Role-Based Programme
RB0384

Ollama for Risk & Internal Audit

Local AI for Risk Analysis, Audit Evidence & Control Review

IMAGE REQUIRED
Duration
8 Hours
Level
Basic
Delivery
Instructor-Led
Format
Workshop

Programme Objectives

  • Build practical capability in using Ollama-hosted local AI models for risk analysis, audit preparation, control review, evidence synthesis, and reporting.
  • Explore privacy-conscious workflows for working with internal policies, audit documentation, risk registers, procedures, and control information.
  • Apply structured prompting and outputs to risk identification, control mapping, audit evidence analysis, findings development, and remediation tracking.
  • Understand how locally hosted models, embeddings, and retrieval can support controlled risk and audit knowledge workflows.
  • Apply confidentiality, evidence validation, audit independence, model evaluation, access controls, and responsible AI practices.

Tools covered

OllamaLocal LLM RuntimeModel LibraryLocal Model ManagementPrompt TemplatesStructured OutputsEmbeddingsLocal Retrieval-Augmented Generation ConceptsFile & Document Processing ConceptsREST API ConceptsTool CallingLocal Knowledge Workflows & Model Evaluation

Who should attend

  • Internal Audit Managers
  • Internal Auditors
  • Risk Managers
  • Enterprise Risk Professionals
  • Operational Risk Professionals
  • Risk Analysts
  • Controls & Assurance Professionals
  • Governance Professionals
  • Business Control Professionals
  • Audit Analysts
  • Technology Risk Professionals
  • Risk & Internal Audit Team Leads

Prerequisites & Participant Readiness

  • Basic understanding of risk management, internal audit, or control-assurance activities
  • Familiarity with risk registers, policies, procedures, audit evidence, or control documentation
  • Basic knowledge of generative AI concepts is helpful
  • Basic awareness of local applications, APIs, or technical environments is beneficial
  • Awareness of confidentiality and audit-independence requirements
  • No advanced programming expertise required
  • No previous Ollama training required

TOC Modules

Concepts
  • Understanding Ollama as a local runtime for working with large language models
  • Understanding local versus cloud AI from privacy, confidentiality, control, and deployment perspectives
  • Identifying use cases across risk assessment, audit planning, evidence analysis, controls, and reporting
  • Understanding that local AI supports but does not replace professional audit or risk judgement
Practical activities
  • Exploring the Ollama environment and available local models
  • Running a model against a simple risk or audit task
  • Mapping Risk / Audit Activity → Local AI Capability → Human Review → Outcome

Scenarios

Risk Register to Control Review

Approved Risk Register → Ollama Local Model → Risk Statement Analysis → Policy & Control Retrieval → Control Mapping → Gap Identification → Risk Manager Review → Updated Risk Assessment

Participants use locally hosted AI to review risk and control information while ensuring final risk ratings, control assessments, and acceptance decisions remain under qualified human ownership.

Audit Evidence to Structured Finding

Audit Objective → Local Policies & Evidence → Ollama-Assisted Review → Evidence Gap → Structured Finding → Auditor Validation → Remediation Action → Follow-Up Tracker

Participants use Ollama to organize and analyze sensitive audit information in a local environment while keeping evidence sufficiency, root-cause confirmation, final findings, and audit conclusions under professional auditor judgement.

Related programmes

Continue with programmes from the same capability area.

Take the next step

Ready to make this programme work for your team?

Customise modules, duration and business scenarios for your team.

Instructor-ledVirtualHybrid

Designed around your roles, tools and real workflows.