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AI Governance Skills Employers Name

What skills do AI governance jobs ask for?

57 competencies, in 10 domains, each named by real postings. Every page says what the skill means in practice, which roles ask for it and how deeply, which backgrounds already carry it, and how many graded topics teach it. Pick one, or check a whole role at once.

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AI Literacy

  • Working AI fluency

    Uses generative AI tools daily, knows what a model can and cannot do, and can say where an output should not be trusted.

    12 roles name it, 21 graded topics teach it

  • Model failure modes and bias recognition

    Recognizes hallucination, drift, skew, brittleness and biased outcomes, and knows how each one enters a system.

    9 roles name it, 10 graded topics teach it

  • Responsible and disclosed use of AI at work

    Applies acceptable-use rules, discloses AI assistance where it matters, and keeps confidential material out of unapproved tools.

    3 roles name it, 21 graded topics teach it

  • Prompting and workflow design with AI

    Briefs a model like a good manager: context, constraints, examples, verification, and knows when to stop delegating.

    2 roles name it, 19 graded topics teach it

  • Judgment when AI supports a decision

    Knows when to trust, verify, escalate or override an AI recommendation, and stays accountable for the decision.

    2 roles name it, 14 graded topics teach it

Governance and Oversight

  • AI governance operating model design

    Designs decision rights, committees, intake, approval tiers and escalation so routine uses move and consequential uses get reviewed.

    10 roles name it, 20 graded topics teach it

  • AI inventory and use-case intake

    Finds every AI system in use, records owner, purpose, data and risk tier, and keeps the record alive as tools change.

    15 roles name it, 13 graded topics teach it

  • AI policy and standards writing

    Writes policies with scope, responsibilities, requirements, exceptions and evidence, so people can follow them and auditors can test them.

    11 roles name it, 10 graded topics teach it

  • Human oversight design

    Defines who reviews AI outputs, what they check, when they can override, and how to keep review from becoming a rubber stamp.

    8 roles name it, 9 graded topics teach it

  • Governance metrics and program measurement

    Measures whether governance works: inventory coverage, owners named, overdue reviews, incidents, approval times, not how busy the committee is.

    3 roles name it, 16 graded topics teach it

  • Agentic AI controls and authorization boundaries

    Governs AI agents that take actions: tool access, least privilege, interruptibility, cascading actions and accountability for what an agent did.

    7 roles name it, 21 graded topics teach it

Risk and Assurance

  • AI risk and impact assessment

    Reviews purpose, data, affected people, accuracy, bias, security, oversight, vendors and law for a use case, scores likelihood and impact, and documents residual risk.

    20 roles name it, 14 graded topics teach it

  • AI risk register and treatment tracking

    Keeps the living record: each risk with a named owner, rating, treatment, residual risk, monitoring metric, threshold and review date.

    10 roles name it, 12 graded topics teach it

  • Control design and operating-effectiveness testing

    Maps risks to preventive, detective and corrective controls, then tests design and operation with samples, evidence and defensible findings.

    12 roles name it, 12 graded topics teach it

  • Evidence collection and audit-ready documentation

    Collects, labels and preserves the evidence that a control operated, a decision was made, and a claim can be defended to an auditor or regulator.

    19 roles name it, 20 graded topics teach it

  • AI audit and independent assurance

    Scopes an AI audit, sets criteria, samples, interviews, tests, writes findings with condition, criteria, cause, effect and recommendation, and tracks remediation.

    2 roles name it, 6 graded topics teach it

  • Model risk management and independent challenge

    Classifies models by tier, sets validation requirements, challenges data, methodology and performance evidence, and reports aggregate exposure.

    5 roles name it, 14 graded topics teach it

  • Post-deployment monitoring and drift detection

    Sets performance metrics, thresholds and review triggers after launch, and treats a model change, a vendor update or new data as a reason to re-check.

    11 roles name it, 12 graded topics teach it

  • AI vendor due diligence and third-party risk

    Tiers vendors by use and impact, requests evidence instead of promises, tests in the customer's context, and plans monitoring and exit.

    24 roles name it, 5 graded topics teach it

  • Contract terms that allocate AI risk

    Turns controls into enforceable obligations: data use, change notice, audit rights, incident duties, subcontractors, IP, exit and deletion.

    4 roles name it, 4 graded topics teach it

  • AI incident response and recovery

    Classifies AI incidents by severity, runs containment, preserves evidence, manages notification, and closes the loop with lessons learned.

    10 roles name it, 3 graded topics teach it

Regulation and Policy

  • EU AI Act obligations and timelines

    Classifies a system by role and risk tier, knows which obligations bind on which date after the Digital Omnibus, and what evidence conformity needs.

    16 roles name it, 24 graded topics teach it

  • NIST AI RMF in practice

    Runs GOVERN, MAP, MEASURE and MANAGE as a cycle with evidence, builds current and target profiles, and applies the generative AI profile.

    15 roles name it, 3 graded topics teach it

  • ISO/IEC 42001 management systems

    Builds and audits an AI management system: context, leadership, planning, support, operation, performance evaluation, improvement and the Annex A controls.

    11 roles name it, 3 graded topics teach it

  • Framework crosswalking without false equivalence

    Compares the EU AI Act, NIST AI RMF, ISO/IEC 42001 and sector rules by intent and control objective, and says where they do not overlap.

    6 roles name it, 5 graded topics teach it

  • US federal and state AI regulation

    Tracks executive orders, OMB guidance, agency rules and the state patchwork, and knows which state laws reach hiring, insurance and consumer decisions.

    10 roles name it, 17 graded topics teach it

  • Regulatory change management

    Spots a regulatory change, decides applicability, assigns actions, updates controls and keeps the implementation evidence.

    11 roles name it, 7 graded topics teach it

  • Policy analysis and decision-ready writing

    Reads bills and rules like a practitioner, compares options with costs and unintended effects, and writes briefs a decision-maker can act on.

    1 role names it, 23 graded topics teach it

  • Rulemaking, comments and legislative process

    Knows how a rule becomes a rule, drafts comments for the record that get read, and prepares testimony and briefings for hearings.

    1 role names it, 6 graded topics teach it

  • Coalition building and stakeholder mapping

    Convenes partners across industry, civil society and government, keeps a stakeholder map current, and gets many parties to one position.

    1 role names it, 6 graded topics teach it

  • Singapore and APAC AI governance frameworks

    Applies IMDA's Model AI Governance Framework, AI Verify and the PDPA alongside Western frameworks for organizations operating across Asia.

    0 roles name it, 8 graded topics teach it

Data and Privacy

  • Data governance operating model and stewardship

    Establishes ownership, stewardship, councils, decision rights and issue workflows so data has a named owner and a defined meaning.

    2 roles name it, 22 graded topics teach it

  • Data lineage and provenance

    Traces where data came from, what transformed it, who owns each hop and where it flows downstream, so a number can be defended.

    6 roles name it, 6 graded topics teach it

  • Data quality rules and monitoring

    Profiles data, sets rules and thresholds, monitors, assigns issues to owners, and knows when data is not fit to train or run a model.

    3 roles name it, 15 graded topics teach it

  • Data classification, access and retention

    Classifies information, applies least privilege, sets retention and acceptable-use rules, and controls what may enter a prompt, a log or an embedding.

    10 roles name it, 19 graded topics teach it

  • Privacy law applied to AI

    Applies GDPR, CCPA and sector rules to training data, inference, automated decisions, lawful basis, individual rights and cross-border transfer.

    15 roles name it, 7 graded topics teach it

  • Privacy by design and privacy engineering

    Builds minimization, purpose limitation, de-identification, consent and retention into an AI system before launch, with tests that prove it.

    3 roles name it, 5 graded topics teach it

Security and Resilience

  • AI security fundamentals

    Understands prompt injection, data poisoning, model theft, insecure integrations and excessive agent privileges, and the controls that reduce each.

    13 roles name it, 17 graded topics teach it

  • Shadow AI and data leakage control

    Finds unapproved AI use, sets which tools are approved and what may be pasted, and detects leakage without policing every keystroke.

    6 roles name it, 12 graded topics teach it

  • AI resilience, continuity and exit planning

    Plans for a vendor failure, an unsafe model change or a suspended service: rollback, manual fallback, data export and safe decommissioning.

    5 roles name it, 3 graded topics teach it

Technical Evaluation

  • How models work, at a governance depth

    Explains training, tokens, context windows, embeddings, retrieval and fine-tuning well enough to ask an engineer a precise question and spot weak evidence.

    15 roles name it, 18 graded topics teach it

  • AI evaluation and testing design

    Designs tests for factuality, robustness, fairness, safety and abuse resistance with rubrics, baselines and thresholds, and says what a score misses.

    8 roles name it, 12 graded topics teach it

  • Explainability, transparency and contestability

    Decides what a person affected by an AI decision must be told, how an output can be explained, and how they can challenge it.

    9 roles name it, 11 graded topics teach it

Business and Transformation

  • AI strategy and opportunity triage

    Decides where AI should be used, where it should not, and which use cases create measurable value, tied to the organization's priorities.

    3 roles name it, 18 graded topics teach it

  • Adoption and change management

    Knows why rollouts stall, separates a skills problem from a trust problem, builds champion networks, and measures adoption honestly.

    9 roles name it, 10 graded topics teach it

  • ROI and value measurement for AI

    Builds an honest value model: baseline, measured change, cost, risk, and the projects that should be stopped.

    6 roles name it, 11 graded topics teach it

  • Governed AI program and portfolio delivery

    Runs stage gates, intake, decision logs and evidence repositories so a pilot cannot reach production without the required approvals.

    3 roles name it, 9 graded topics teach it

  • Executive and board communication on AI risk

    Turns technical uncertainty into a one-page decision: material risks, trends, exceptions, remediation, and what the board is being asked to accept.

    18 roles name it, 10 graded topics teach it

  • Cross-functional facilitation and influence

    Interviews, facilitates, challenges and secures action across legal, security, product and business teams without owning every decision.

    24 roles name it, 20 graded topics teach it

  • Buying AI well

    Writes requirements, runs a fair evaluation, pilots within limits, and refuses a demo as evidence.

    5 roles name it, 15 graded topics teach it

People and Workforce

  • AI literacy training and enablement design

    Designs role-based AI training that measures skill, not attendance, with approved-use guidance, office hours and communities of practice.

    6 roles name it, 20 graded topics teach it

  • AI in hiring and employment decisions

    Knows the rules on automated employment decisions, bias audits and notices, and treats hiring AI as the highest-risk use case it is.

    3 roles name it, 4 graded topics teach it

  • Workforce transition and role redesign

    Redesigns roles as AI absorbs tasks, plans reskilling instead of replacement, and supports managers in honest conversations about how work changes.

    2 roles name it, 17 graded topics teach it

  • Leading a team that works with AI

    Sets expectations for AI use on a team, reviews AI-assisted work, delegates to agents deliberately and keeps accountability with people.

    4 roles name it, 19 graded topics teach it

Public Protection

  • Deepfake, voice-clone and scam recognition

    Recognizes AI-enabled fraud, from cloned voices and synthetic video to romance and investment scams, and knows the one move that breaks the script.

    1 role names it, 8 graded topics teach it

  • Protecting vulnerable people from AI harm

    Designs safeguards, family protocols and reporting paths for older adults, minors and people who cannot challenge an automated outcome.

    1 role names it, 8 graded topics teach it

  • Ethical reasoning turned into decision criteria

    Identifies value conflicts in an AI use, asks who benefits and who bears risk, and turns principles into criteria a review can apply.

    5 roles name it, 7 graded topics teach it

Questions

What skills do AI governance jobs ask for?
Postings name a closed set of competencies across AI literacy, governance and oversight, risk and assurance, regulation and policy, data and privacy, security, technical evaluation, business transformation, workforce and public protection. Each has its own page here with the roles that name it and where it is taught.
Do I need to code to work in AI governance?
Rarely. Most of the competencies below are judgment, evidence and communication skills applied to AI systems. The technical ones ask for enough fluency to read an evaluation result and ask a precise question, not to build a model.
How do I prove a skill I already have?
Pass the graded topics that teach it: each is graded by explaining it back against the lesson's own transcript. The readiness check counts what you can prove, then shows what remains.