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AI Audit - Certification on Audit of Digital Systems (CADS) Foundational level

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Overview

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This course equips audit participants to audit data and AI-driven systems effectively. It further focuses on understanding AI systems, identifying risks and biases, using AI tools to enhance audit efficiency, and developing structured approaches to verify, validate, and trace AI outputs.

Intended audience

Public auditors under CAG and anyone interested in Public Audit Domain

Prerequisites

  • Public Audit (Foundation) is a mandatory course for all CADS learners. Successful completion of Public Audit (Foundation) is a prerequisite for enrolment in the AI Audit domain, as it is for all other CADS technical domains. Learners are advised to complete Public Audit (Foundation) first before proceeding to this domain.
  • All of: Public Audit - Certification on Audit of Digital Systems (CADS) Foundational level

Assessment & certification

  • Assessment fee: Included — no extra fee
  • Assessment mode: Center based
  • Assessment type: MCQ
  • Certificate provider: The passing percentage is 50% of the total marks.

NCrF level: 5.5 (NCrF credit-eligible)

Syllabus

  • Week 1: Module 0: Foundations (optional) – Python & Tools (3h)
    Tools Orange → ML workflows Tableau → visual analysis Python for auditors: o Load data o Inspect structure o Identify gaps & duplicates o Aggregate for audit insights
  • Week 2: Module 1: AI Foundations (3h)
    What is AI ? The Evolution of AI: From Traditional AI to Generative AI Types of AI Introduction to Transformers, LLMs, RAG, Vector Embeddings and other common terms Why AI has become widely usable now algorithms, cloud access, and consumer-friendly interfaces. How professional work is changing o Show how audit, accounting, reporting, compliance, and business review workflows are becoming more AI-assisted. o Use examples such as AI-assisted report review, document comparison, evidence summarization, and first-draft memo preparation. AI in government systems Opportunities: speed, scale, and support for repetitive work. Risks: Hallucination, Bias, Over-reliance, Lack of explainability
  • Week 3: Module 2: Basic Data Literacy for Audit (3h)
    Focus: Understanding data as audit evidence Topics: • Descriptive statistics as a way to understand business data • Probability as uncertainty, not certainty • Inferential thinking in practical work • Sampling and representativeness • Correlation versus causation • Data quality issues that matter for analytics and AI • Introduce class imbalance through examples such as fraud, duplicate payments, or policy violations, • Outliers in government data • Bias
  • Week 4: Module 3: Core AI Concepts for Auditing (3h)
    Topics: • Model, training, prediction • Types of AI tasks: o Classification o Regression o Clustering o Anomaly detection • Accuracy vs Precision vs Recall • Model risks: o Overfitting o Drift o Missing features • Rule based systems vs learning-based systems
  • Week 5: Module 4: Foundations for Auditing AI Systems (3h)
    Focus: Understanding how AI systems work Topics: • AI audit vs IT audit • Backward traceability: o Output → Data → Source • AI as audit evidence • Documentation framework: Tool used, Data used, Output generated, Verification done, Documentation maintained
  • Week 6: Module 5: Prompt Engineering for Audit (4h)
    Focus: Using AI effectively in audit work Topics: • Good vs bad prompts • Structured prompting: o Role o Task o Context o Constraints o Format o Evidence • Error types: o Hallucination o Omission o Overconfidence
  • Week 7: Module 6: AI in Daily Audit Work (3h)
    Focus: Practical productivity use-cases Topics: Starting with document-heavy work Comparing versions and identifying changes Extracting information from unstructured documents Converting unstructured information into structured outputs Drafting first-cut professional outputs Reconciling and cross-checking information
  • Week 8: Module 7: Basics of AI Analytics & Image-Based Verification (3h)
    Focus: Detecting patterns and anomalies Topics: • Anomaly detection (financial data) • GIS demo
  • Week 9: Module 8: Evaluating AI Outputs (3h)
    Focus: Ensuring AI outputs are trustworthy Topics: • Error types: o Hallucination o Omission o Overconfidence • Audit checklist: o Source verification o Missing info o Assumptions o Context consistency
  • Week 10: Module 9: Responsible AI & Governance (2h)
    Focus: Legal and ethical compliance Topics: • Responsible AI principles (NITI Aayog) • DPDP Act implications • AI governance controls • Global frameworks

Taught by

Experts from IIT Madras, IITM Pravartak Technologies Foundation and Office of CAG designated experts

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