Can you move from blank page to operational compliance? They want systematic thinking: inventory, framework, controls, monitoring, and continuous improvement.

1. AI system inventory — identify all AI systems, their purposes, data inputs, and decision impacts. 2. Risk classification — tier systems under EU AI Act (high/limited/minimal) and NIST AI RMF categories. 3. Controls framework — map governance controls to regulatory requirements (EU AI Act, NIST AI RMF, ISO 42001). 4. GRC platform configuration — set up OneTrust, ServiceNow, or Credo AI for automated compliance tracking. 5. Continuous monitoring — define KRIs, establish audit schedules, and build reporting dashboards for leadership.

This tests regulatory depth. EU AI Act compliance is the primary demand driver. They want someone who understands Article 43 requirements in operational detail.

Article 43 requires demonstrating compliance across seven dimensions: data governance (training data quality, representativeness, documentation), technical documentation (system design, purpose, limitations), transparency (user-facing disclosures), human oversight (override capabilities, monitoring procedures), accuracy (performance benchmarks across populations), robustness (reliability under adversarial conditions), and cybersecurity (security controls for the AI system). Build an evidence package for each dimension. Internal assessment is the default; some categories require third-party notified body assessment.

Over 1,200 AI regulations exist worldwide. They want evidence you can operationalize multi-jurisdictional compliance without building a separate program for each regulation.

Build a controls harmonization matrix: map all applicable regulations (EU AI Act, GDPR, CCPA, FDA, SEC/EEOC guidance, state-level laws) against a single controls framework. Use NIST AI RMF as the integrating framework — its categories map to most regulatory requirements. For each control, document which regulations it satisfies. This lets you implement once, demonstrate compliance to many. Track jurisdiction-specific requirements in a GRC platform with automated regulatory update feeds. Report compliance posture per jurisdiction on dashboards.

Vendor AI is everywhere. They want someone who can evaluate whether third-party models meet compliance standards before they enter production.

Build a vendor AI assessment framework: 1. Pre-procurement due diligence — evaluate vendor’s own governance, data practices, and regulatory compliance posture. 2. Contractual requirements — audit rights, data handling obligations, incident notification, liability allocation. 3. Technical assessment — bias testing, explainability capabilities, security controls. 4. Ongoing monitoring — model performance tracking, compliance attestation requirements, change notification processes. 5. Exit planning — data portability, model replacement procedures, transition timelines.

GRC platform proficiency is a core skill. They want someone who can operationalize compliance at scale, not just write policies.

Demonstrate familiarity with established GRC platforms (ServiceNow GRC, OneTrust, RSA Archer) and purpose-built AI governance tools (Credo AI, Holistic AI, Microsoft Purview). Key AI-specific configurations: risk classification workflows (EU AI Act tiering), conformity assessment checklists, model inventory management, audit trail automation (evidence collection for ISO 42001), and KRI dashboards (fairness drift, compliance gap closure, assessment completion). The differentiator is configuring these for AI-specific governance, not general IT GRC.