Learn how AI governance, AI risk management, privacy, ethics and controls come together — and how to evaluate them using a practical audit mindset.
Live • Part-time • Practical • Workpaper-driven
Organizations need people who can ask what can go wrong, who owns the risk and what evidence proves the controls work.
AI is increasingly embedded in business processes, creating new governance and control questions.
Bias, hallucination, model drift, security, privacy exposure and over-reliance require structured risk management.
Policies, ownership, evidence, risk registers, monitoring and defensible governance processes matter.
These three layers work together — and understanding the difference is foundational to an AI governance audit.
Enterprise-level policies, strategy, roles, structures and guardrails directing how AI is adopted and controlled.
Identifying, assessing and monitoring risks specific to AI systems throughout their life cycle.
Controls ensuring AI data and decisions respect privacy, fairness, transparency and ethical principles.
AI types, life cycle, strategy, governance roles, policies, awareness and metrics.
Bias, hallucination, model drift, security and over-reliance risks; assessment and monitoring.
Quality, classification, lineage, ownership, privacy principles, DPIAs and anonymization.
ISO/IEC 42001, NIST AI RMF, EU AI Act, India's evolving AI governance landscape and responsible AI.
Build an RCM, perform a mock audit, write observations and prepare remediation plans.
Practice explaining governance, risk, controls, evidence and audit findings.
The supplied brochure's five-module structure is preserved; the current landing-page plan can deliver it as a live, part-time 40–60 day cohort.
AI types, life cycle, strategy, roles, policies, awareness and metrics.
Risk identification, likelihood, impact, prioritization, monitoring and incidents.
Quality, classification, lineage, privacy principles, DPIAs and de-identification.
ISO/IEC 42001, NIST AI RMF, EU AI Act, Indian landscape and responsible AI.
Mock organization, RCM, observations, deficiencies, remediation and interview prep.
Identify AI systems, structures, data flows, risks and standards.
Review governance charters, policies, workflows, pipelines and risk registers.
Test design and operating effectiveness of governance, risk and privacy processes.
Review minutes, policies, approvals, risk registers, DPIAs and monitoring evidence.
Trace gaps to policy, ownership, model, dataset or monitoring weaknesses.
Write clear deficiencies and appropriate remediation actions.
Co-founder and lead trainer with IT audit, SAP/GRC controls and IAM background. Her ISO/IEC 42001 Lead Auditor credential anchors the AI governance audit approach.
Co-founder and trainer with SAP/Oracle business-process, control design and risk-assessment expertise.
The brochure positions training around real audit workpapers, RCMs, evidence review, deficiency write-ups and practical judgment.
Commerce, Finance and IT graduates targeting IT Audit or Internal Audit roles.
Professionals expanding from traditional audit and risk into AI governance.
Functional consultants moving toward GRC or controls advisory.
Articleship students and finance professionals interested in governance skills.
Risk Advisory, SOX, Data Privacy and Internal Controls professionals.
Those preparing for CISA, ISACA AAIA or building groundwork toward ISO/IEC 42001 Lead Auditor certification.
Start on WhatsApp, share your details and preferred session, then complete the live Zoom registration.
Tap the button and send the pre-filled message to InfoMynt.
Send your name, email, current role and preferred demo session.
Complete the Zoom registration to reserve your live demo seat.
The supplied brochure states that the program does not require prior AI or data science background.
No. The curriculum includes workpaper-driven exercises, RCM preparation, evidence review, governance deficiency write-ups and a mock AI governance audit.
ISO/IEC 42001, NIST AI RMF, the EU AI Act and India's evolving AI governance landscape.
Live, part-time delivery is planned for 40–60 days, using the five-module curriculum from the supplied brochure.
Build practical capability across AI risk, privacy, ethics, standards and audit methodology.