Control Tester & Advisor – Data & AI Governance

Role Summary

Control Tester and Advisor – Data & AI Governance is responsible for executing and leading control testing activities and providing risk and control advisory support related to Data Governance and AI Governance.

This role assesses design effectiveness and operating effectiveness of controls, performs end‑to‑end process walkthroughs, analyzes governance artifacts, and identifies, documents, and communicates control issues. In addition, the role partners with business, technology, and risk teams to provide advisory support on risk identification, control design, process improvements, and risk assessment activities related to data and AI governance.

The role requires strong testing judgment, the ability to develop and execute test steps, and the ability to provide practical, risk‑based advice while maintaining independence and objectivity.

• Execute design effectiveness and operating effectiveness testing of controls related to Data Governance and AI Governance, including data quality, data management, AI lifecycle governance, and ethical AI controls.
• Develop and document test steps and test scripts aligned to approved testing methodologies and risk frameworks.
• Perform reperformance testing, sampling, and evidence validation to assess control execution.
• Apply professional judgment to determine control effectiveness, identify control gaps, and assess residual risk.

• Provide risk, control, and process advisory support to business and technology partners related to Data and AI governance.
• Advise on control design, control enhancements, and process improvements to address identified risks or emerging governance expectations.
• Support and provide input into risk assessments, including identification of inherent risks, evaluation of mitigating controls, and assessment of control coverage.
• Assist stakeholders in understanding risk and control expectations, governance standards, and testing outcomes.
• Offer guidance on data and AI governance best practices, including alignment to internal policies, standards, and risk frameworks.
• Support proactive risk management efforts by identifying potential control weaknesses or governance gaps outside of formal testing cycles.

• Lead and conduct walkthroughs with control owners and stakeholders to understand end‑to‑end processes related to in‑scope control activities.
• Document process flows, control descriptions, and key risks based on walkthroughs and artifact reviews.
• Develop and maintain a working understanding of how data and AI controls operate within business and technology processes, enabling both testing and advisory activities.

• Analyze and assess business and governance artifacts, including:
• Data governance policies, standards, and procedures
• Data lineage, metadata, and data quality documentation
• AI governance artifacts (e.g., model lifecycle documentation, approvals, monitoring evidence)
• Evaluate whether artifacts sufficiently demonstrate control design, operating effectiveness, and risk mitigation.
• Provide advisory feedback to stakeholders where artifacts or documentation do not fully support risk and control expectations.

• Identify, document, and clearly articulate control deficiencies, design gaps, and operating issues, including root cause analysis.
• Draft clear, risk‑based issue descriptions and contribute to discussions on risk severity and impact.
• Provide actionable, practical recommendations that balance risk mitigation with business and operational considerations.
• Communicate testing results, risk insights, and advisory recommendations to stakeholders in a clear and professional manner.

• Act as a primary testing and advisory contact for business partners, technology teams, and risk partners for assigned areas.
• Partner with stakeholders to clarify control intent, evidence expectations, risk ownership, and remediation approaches.
• Support ongoing governance forums, working groups, or risk discussions related to Data and AI governance.
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