controls such as system inventory and system cards, evaluations and measurement results that feed risk treatment, third-party and model-supplier assurance, incident response coordination for AI-specific failure modes, and the ability to translate management-system expectations into workable operating procedures. Experience bridging risk, technology, and product delivery is more valuable than a pure research or pure compliance background alone.

Measurable outcomes for the job description can include policy coverage across registered AI and agent use cases, percentage of high-risk systems with approved risk assessments before release, time to assign an accountable owner for new AI inventory entries, and closure of governance design gaps identified in internal reviews.

AI Compliance Officer job description essentials

The AI Compliance Officer provides objective assurance that AI systems and agents meet internal policy and applicable external obligations. Responsibilities center on conformity assessment, control testing, evidence management, monitoring cadence, exception tracking, and serving as the primary interface for auditors and, where relevant, regulatory inquiries. For higher-risk systems, this includes verifying that risk management, data governance, technical documentation, logging, transparency, human oversight, and post-market or post-release monitoring obligations are met in proportion to risk tier and jurisdiction.

Independence is a design requirement, not a preference. The Compliance Officer should be able to challenge product, platform, and even governance-design choices without reporting lines that create direct conflicts. The role owns monitoring findings quality, completeness of evidence packs, and escalation of unremediated exceptions. It does not set enterprise risk appetite and does not replace the CISO on security control ownership.

Technical literacy must be sufficient to evaluate whether controls actually operate. That means understanding how agent identity is established, how permissions and tool access are scoped, whether runtime allow, deny, or require-approval decisions are enforced centrally, and whether audit logs of prompts, tool calls, policy decisions, and outcomes are retained in a form suitable for investigation and evidence. The officer need not implement the control plane, but must know what complete evidence looks like.

Measurable outcomes include percentage of high-risk or customer-facing agents with complete pre-production evidence packs, audit finding closure rates, time-to-detect and time-to-escalate policy violations, exception aging, and recurring nonconformity rates by control domain.

Technical governance capabilities both roles must oversee

Job descriptions should reference control objectives for AI agents, not vendor feature names. Both roles need shared literacy across the following surfaces, with Governance defining standards and Compliance testing evidence of operation.

• Identity: How agents are uniquely established and attributed in production.
• Least-privilege permissions: How access to tools and data domains is scoped and reviewed.
• Tool-call policy: Which actions are allowed, denied, or require human approval.
• Runtime enforcement: Central gates that product teams cannot bypass.
• Audit logging: Retained records of prompts, tool calls, policy decisions, and outcomes suitable for investigation and evidence.

Operating-model boundaries and collaboration RACI

• CISO and security engineering: Own security control design and operation, identity infrastructure patterns, threat detection, and incident response. Governance aligns AI policy with security standards; Compliance tests AI-specific control evidence without absorbing SecOps.
• Legal and privacy: Interpret regulatory obligations, notices, and lawful basis questions. Governance translates counsel guidance into operational policy; Compliance verifies implemented controls and documentation match the agreed legal position.
• Enterprise risk: Sets risk appetite and escalates material residual risk. Governance operationalizes appetite into AI risk tiers and acceptance workflows; Compliance reports control gaps and exception trends against appetite.
• Platform engineering: Implements the control plane for identity, policy decision and enforcement points, and centralized runtime gates so product teams cannot bypass governance. Governance specifies required control objectives; Compliance validates enforcement and logging in lower and production environments.
• AI product and delivery teams: Build and run models and agents within approved patterns. They supply system cards, evaluation results, tool inventories, and change records. Neither governance nor compliance should become a shadow product owner.
• Human oversight breakpoints: Map required human approval for elevated privileges, irreversible actions, and regulated data processing. Governance defines breakpoints; Compliance samples whether runtime workflows actually stop and record those approvals.

Qualifications, seniority, and evaluation criteria

Enterprise postings should anchor qualifications in operating experience, not slogans. Prefer candidates who have implemented management-system elements such as policy, risk assessment, objectives, internal audit interface, and continual improvement in technology or regulated environments. For the Governance Manager, prior ownership of cross-functional operating models, risk frameworks, and executive forums is highly relevant. For the Compliance Officer, prior control testing, evidence management, audit response, and constructive challenge of delivery teams is highly relevant.

Technical depth should match the agent control surfaces described above. Leaders do not need to write enforcement code, but they must be able to review architecture choices such as a centralized policy decision path in front of tools and data resources, evidence pipelines that preserve model version and approval history, and separation of duties between those who ship agents and those who attest controls.

When evaluating candidates or written JDs, ask which decisions the role owns versus CISO and counsel; how independence is preserved; what artifacts are mandatory before a high-risk or customer-facing agent reaches production; and which KPIs show reduced residual risk and improved audit readiness. Public frameworks define accountability functions; your organization still must tailor titles, span of control, and regulatory scope to its inventory and risk tiers.

Runtime governance platforms can support these roles by enforcing policy, permissions, monitoring, and audit logging for agents in production. Role design remains an organizational responsibility: clear ownership first, tooling second.