Role-Based Programme
RB0929

Hugging Face for Risk & Internal Audit

Review AI Models, Data & Controls with Confidence

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Duration
8 Hours
Level
Basic
Delivery
Instructor-Led
Format
Workshop

Programme Objectives

  • Build foundational proficiency in using Hugging Face to review AI models, datasets, repositories, and documentation from a risk and audit perspective.
  • Assess model purpose, limitations, training information, evaluation evidence, licensing, and dataset characteristics.
  • Identify security, access, governance, and deployment risks associated with third-party and internally managed AI assets.
  • Apply structured review techniques to gated resources, repository security indicators, permissions, and audit trails.
  • Create repeatable AI due-diligence and audit-review workflows supported by documented evidence and human judgement.

Tools covered

Hugging Face HubModel CardsDataset CardsRepository MetadataLicensesGated Models & DatasetsSecurity ScanningAccess ControlsAudit LogsInference Endpoints

Who should attend

  • Internal Auditors
  • IT Auditors
  • Technology Risk Professionals
  • AI Risk & Governance Professionals
  • Enterprise Risk Professionals
  • Information Security Risk Professionals
  • Compliance Professionals
  • Internal Controls Professionals
  • Risk Assurance Professionals
  • Model Risk Professionals
  • Third-Party Risk Professionals
  • Audit Managers

Prerequisites & Participant Readiness

  • Basic understanding of risk, audit, controls, or compliance
  • Basic awareness of Artificial Intelligence and Machine Learning is helpful
  • Familiarity with technology or information-security controls is beneficial
  • No model-development or advanced programming experience required
  • No previous Hugging Face experience required

TOC Modules

Concepts
  • Understanding the Hugging Face Hub and its models, datasets, repositories, and AI assets
  • Understanding public, private, and organisation-managed resources
  • Identifying risk areas across model sourcing, data, security, access, and deployment
  • Understanding the AI Asset → Evidence → Risk → Control → Review lifecycle
Practical activities
  • Exploring Hugging Face from an auditor's perspective
  • Locating model, dataset, repository, ownership, and documentation information
  • Creating a basic AI asset review checklist

Scenarios

Third-Party AI Model Due Diligence

Proposed Model → Model Card → Dataset Review → License → Security Indicators → Access Requirements → Risk Assessment → Approval Recommendation

Participants perform a structured review of a Hugging Face model proposed for business use and prepare an evidence-backed recommendation covering documentation, data, licensing, security, and governance risks.

AI Repository & Access Control Review

AI Environment → Repositories → Users & Permissions → Gated Assets → Security Indicators → Audit Logs → Findings → Remediation Actions

Participants review an organisation's Hugging Face environment to identify access-control, repository-security, and governance gaps and prepare structured audit observations.

## Current Capability Reference

Hugging Face Model Cards can document intended uses, limitations, biases, training datasets, and evaluation results, while Dataset Cards provide context such as license, language, size, and responsible-use information. The Hub also supports gated assets, access controls, malware/pickle/secret scanning, and organisation Audit Logs.

Inference Endpoints include configurable authentication and network-access options, including private connectivity patterns, making deployment configuration relevant to technology-risk and audit reviews.

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