Role-Based Programme
RB0383

Ollama for Risk & Internal Audit

Private AI for Evidence Review, Risk Analysis & Audit Support

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Duration
4 Hours
Level
Awareness
Delivery
Instructor-Led
Format
Awareness Session

Programme Objectives

  • Understand how Ollama can support private/local AI-assisted risk, control, audit, and evidence-review activities.
  • Explore practical approaches for summarising policies, procedures, audit evidence, risk information, and remediation records.
  • Experience AI-assisted identification of themes, exceptions, information gaps, and follow-up questions from supplied evidence.
  • Discover how structured outputs and retrieval techniques can support consistent risk and audit workflows.
  • Recognise requirements for confidentiality, evidence traceability, model validation, professional judgement, and responsible AI use.

Tools covered

OllamaOllama Model LibraryLocal ModelsOllama CLIOllama APIStructured OutputsEmbeddingsTool Calling

Who should attend

  • Risk Managers
  • Internal Audit Managers
  • Internal Auditors
  • Risk Analysts
  • Audit Analysts
  • Enterprise Risk Management Professionals
  • Governance, Risk & Control Professionals
  • Operational Risk Professionals
  • Internal Control Professionals
  • Controls Assurance Professionals
  • Audit Coordinators
  • Risk & Internal Audit Team Leads

Prerequisites & Participant Readiness

  • Basic understanding of risk-management or internal-audit processes
  • Familiarity with risks, controls, policies, audit evidence, findings, or remediation activities
  • Basic familiarity with generative AI is helpful but not mandatory
  • No programming expertise required
  • No previous Ollama experience required
  • Appropriate organisational approval should be obtained before using confidential, regulated, privileged, or personal information

TOC Modules

Concepts
  • Understanding Ollama and the concept of running AI models in controlled/local environments
  • Exploring model selection based on task, capability, infrastructure, and governance requirements
  • Mapping risk and audit activities across Review → Analyse → Validate → Document → Report
  • Distinguishing AI-generated observations from verified audit evidence and professional conclusions
Practical activities
  • Exploring Ollama and running a sample risk-management prompt
  • Comparing outputs from selected models on the same audit question
  • Identifying suitable and unsuitable risk/audit use cases

Scenarios

Control Evidence to Preliminary Audit Observation

Control Requirement → Policy / Procedure → Evidence Input → Ollama Analysis → Potential Gap / Exception → Supporting Evidence Review → Auditor Validation → Preliminary Observation

Participants use Ollama to organise and analyse control information while retaining evidence evaluation, materiality assessment, and audit judgement with qualified professionals.

Risk Register to Management Review Summary

Risk Records → Structured AI Analysis → Risk Themes → Control / Action Mapping → Potential Exceptions → Human Validation → Management Review Summary

Participants convert unstructured risk information into a consistent review format that highlights themes and follow-up areas without delegating final risk assessment or management decisions to AI.

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