Role-Based Programme
RB0930

Hugging Face for Risk & Internal Audit

Assess AI Models, Data, Controls & Governance

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Duration
16 Hours
Level
Intermediate
Delivery
Instructor-Led
Format
Capability Training

Programme Objectives

  • Develop functional proficiency in using Hugging Face to review AI models, datasets, repositories, and deployment information from a risk and audit perspective.
  • Evaluate model provenance, intended use, limitations, licences, datasets, security indicators, and supporting documentation.
  • Assess access governance, repository controls, authentication, deployment configurations, and evidence required for AI-control reviews.
  • Build reusable workflows for third-party AI due diligence, model-risk reviews, repository audits, and AI governance assessments.
  • Apply professional judgement to distinguish automated indicators from validated audit findings and risk conclusions.

Tools covered

Hugging Face HubModel CardsDataset CardsModel & Dataset RepositoriesEvaluation ResultsLicencesGated Models & DatasetsSecurity ScanningOrganizationsAccess ControlsResource GroupsAudit LogsInference Endpoints

Who should attend

  • Risk Managers
  • Internal Auditors
  • IT Auditors
  • Technology Risk Professionals
  • AI Risk & Governance Professionals
  • Model Risk Professionals
  • Information Security Auditors
  • Governance, Risk & Compliance Professionals
  • Operational Risk Professionals
  • Third-Party Risk Professionals
  • Compliance & Assurance Professionals
  • Technology Governance Teams
  • Internal Control Professionals

Prerequisites & Participant Readiness

  • Basic understanding of risk, audit, controls, or governance
  • Familiarity with technology or information-security risks is helpful
  • Basic awareness of AI and machine-learning concepts
  • Familiarity with control testing and evidence review is recommended
  • No advanced programming or data-science expertise required
  • No previous Hugging Face experience required

TOC Modules

Concepts
  • Understanding the Hugging Face ecosystem: models, datasets, repositories, Spaces, and deployment services
  • Understanding how AI assets move from development and sharing to deployment and business use
  • Identifying risk domains including model, data, security, licensing, access, and operational risks
  • Mapping Hugging Face artefacts to audit objectives and control evidence
Practical activities
  • Exploring representative model, dataset, and repository pages
  • Identifying audit-relevant information available within each artefact
  • Creating an initial AI Asset Risk Review checklist

Scenarios

Third-Party AI Model Due Diligence

Business Use Case → Hugging Face Model → Model Card → Dataset & Licence Review → Evaluation Evidence → Security Indicators → Risk Assessment → Approval Recommendation

Participants assess a proposed third-party Hugging Face model before enterprise adoption, identifying documentation, data, licensing, performance, security, and governance risks requiring remediation or approval.

Internal Hugging Face Repository & Access Audit

Organization → Models / Datasets → Users & Roles → Tokens → Access Controls → Audit Evidence → Exceptions → Corrective Actions

Participants conduct a structured review of an organization's Hugging Face environment, test repository and access controls, identify potential governance weaknesses, and produce evidence-backed audit observations and corrective-action recommendations.

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