Passing AAISM: A Deep Dive into Domain 1 AI Governance
Prabh Nair
20,677 views
Are you preparing for ISACA AAISM and confused about how to approach Domain 1: AI Governance and Program Management? In this master class, Prabh Nair explains AAISM Domain 1 in a practical, exam-focused, and governance-driven way.
AAISM is not a data science certification. It is not mainly about Python, neural networks, model coding, or training models from scratch. AAISM is about how security leaders govern, manage, secure, monitor, and respond to AI-related risks in an enterprise environment.
00:00 - 00:50 – Introduction
00:50 – 04:24 - What is AAISM & its Prerequisites
04:24 – 05:27 - AAISM Certification & Maintenance
05:27 – 06:41 - Domain and Exam
06:41 – 11:05 - AAISM Overview & Do Not Prepare Like a Technical AI Engineer
11:05 – 12:17 - AAISM is not a data science certification
12:17 – 13:07 - AI with existing Security Programs
13:07 – 14:16 - Where AAISM adds value beyond CISSP and CISM
14:16 – 17:00 - Start with Governance
17:00 – 18:08 - Think in AI lifecycle, not one-time deployment
18:08 – 19:38 - Separate “AI for security” and “Security for AI”
19:38 – 20:51 - Risk, Control and Assurance
20:51 – 22:10 - AAISM Formula
22:10 – 24:24 - Domain 1 (AI Governance and program management) – Introduction & Review
24:24 – 25:45 - What is AI Landscape & Foundation
27:37 – 28:28 - AI/ML Foundation and Need of AI Security Management
28:28 – 31:52 - AI Governance & Security Governance
31:25 – 35:01 - Where AI failed & AI Principles
35:01 – 35:56 – Introduction
35:56 – 37:11 - Explainability and Trust
37:11 – 39:45 - Enterprise Risk and Responsibilities
39:45 – 41:32 - Role-based and governance-focused
41:32 – 42:54 - Role of Enterprise Governing Body
42:54 – 47:19 - AI stakeholders & Engagement
47:19 – 49:45 - AI Charter & its Components
49:45 – 51:49 - AI Steering Committee and its members
51:49 – 53:30 - Role of the Enterprise in AI
53:30 – 54:52 - AI Standard and Framework
54:52 – 56:10 - How core AI governance frameworks work together
56:10 – 57:19 - Compliance with Laws and Regulations
57:19 – 59:06 - Regulatory Gaps and Governance
59:06 – 01:01:57 - Internal Enterprise Challenge & AI use cases
01:01:57 – 01:02:04 - Business Problems and Potential AI Solutions
01:02:04 – 01:03:00 - Why AI Projects Fail
01:03:00 – 01:04:30 - Good AI use cases and & Limitations of AI Use
01:04:30 – 01:05:27 - Governance Actions
01:05:27 – 01:07:06 - Business cases, Needs, Scope and Objective
01:07:06 – 01:10:00 - Cost -Benefit Analysis & Return on Investment
01:10:00 – 01:12:47 - Part B- AI Implementation and AI Strategies
01:12:47 – 01:14:44 - Value Alignment
01:14:44 – 01:18:20 - Buying AI Tools or Building AI Solutions
01:18:20 – 01:19:07 – Vendors
01:19:07 – 01:22:52 - AI Policy Development & Responsible AI
01:22:52 – 01:25:23 - Starting a RAI Program
01:25:23 – 01:27:17- AI Procedures and Manuals
01:27:17 – 01:31:43 - Ethical Consideration in AI
01:31:43 – 01:34:17 - Transparency and Explainability & Trust and Safety
01:34:17 – 01:37:42 - Intellectual Prosperity & Human Rights & Environmental Impact
01:37:42 – 01:39:03 - Part-C – AI Asset Identification and Inventory
01:39:03 – 01:40:47 - Data Inventory and Management
01:40:47 – 01:43:10 - Data Life Cycle & Data Lineage
01:43:10 – 01:44:36 - Data Classification & Data Collection for AI
01:44:36 – 01:48:12 - Data Collection and Data Collection Risk for AI
01:48:12 – 01:51:17 - Data Confidentiality, Quality & Balancing
01:51:17 – 01:53:14 - Model Cards
01:53:14 – 01:58:47 - Part D – AI Security Program Development
01:58:47 – 02:04:02 - Component AI Security Program
02:04:02 – 02:05:50 - AI use in Enterprise security and operations
02:05:50 – 02:07:09 - Part -E – Business Continuity and Incident Response
02:07:09 – 02:09:40 - BCP & Business Impact Analysis
02:09:40 – 02:13:38 - BIA Metrics & Incident Response
02:13:38 – 02:16:52 - Business Problem and Potential AI Solutions
02:16:52 – 02:22:14 - Incident Management Cycle
02:22:14 – 02:23:19 - AI Enabled Incident Response
02:23:19 – 02:23:54 - AI Powered Incident Response – Benefits & Challenges
02:23:54 – 02:24:54 - Best Practices for Incident Response Automation
In this session, we focus on the right AAISM mindset:
Do not think like a technical AI engineer. Think like an AI security governance leader.
Domain 1 focuses on how organizations should establish AI governance, define ownership, create policies, manage stakeholders, maintain AI asset and data inventories, build AI security programs, and prepare for AI-related incidents and business continuity scenarios.
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20 Comments
Passing AAISM: A Deep Dive into Domain 1 AI Governance
Prabh Nair
20,677 views
Are you preparing for ISACA AAISM and confused about how to approach Domain 1: AI Governance and Program Management? In this master class, Prabh Nair explains AAISM Domain 1 in a practical, exam-focused, and governance-driven way.
AAISM is not a data science certification. It is not mainly about Python, neural networks, model coding, or training models from scratch. AAISM is about how security leaders govern, manage, secure, monitor, and respond to AI-related risks in an enterprise environment.
00:00 - 00:50 – Introduction
00:50 – 04:24 - What is AAISM & its Prerequisites
04:24 – 05:27 - AAISM Certification & Maintenance
05:27 – 06:41 - Domain and Exam
06:41 – 11:05 - AAISM Overview & Do Not Prepare Like a Technical AI Engineer
11:05 – 12:17 - AAISM is not a data science certification
12:17 – 13:07 - AI with existing Security Programs
13:07 – 14:16 - Where AAISM adds value beyond CISSP and CISM
14:16 – 17:00 - Start with Governance
17:00 – 18:08 - Think in AI lifecycle, not one-time deployment
18:08 – 19:38 - Separate “AI for security” and “Security for AI”
19:38 – 20:51 - Risk, Control and Assurance
20:51 – 22:10 - AAISM Formula
22:10 – 24:24 - Domain 1 (AI Governance and program management) – Introduction & Review
24:24 – 25:45 - What is AI Landscape & Foundation
27:37 – 28:28 - AI/ML Foundation and Need of AI Security Management
28:28 – 31:52 - AI Governance & Security Governance
31:25 – 35:01 - Where AI failed & AI Principles
35:01 – 35:56 – Introduction
35:56 – 37:11 - Explainability and Trust
37:11 – 39:45 - Enterprise Risk and Responsibilities
39:45 – 41:32 - Role-based and governance-focused
41:32 – 42:54 - Role of Enterprise Governing Body
42:54 – 47:19 - AI stakeholders & Engagement
47:19 – 49:45 - AI Charter & its Components
49:45 – 51:49 - AI Steering Committee and its members
51:49 – 53:30 - Role of the Enterprise in AI
53:30 – 54:52 - AI Standard and Framework
54:52 – 56:10 - How core AI governance frameworks work together
56:10 – 57:19 - Compliance with Laws and Regulations
57:19 – 59:06 - Regulatory Gaps and Governance
59:06 – 01:01:57 - Internal Enterprise Challenge & AI use cases
01:01:57 – 01:02:04 - Business Problems and Potential AI Solutions
01:02:04 – 01:03:00 - Why AI Projects Fail
01:03:00 – 01:04:30 - Good AI use cases and & Limitations of AI Use
01:04:30 – 01:05:27 - Governance Actions
01:05:27 – 01:07:06 - Business cases, Needs, Scope and Objective
01:07:06 – 01:10:00 - Cost -Benefit Analysis & Return on Investment
01:10:00 – 01:12:47 - Part B- AI Implementation and AI Strategies
01:12:47 – 01:14:44 - Value Alignment
01:14:44 – 01:18:20 - Buying AI Tools or Building AI Solutions
01:18:20 – 01:19:07 – Vendors
01:19:07 – 01:22:52 - AI Policy Development & Responsible AI
01:22:52 – 01:25:23 - Starting a RAI Program
01:25:23 – 01:27:17- AI Procedures and Manuals
01:27:17 – 01:31:43 - Ethical Consideration in AI
01:31:43 – 01:34:17 - Transparency and Explainability & Trust and Safety
01:34:17 – 01:37:42 - Intellectual Prosperity & Human Rights & Environmental Impact
01:37:42 – 01:39:03 - Part-C – AI Asset Identification and Inventory
01:39:03 – 01:40:47 - Data Inventory and Management
01:40:47 – 01:43:10 - Data Life Cycle & Data Lineage
01:43:10 – 01:44:36 - Data Classification & Data Collection for AI
01:44:36 – 01:48:12 - Data Collection and Data Collection Risk for AI
01:48:12 – 01:51:17 - Data Confidentiality, Quality & Balancing
01:51:17 – 01:53:14 - Model Cards
01:53:14 – 01:58:47 - Part D – AI Security Program Development
01:58:47 – 02:04:02 - Component AI Security Program
02:04:02 – 02:05:50 - AI use in Enterprise security and operations
02:05:50 – 02:07:09 - Part -E – Business Continuity and Incident Response
02:07:09 – 02:09:40 - BCP & Business Impact Analysis
02:09:40 – 02:13:38 - BIA Metrics & Incident Response
02:13:38 – 02:16:52 - Business Problem and Potential AI Solutions
02:16:52 – 02:22:14 - Incident Management Cycle
02:22:14 – 02:23:19 - AI Enabled Incident Response
02:23:19 – 02:23:54 - AI Powered Incident Response – Benefits & Challenges
02:23:54 – 02:24:54 - Best Practices for Incident Response Automation
In this session, we focus on the right AAISM mindset:
Do not think like a technical AI engineer. Think like an AI security governance leader.
Domain 1 focuses on how organizations should establish AI governance, define ownership, create policies, manage stakeholders, maintain AI asset and data inventories, build AI security programs, and prepare for AI-related incidents and business continuity scenarios.
AI Practical
How to Pentest LLMs Like a Security Resear...
ISO 42001 Practical
ISO/IEC 42001 Practical AIMS Implementatio...
Practical AI Governance
PRACTICAL AI GOVERNANCE: STEP BY STEP PROCESS
AI Governance Foundation
AI Governance Simplified: From Zero to Pro
KK Book AI Governance
https://www.amazon.in/dp/9376311353
https://lnkd.in/gYzspcCQ
AAISM Domain 2
AAISM Domain 2 Master Class | AI Risk Mana...