Practical AI Audit Bootcamp
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Artificial Intelligence is rapidly being embedded across enterprise systems, from decision-making engines to customer-facing applications, making governance and audit readiness increasingly critical. As adoption accelerates, organizations face growing challenges in understanding how to govern, audit, and control AI systems effectively.
This Practical AI Audit Bootcamp bridges that gap by building a strong foundation in AI systems, governance principles, and audit methodologies. It focuses on how AI operates within enterprises and how auditors and risk professionals evaluate risks, controls, and compliance. Through structured frameworks and real-world case studies, the program builds practical, governance-driven AI audit capabilities for real-world application.
Krish
19+ Years of ExperienceKrish brings 19+ years of experience in cloud security, governance, and enterprise architecture, with deep expertise in Cloud GRC, security assessments, and secure cloud adoption. He has worked extensively on designing and securing enterprise cloud environments across AWS, Azure, and GCP, supporting large-scale migrations, compliance initiatives, and risk management programs.
His specializations include:
- Cloud security architecture and enterprise cloud governance
- Cloud GRC, risk assessments, and compliance alignment
- AI governance, responsible AI, and AI risk management
- Cloud audits, misconfiguration analysis, and control validation
- Secure cloud migration and hybrid/multi-cloud environments
Understanding AI Before You Audit It
- Fundamentals of Artificial Intelligence, Machine Learning, Generative AI, and LLMs
- How enterprise AI systems work
- AI vs Traditional IT Systems
- Why AI creates new audit challenges
- Common AI risks: Bias, Hallucination, Privacy, Security, Compliance
- Real-world AI failures and audit lessons learned
Why AI Governance Matters
- What is AI Governance
- Why organizations need AI Governance
- Responsible AI and Trustworthy AI
- Fairness, Accountability, Transparency, Explainability
- Governance, ownership, and accountability models
- Roles and responsibilities across the AI lifecycle
Understanding AI Architecture for Auditors
- AI lifecycle from data to decision
- Data Layer
- Model Layer
- Deployment Layer
- Monitoring Layer
- Governance checkpoints across the AI lifecycle
- Identifying audit control points
- Unified Control Framework (UCF) concept
Audit Readiness for AI Systems
- Where auditors should start
- Identifying key stakeholders
- Key documents to request before the audit
- Evidence collection strategy
- AI risk-based audit planning
- Scoping AI audits effectively
Core AI Audit Domains
- Data Governance Audit
- Model Governance Audit
- AI Risk Management Review
- Privacy and Regulatory Compliance Audit
- AI Security Control Review
- Logging, Monitoring, and Incident Response
- Third-Party AI Vendor Risk Management
- AI Policy and Governance Committee Review
Practical AI Audit Checklist
- Step-by-step AI audit checklist
- Control validation methodology
- Sample audit evidence review
- Red flags auditors should never miss
- High-risk findings examples
- Severity classification of findings
- Executive reporting approach
Model Validation and Responsible AI Audit
- Bias and Fairness Testing
- Explainability and Interpretability Review
- Model Drift and Performance Degradation
- Groundedness and Hallucination Risks
- Prompt Engineering Risks
- Human Oversight and Decision Accountability
- Validation controls that auditors should verif
End-to-End AI Audit Case Study
- Real Enterprise Simulation
- Case Study: Auditing an AI-Powered Customer Support Chatbot
Building Your Internal AI Audit Framework
- Creating your own AI audit methodology
- Building an AI control library
- AI audit reporting templates
- AI audit maturity model
- Continuous AI assurance approach
- Preparing for future regulatory audits
*Note: Participants will have access to session recordings for a period of 60 days.
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