Enterprise Cloud & AI Governance Professional Training

PART 1: Cloud Security Governance
• Module 1: Cloud Computing Concepts & Architecture
  • Cloud Computing Overview
  • Essential characteristics, benefits, and challenges
  • Abstraction & Orchestration
  • Cloud Service Models: IaaS, PaaS & SaaS
  • Deployment Models: Public, Private, Hybrid & Community
  • CSA Enterprise Architecture Model
  • Cloud Security Overview
  • Shared Security Responsibility Model
  • Scope, Responsibilities & Models
  • Threat landscape and new attack vectors in cloud
• Module 2: Introduction to Cloud Security Governance
  • Understanding cloud security governance
  • Complexities in Cloud Security Governance
  • Leveraging key tools for governance in the cloud & Shared Security Responsibility Model
  • Building & integrating an effective cloud governance strategy
  • Analyzing cloud-specific threats and attack vectors
  • Case Study: Capital One Data Breach and its Timeline
• Module 3: Cloud Risk Assessment and Management
  • Identifying cloud-specific risks and threats
  • Risk assessment methodologies for cloud environments
  • Developing risk management strategies
  • Cloud risk monitoring and continuous improvement
  • Case Study: Conducting a Cloud Risk Assessment & Creating a Sample Risk Assessment Report
• Module 4: Cloud Compliance & Audit
  • Cloud Compliance Program Overview
  • Design & Build a Cloud Compliance Program
  • Cloud-Relevant Laws & Regulations Examples
  • Implementing compliance controls in cloud environments
  • Compliance Inheritance
  • Artifacts of Compliance
  • Defining controls and evaluating effectiveness
  • Audit characteristics, principles, and criteria in Cloud
  • Auditing standards for cloud computing
  • Case Study: Enabling PCI DSS Compliance on AWS
  • Case Study: Perform a practical Cloud Auditing
• Module 5: Organization Management
  • Organization Hierarchy Models
  • Managing Organization-Level Security Within a Provider
  • Considerations for Hybrid & Multi-Cloud Deployments
• Module 6: Identity and Access Management (IAM) & Zero Trust in the Cloud
  • Principles of IAM in cloud environments
  • Federation, Single sign-on (SSO) and multi-factor authentication (MFA) in the cloud
  • Zero Trust Model (ZTMF)
  • Architecting for zero trust
  • Case Study: Best Practices & Baselining Identity & Access Management in AWS
• Module 7: Cloud Data Security and Encryption
  • Primer on Cloud Storage
  • Data Security Controls, Tools & Techniques
  • Building a proper data classification program for the cloud
  • Data dispersion and resiliency
  • Data Encryption and Key Management best practices
  • Data retention, deletion, and archiving policies for cloud
  • Data Sovereignty & Legal hold challenges and preparation
  • Scenario Discussion: Data encryption strategies, 3rd party integration, and practical architecture
• Module 8: Cloud Infrastructure & Networking
  • Securing virtual networks in the cloud
  • Network segmentation and isolation strategies
  • Application and network-level firewalls for cloud environments
  • Attack distribution and DDoS protection in the cloud
  • Zero Trust for Cloud Infrastructure & Networks
  • Secure Access Service Edge (SASE)
• Module 9: Cloud Workload Security
  • Types of Cloud Workloads
  • Impact on Workload Security Controls
  • Virtual Machines, Containers, Serverless Security Strategies
  • Securing AI Workloads
  • AI-System Threats
  • AI Risk Mitigation and Shared Responsibilities
• Module 10: Security Monitoring
  • Cloud Monitoring
  • Beyond Logs – Posture Management
  • Cloud Telemetry Sources
  • Collection Architectures
  • AI for Security Monitoring
• Module 11: Application Security
  • Secure Development Lifecycle
  • Architecture’s Role in Secure Cloud Applications
  • Identity & Access Management and Application Security
  • DevOps & DevSecOps
  • Microservices
• Module 12: Incident Response and Cloud Forensics
  • Incident Response Lifecycle
  • Preparation
  • Detection & Analysis
  • Containment, Eradication, & Recovery
  • Post Incident Analysis
  • Developing a cloud-specific incident response plan
  • Investigating security incidents in the cloud
  • Digital forensics challenges and best practices in cloud environments
  • Scenario Discussion: Creating an Incident Response Runbook
• Module 13: Cloud Security Assurance and Assessment & STAR
  • Cloud security assessment methodologies
  • Security controls testing and validation in the cloud.
  • Cloud security certifications and their significance
  • CCM and CAIQ
  • CCM Domains & Controls
  • Mapping standards and frameworks
  • CSA STAR Program
  • Security & Privacy Implications of STAR
  • STAR Program Components
  • Scenario Discussion: Creating an assessment report on Cloud-based on CCM & CAIQ
• Module 14: Cost Management and Security
  • Understanding the cost implications of security decisions
  • Budgeting for cloud and cloud security initiatives
  • Cost optimization without compromising security
  • Cost-benefit analysis and return on investment for Cloud services

PART 2: AI Governance

• Module 1: AI Foundations
  • Types of AI (Functionality & Capabilities)
  • Branches & Applications of AI across industries
  • AI Technology Stack
  • Machine Learning Components, Processes, and Types
  • Generative AI & Large Language Models (LLMs)
  • Common AI Attacks & Mitigation
  • Ethical Considerations
• Module 2: Ethics, Responsible AI & Societal Impact
  • Principles of Responsible AI
  • Bias, Fairness, and Discrimination
  • Privacy & Security Concerns
  • Job Displacement & Economic Impact
  • Bias: Use Cases
  • Types of AI Discrimination
  • Addressing algorithmic bias and fairness
  • Privacy concerns and data protection.
  • Responsible AI Development and Deployment
  • Key principles of Responsible AI
  • Case Studies
• Module 3: Global AI Laws & Regulations
  • Overview of existing AI laws and regulations
  • Legal and ethical considerations: Data privacy, bias, transparency, accountability
  • Emerging trends in AI legislation
  • How do AI regulations affect the adoption of AI in different industries
  • Categories of AI Law
  • Legal and ethical considerations: Data privacy, bias, transparency, accountability
  • OECD AI Principles: Fairness, transparency, and accountability.
  • EU AI Act
  • ISO/IEC 42001:2021 for Artificial Intelligence
  • Assessing the regulatory impact on AI systems.
  • Managing cross-border compliance
  • Intellectual Property Rights
  • Liability and Accountability
• Module 4: AI Governance
  • Governance & Types
  • Enterprise AI Governance Vs. Responsible AI Governance
  • AI Governance Models (Centralized, Decentralized, Federated)
  • Trustworthy AI
  • Responsible Artificial Governance (RAG)
  • Transparency, explainability & Liability
  • Designing AI Governance Committees & Councils
  • Aligning AI with Business Objectives
  • Building & Measuring AI Governance Programs
  • Identifying and Engaging Stakeholders
  • Aligning Stakeholder Interests with Governance Objectives
  • Managing Expectations & Communication
  • Role-Based Exercises
• Module 5: AI Models, Architecture & Lifecycle
  • Key Layers of AI Architecture (Data, Model, Application, Security)
  • Governance in AI Architecture
  • AI System Lifecycle & Governance Integration
  • AI in the Cloud
  • Understanding AI Models
  • Model Evaluation & Interpretability (LIME, SHAP, Rule-Based, Visualizations)
  • Explainability & Accountability (GDPR Right to Explanation)
  • RAG & Prompt Engineering
  • Agentic AI & Automation
  • Model Drift, Degradation, Monitoring
  • Model Cards & Documentation
• Module 6: AI Risk Management
  • AI Risk Categories: Ethical, Operational, Societal
  • NIST AI RMF & MIT AI Risk Repository
  • AI Risk Register & AI Impact Assessment (AIIA)
  • Risk Assessment Methodologies (FMEA, FTA)
  • EU AI Act Risk Tiers
  • Bias Identification & Mitigation
  • Third-Party AI Risk Management
  • AI Governance Maturity Models
  • Case Study: AI-Powered Chatbot Risks
• Module 7: Data Governance for AI
  • Data Strategy for AI
  • Data Governance Policy
  • Data quality, Data Gathering
  • Data Cleansing
  • Data Labelling, Data Privacy & Security, Data Ethics
  • Data Bias
  • Data Validation and Testing Data
  • Data lifecycle management for AI projects
  • Data collection, processing, storage, and use for AI systems
  • Data exfiltration
  • Data Anonymization, Pseudonymization, and Differential Privacy techniques
  • Case Study: AI recommendation engi
  • Implementing data governance frameworks for AI
  • AI data security
• Module 8: AI Model Validation & Testing
  • Understanding AI Models
  • Model Evaluation & Interpretability (LIME, SHAP, Rule-Based, Visualizations)
  • Explainability & Accountability (GDPR Right to Explanation)
  • Retrieval Augmented Generation (RAG) & Prompt Engineering
  • Model Drift, Degradation, and Monitoring
  • Model Validation & Testing (Bias, Robustness, Failures)
  • Model Cards & Documentation
• Module 9: AI on Cloud
  • Role of Cloud in AI
  • AI Hosting Models on Cloud
  • Key considerations for choosing CSP for AI Workloads
  • Leveraging Native Cloud Security for AI
  • Addressing AI-Specific Security Vectors in the Cloud
  • Integrating AI Governance into Cloud Infrastructure
  • Case Study: AI Application Lifecycle
• Module 10: AI Security
  • AI Threat Landscape
  • Security Controls Across AI Lifecycle
  • Encryption, IAM, and Intrusion Detection
  • AI Red Teaming & Adversarial Attacks
  • Incident Response for AI Systems
• Module 11: Auditing AI Systems
  • AI Audit Frameworks & Standards
  • Key Audit Areas & Techniques
  • Challenges in AI Auditing (Methodologies, Data Access)
  • AI Audit Simulation Exercise
• Module 12: SDLC for AI Systems
  • SDLC Methodologies (Agile, DevOps, Waterfall)
  • Governance in Each SDLC Phase
  • Planning, Design, Development, Testing, Deployment, Maintenance

This training is ideal for:

• Cloud Security & Governance Professionals
• AI Governance, GRC & Risk Professionals
• Security Architects & Enterprise Architects
• IT & Security Leaders overseeing AI adoption
• Consultants, Auditors, and Assessors
• Compliance, Privacy & Policy Managers
• Cloud & AI Program Managers

• Basic understanding of cloud computing and security concepts.
• Some experience in information security, risk management, & Governance is beneficial but not mandatory.
• Exposure to AI, data, or digital transformation initiatives will be helpful, but no tech/programming background is required.

Upon successful completion of the training, participants will be able to:

• Develop a holistic governance perspective covering both Cloud and AI systems
• Gain the ability to design and operate enterprise-wide Cloud & AI governance programs aligned with business objectives and regulatory expectations
• Learn how to translate cloud security, risk, and compliance controls into AI-enabled environments
• Build expertise in cloud & AI risk management
• Understand cloud governance models, shared responsibility, and compliance inheritance in enterprise environments
• Learn GRC aspects specific to cloud, including data security, IAM, visibility, and infrastructure governance.
• Gain clarity on AI governance principles, including accountability, transparency, explainability, and oversight
• Learn how to govern data used by AI systems across its lifecycle, including privacy, bias, and data protection
• Understand global AI regulations and standards and their impact on enterprise AI adoption
• Strengthen skills in audit, assurance, and evidence readiness for both cloud platforms and AI systems
• Position yourself as a Cloud & AI Governance professional capable of supporting regulated, large-scale enterprise initiatives