The Governance & Trust Engineering role at Frost & Sullivan focuses on ensuring the trustworthiness of MetaBrain AI solutions by translating research standards, data permissions, and AI risks into practical controls and test evidence. The incumbent will report to the Head of Decision Intelligence Governance & Trust Engineering and work closely with privacy, security, and advisory teams to maintain client confidence through rigorous oversight and accountable release processes. Key responsibilities include: Governing evidence and permitted use by maintaining registers for datasets, models, and workflows; defining assurance standards for research quality, bias detection, and accuracy; translating policy into technical controls such as audit events and tenant-isolation tests; and supporting accountable operations through risk assessments, model documentation, and incident coordination. Essential requirements include at least 4 years of experience in research quality, consulting delivery assurance, audit, risk, or data governance, along with strong evidence-based writing and structured risk judgment. Candidates should possess basic AI knowledge and the ability to identify AI output failures. Preferred qualifications include exposure to responsible AI frameworks, privacy-by-design, and data provenance. While hands-on coding in SQL or Python is useful for the governance role, it is a mandatory requirement for the Engineering variant of this position. This role operates within a hybrid work environment in Singapore.
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Skills for this role
Data GovernanceRisk ManagementAuditResponsible AIPrivacy-by-designSecurity TestingData ProvenanceAI AssuranceSQLPythonAccess-control testingLogging
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A focused preparation guide, built around this job’s responsibilities and requirements.
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Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.
01
Understand the role
What matters most
Establishing AI Assurance Frameworks
Develop and maintain rigorous standards for research quality, bias detection, and accuracy. This involves creating test protocols to identify AI output failures such as unsupported claims, inaccurate citations, and numerical errors, ensuring that MetaBrain solutions remain trustworthy.
Data Governance and Compliance Oversight
Maintain comprehensive registers for datasets, models, and decision workflows. You must track ownership, source licenses, consent, and retention policies to ensure all data usage aligns with legal and privacy requirements.
Translating Policy into Technical Controls
Collaborate with engineering teams to specify and implement practical controls, including role permissions, approval gates, audit events, and tenant-isolation tests, effectively bridging the gap between high-level policy and software implementation.
Accountable Risk Management
Support operational accountability by maintaining risk assessments, model documentation, and release evidence. You are expected to report risks independently from delivery pressures and coordinate incident responses or red-team findings.
Likely challenges to think through
Balancing Rigor with Delivery Speed
The role requires protecting client confidence without replacing professional judgment with a rigid checklist. You will need to navigate the tension between maintaining high assurance standards and the pressure to release software-enabled services.
Navigating Cross-Functional Influence
As a governance specialist, you must drive requirements and assurance evidence while working with technical partners, legal, and security teams. You must exert influence and provide constructive challenge without having formal authority over final risk acceptance or production release.
Technical Translation for AI Systems
You must be able to identify and explain AI output failures to non-technical stakeholders while simultaneously specifying technical requirements for engineers. Success requires a strong grasp of both the business context of the research and the technical implementation of controls.
03
Practice your answers
Technical practice
Designing AI Assurance Tests
What to coverHow would you design a test suite to detect AI hallucinations or inaccurate citations in a business intelligence model? Focus on the methodology for defining ground truth, identifying edge cases, and documenting remediation steps for identified failures.
Data Provenance and Governance
What to coverExplain how you would maintain a register for datasets and models to ensure compliance with source licenses and data permissions. Discuss the importance of tracking lineage and how you would handle conflicting retention or usage restrictions.
Translating Policy into Technical Controls
What to coverDescribe the process of translating high-level privacy or security policies into specific technical controls like audit events or tenant-isolation tests. How do you ensure these controls are both effective and measurable for an engineering team?
Identifying AI Output Failures
What to coverGiven a scenario where an AI model provides a biased recommendation, what steps would you take to analyze the root cause? Explain how you would document this failure and communicate the risk to stakeholders without relying solely on automated metrics.
Risk Assessment in AI Workflows
What to coverWhat framework would you use to conduct a risk assessment for a new AI-driven decision workflow? Address how you balance the need for rigorous oversight with the requirement to maintain delivery velocity.
Access Control and Logging
What to coverIn the context of a multi-tenant AI environment, how would you verify that access controls and logging mechanisms are functioning correctly? Suggest methods for testing that data masking and permission gates are applied consistently.
Privacy-by-Design Implementation
What to coverHow do you integrate privacy-by-design principles into the development lifecycle of an AI solution? Provide examples of how you would influence technical design choices early in the process to minimize data exposure.
Incident Coordination and Rollback
What to coverDescribe your approach to coordinating an incident response when an AI model produces unexpected or harmful results. How do you determine the criteria for triggering a rollback and ensuring advisor acceptance before re-release?
Behavioural practice
Navigating Delivery Pressure
What to coverDescribe a situation where you had to report significant risks or block a release despite pressure from delivery teams to proceed. How did you maintain your independence and ensure your concerns were documented and addressed?
Constructive Challenge in Cross-Functional Teams
What to coverGive an example of a time you had to provide constructive challenge to a technical or legal partner regarding a proposed AI implementation. How did you frame your argument to ensure alignment while maintaining professional relationships?
Evidence-Based Decision Making
What to coverTell me about a time you had to make a high-stakes decision based on incomplete or ambiguous data. How did you structure your judgment, and what evidence did you gather to support your final recommendation?
Managing Stakeholder Expectations
What to coverDescribe a scenario where you had to explain a complex AI risk to a non-technical stakeholder. How did you ensure they understood the implications without oversimplifying the technical reality?
02
Sharpen your knowledge
Topics to prepare
AI Assurance and Risk Frameworks
Review industry-standard frameworks for AI governance, such as NIST AI RMF or ISO/IEC 42001. Exercise: Draft a risk assessment template for an AI model that identifies potential failure modes like bias, hallucination, or data leakage, and map these to specific mitigation controls. This is relevant to the role's responsibility for defining assurance standards and supporting accountable operations.
Data Governance and Provenance
Study the lifecycle of data in AI systems, focusing on lineage, consent, and licensing. Exercise: Create a mock data register for a hypothetical AI project, documenting source ownership, usage restrictions, and retention policies. This directly supports the requirement to maintain registers for datasets and models.
Skills to revise
Evidence-Based Technical Writing
Practice translating complex policy requirements into clear, actionable technical specifications. Exercise: Write a one-page document outlining a test plan for 'tenant-isolation' or 'access-control' in a multi-tenant AI environment. This is essential for the role's core task of translating policy into technical controls for engineering teams.
AI Output Failure Analysis
Develop a systematic approach to auditing AI model outputs for accuracy and reliability. Exercise: Select a public AI model, generate responses to complex queries, and document specific instances of 'unsupported claims' or 'inaccurate citations' with a structured critique. This aligns with the requirement to identify and explain AI output failures.
SQL and Logging Analysis
Gain proficiency in querying audit logs and database tables to verify system behavior. Exercise: Write a SQL query to extract specific audit events from a log table to verify that a user's access permissions were correctly enforced during a model inference task. This is highly relevant for the Engineering variant of the role and useful for the Governance role.
04
Your preparation checklist
Prepare a portfolio of evidence-based risk assessments
Compile examples of past work where you translated complex policy or regulatory requirements into actionable technical controls. Focus on demonstrating your ability to document data provenance, model registers, or audit trails in a structured, clear manner.
Develop a framework for identifying AI output failures
Create a brief case study or presentation explaining how you would detect and document specific AI risks such as unsupported claims, bias, or inaccurate citations. Show how you would design a test to validate these issues and track remediation.
Refine your technical proficiency for the Engineering variant
If applying for the Engineering variant, prepare to demonstrate hands-on experience with SQL and Python for implementing security controls, such as tenant-isolation tests, data masking, or logging mechanisms. Be ready to discuss how you verify these controls in a production environment.
Formulate questions on the governance-engineering interface
Prepare thoughtful questions for the interviewers about how the Governance & Trust team collaborates with software engineers to balance delivery speed with rigorous assurance standards. Ask how they handle independent risk reporting when faced with project deadlines.
Availability last checked: 18 Oct 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy
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