AI Powered Course
InfosecTrain’s Certified Enterprise Cloud and AI Security Governance Training
Governance that Secures Cloud and AI
Program Highlights
InfosecTrain’s Certified Advanced Cloud & AI Security Governance course equips professionals with the strategic and practical skills needed to govern security, manage risk, and ensure compliance across modern cloud and AI environments. As organizations increasingly adopt cloud and artificial intelligence, security demands extend beyond technical controls to enterprise governance, regulatory alignment, and accountability. This program emphasizes enterprise-level decision-making, risk oversight, and operational governance, enabling participants to align security initiatives with business objectives, regulatory requirements, and emerging AI risks across enterprises, regulated industries, and government sectors.
32-Hour Instructor-Led Training 
Hands-on Wazuh SIEM & XDR Labs 
Detection Engineering with Custom Rules & Decoders 
SOC Analyst Triage & Investigation Workflow 
Endpoint Telemetry from Windows & Linux 
Active Response & Automation 
Vulnerability Detection & Compliance Monitoring 
Dashboarding & SOC Visualization 
Recorded Sessions & Post-Training Support
Training Schedule
- upcoming classes
- corporate training
- 1 on 1 training
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InfosecTrain’s Certified Enterprise Cloud and AI Security Governance Training is a comprehensive, governance-driven program designed to help professionals secure and govern cloud and AI environments at an enterprise scale. The course provides a structured understanding of how cloud platforms and AI systems operate, and how security, risk management, compliance, and accountability must be governed across their full lifecycle.
The program covers cloud computing and AI foundations, followed by deep dives into cloud and AI security governance, risk assessment, compliance, audit, IAM, data governance, workload security, monitoring, application security, and incident response. Learners will gain practical insight into governing AI data, identities, pipelines, models, and monitoring using cloud-native controls.
Aligned with global frameworks such as ISO/IEC 42001, NIST AI RMF, EU AI Act, and CSA AICM, this course prepares participants to design defensible governance models, conduct AI risk and compliance assessments, manage AI-specific incidents, and support responsible AI initiatives across enterprises, regulated industries, and government environments.
- Module 1: Cloud Computing Concepts & Architecture
- Cloud Computing Overview
- Essential characteristics, benefits, and challenges
- Abstraction & Orchestration
- Cloud Service Models & Deployment Models
- CSA Enterprise Architecture Model
- Cloud Security Overview
- Shared Security Responsibility Model
- Scope, Responsibilities & Models
- Threat landscape and new attack vectors in cloud
- Module 02: AI Concepts & Architecture
- Fundamentals of Artificial Intelligence & Machine Learning
- AI Systems Classification
- Types of AI
- AI Usage & Impact
- Use Cases, Benefits & Challenges
- AI Governance Foundation
- AI Model Types
- Training Types
- AI Technology Stack
- AI Impact & Principles
- Module 03: Introduction to Cloud & AI Security Governance
- Foundations of Cloud & AI Security Governance
- Objectives of Governance vs. Security
- Enterprise Risk Governance in Cloud & AI
- Cloud Security Frameworks & Policies
- Complexities in Cloud & AI Security Governance
- Governance as a Business Enabler
- Impact of Cloud Service & Deployment Models
- Cloud Risk Trade-offs & Governance Tools
- Shared Responsibility & Governance Enablers
- Contracts, SLAs & PLAs
- Roles & Critical Stakeholders in Cloud & AI Governance
- Cloud & AI Threat Landscape
- Cloud-specific Threats & Attack Vectors
- AI threat landscape
- Defense-in-depth Approach
- Security Controls Across Cloud & AI Lifecycle
- Encryption, IAM & Intrusion Detection
- AI lifecycle Security Controls
- AI Red Teaming & Adversarial Attacks
- Incident Response for AI Systems
- Case Study
- Capital One Cloud Data Breach – Governance Failures
- Foundations of Cloud & AI Security Governance
- Module 4: Risk Assessment and Management
- Cloud-Specific Risks & Threats
- Data Breaches, Data Loss, and Multi-tenancy
- Misconfigurations, and Shared Resource Risks
- Real-world Cloud Security Incident Case
- Cloud Risk Assessment Methodologies
- Cloud Risk Assessment Process
- NIST Cybersecurity Framework for Cloud
- Risk Register Development
- Cloud Risk Treatment & Control Selection
- Risk Acceptance, Avoidance, Transfer, and Mitigation
- Cloud Security Control Selection
- Vendor & Third-party Cloud Risk Assessment
- Cloud Risk Monitoring & Continuous Improvement
- Cloud Security Metrics & KPIs
- SIEM in Cloud Environments
- Incident Management & Cloud Security Policy Basics
- AI Risk Categories
- Ethical, operational, societal risks
- AI Risk Frameworks & Models
- NIST AI RMF
- MIT AI Risk Repository
- EU AI Act risk tiers
- AI Risk Assessment & Governance
- AI Risk Register & AI Impact Assessment (AIIA)
- Bias Identification & Mitigation
- Third-party AI Risk Management
- AI Governance Maturity Models
- Case Studies
- Cloud Risk Assessment & Sample Risk Report
- AI-powered chatbot risk assessment
- Cloud-Specific Risks & Threats
- Module 5: Cloud & AI Compliance, Audit & Assurance
- Cloud Compliance Program Overview
- Designing & Building a Cloud Compliance Program
- Cloud-Relevant Laws & Regulations (Overview)
- Implementing Compliance Controls in Cloud Environments
- Compliance Inheritance & Shared Responsibility
- Compliance Artifacts & Evidence Management
- Cloud Auditing Fundamentals
- Audit Characteristics, Principles & Criteria
- Types of Audits
- Audit Steps, Objectives & Scope
- Auditing & Reporting in the Cloud
- Cloud Auditing Standards & Frameworks
- Auditing AI Systems
- AI Audit Frameworks & Standards
- Key AI Audit Areas & Techniques
- Challenges in AI Auditing (Models, Data Access, Transparency)
- Practical Exercises & Case Studies
- PCI DSS Compliance in Cloud
- AI Audit Simulation Exercise
- Module 6: Organization Management
- Organization Hierarchy Models
- Organization Capabilities Within a Cloud Service Provider
- Building a Hierarchy Within a Provider
- Managing Organization-Level Security Within a Provider
- Identity Provider & User/Group/Role Mappings
- Common Organization Shared Services
- Considerations for Hybrid & Multi-Cloud Deployments
- Organization Management for Hybrid Cloud Security
- Organization Management for Multi-Cloud Security
- Organization Management for SaaS Hybrid & Multi-Cloud
- Organization Hierarchy Models
- Module 7: Identity and Access Management (IAM) for Cloud & AI
- Foundations of IAM in Cloud & AI
- IAM Concepts, Components & Importance
- IAM Across Major Cloud Platforms
- RBAC, ABAC & PBAC Models
- Roles, Permissions & Access Governance
- Role Design, Hierarchy & Inheritance
- Least Privilege & Authorization Creep Prevention
- Federation, SSO & MFA
- Federated Identity & Cloud Integration
- SO & MFA Best Practices
- Zero Trust for Cloud & AI
- Zero Trust Principles
- Continuous Authentication & Least Privilege
- Zero Trust Implementation in Cloud & AI Systems
- IAM for AI Workloads
- Human vs Machine Identities
- Service Accounts & Model Access Control
- API & Inference Access Governance
- Case Study
- Best Practices & IAM Baselining in Cloud Environments
- Foundations of IAM in Cloud & AI
- Module 8: Cloud & AI Data Security, Privacy & Governance
- Strategic Role of Data in Cloud & AI Systems
- Enterprise Data Strategy for AI
- Cloud Storage Types for AI Workloads
- Storage Models (Object, Block, File)
- Use Cases & Selection Criteria
- Data Governance Policy Framework
- Data Ownership & Stewardship
- Data Quality & Data Gathering
- Data Lifecycle Management for AI Projects
- Data Lineage, Traceability & Regulatory Mapping
- Data Cleansing, Labelling & Ethics
- Data Quality Improvement
- Data Labelling Risks
- Data Ethics & Responsible Data Use
- Data Bias in AI Systems
- Data Validation & Testing
- Data Security Tools & Techniques
- Data Classification
- Identity & Access Management
- Access Policies
- Encryption & Key Management
- Data Loss Prevention (DLP)
- Building a Cloud Data Classification Program
- Policy Establishment
- Monitoring & Enforcement
- Data Privacy & Protection for AI
- Data Anonymization & Pseudonymization
- Differential Privacy Techniques
- Data Exfiltration Risks
- Data Sovereignty, Residency & Cross-Border Governance
- Legal & Compliance Implications
- Data Localization & Geo-Fencing
- Regional Regulatory Compliance (eg: GDPR)
- Data Dispersion, Replication & Resiliency Governance
- Multi-Region Replication & DR
- Governance Concerns on Location & Access
- Contractual, SLA & Audit Controls
- Data Encryption & Key Management Best Practices
- Encryption Standards & Algorithms
- Key Lifecycle Management
- Cloud Provider Key Management Services
- Data Retention, Deletion & Archiving Policies
- Secure Data Erasure
- Lifecycle Automation
- Legal Hold Challenges
- Key Cloud & AI Data Governance Risks
- Data Poisoning
- Data Leakage & Exfiltration
- PII Misuse
- Cross-Border Data Violations
- Data Security for AI & AI as a Service (AIaaS)
- Case Studies
- Securing Sensitive Data in Cloud Object Storage
- AI Recommendation Engine – End-to-End Data Governance
- Module 9: Cloud Infrastructure & Networking
- Cloud Network Architecture & Security Foundations
- Virtual Networks, Isolation & Segmentation
- Security Groups, NACLs & Firewall Concepts
- Software-Defined Networking (SDN)
- Network Segmentation & Zero Trust Networking
- Segmentation & Zoning Strategies
- Zero Trust Network Access (ZTNA)
- Cloud Firewalls & Application Protection
- Cloud Firewall Services
- Web Application Firewall (WAF)
- DDoS & Network Attack Protection
- DDoS Attack Concepts
- Cloud DDoS Mitigation Services
- Detection & Response Strategies
- Zero Trust & Secure Network Access Models
- Software-Defined Perimeter
- Secure Access Service Edge (SASE)
- Cloud Network Architecture & Security Foundations
- Module 10: Cloud Workload Security
- Types of Cloud Workloads
- Impact on Workload Security Controls
- Securing Virtual Machines
- Virtual Machine Challenges & Mitigations
- Creating Secure VM Images with Factories
- Snapshots & Public Exposures/Exfiltration
- Securing Containers
- Container Images
- Container Network Architecture
- Container Orchestration & Management Systems
- Container Orchestration Security
- Runtime Protection for Containers
- Securing Serverless and Function as a Service
- FaaS Security Issues
- IAM for Serverless
- Environment Variables & Secrets
- Securing AI Workloads
- AI-System Threats
- AI Risk Mitigation and Shared Responsibilities
- Module 11: Security Monitoring
- Role of Security Monitoring in Cloud & AI Governance
- Cloud Monitoring Fundamentals
- Logs, Metrics & Events
- Security vs Operational Monitoring
- Cloud Telemetry Sources
- Management Plane Logs
- Service & Application Logs
- Resource-Level Logs
- Cloud-Native Monitoring Tools
- Log Collection & Monitoring Architectures
- Centralized Log Collection
- Log Storage & Retention Governance
- Cascading / Multi-Account Log Architecture
- Beyond Logs – Security Posture Management
- Cloud Security Posture Management (CSPM)
- Configuration Drift Detection
- Continuous Compliance Monitoring
- AI for Security Monitoring
- AI-Driven Threat Detection
- Behavioural Analytics
- Anomaly Detection in Cloud Environments
- Alerting & Automated Response Governance
- Security Alert Orchestration
- Event-Driven Response Models
- Notification, Escalation & Auditability
- Monitoring Risks & Governance Challenges
- Log Tampering
- Alert Fatigue
- Blind Spots in Multi-Cloud & AI Workloads
- Module 12: Application Security
- Secure Development Lifecycle (SDLC)
- SDLC Stages
- Threat Modelling
- Pre-Deployment & Post-Deployment Security Testing
- SDLC Methodologies
- Agile, DevOps, Waterfall
- Governance in Each SDLC Phase
- Planning, Design, Development, Testing, Deployment, Maintenance
- Architecture’s Role in Secure Cloud Applications
- Cloud Impacts on Application Security
- Architectural Resilience
- Identity & Access Management in Application Security
- Secrets Management
- DevOps & DevSecOps Integration
- SDLC for AI Systems
- Secure AI Model Development Lifecycle
- Governance Across AI Training, Testing & Deployment
- Secure Development Lifecycle (SDLC)
- Module 13: Incident Response
- Role of Incident Response in Cloud & AI Governance
- Incident Response Lifecycle
- Preparation, Detection, Containment, Eradication, Recovery, Lessons Learned
- Cloud-Specific Incident Response Planning
- Shared Responsibility
- Testing, Table-Top & Simulation Exercises
- Cloud Incident Investigation & Triage
- Incident Classification
- Impact Assessment
- Business vs Technical Prioritization
- Evidence Collection & Cloud Forensics
- Logs & Digital Artifacts
- Evidence Preservation
- Data Integrity & Chain of Custody
- Digital Forensics in Cloud Environments
- Shared Infrastructure & Multi-Tenant Challenges
- Cloud Forensics Best Practices
- AI-Specific Security Incidents
- Data Leakage vs Model Inversion
- Model Drift
- Adversarial & Prompt-Based Attacks
- AI Incident Playbook Design
- Detection & Response
- Model Isolation & Rollback
- Dataset & Pipeline Integrity Validation
- Incident Communication & Governance
- Executive, Regulatory & Legal Reporting
- Scenario Discussion
- Designing a Cloud & AI Incident Response Runbook
- Module 14: Global AI Laws, Regulations & Accountability
- Overview of Global AI Laws & Regulations
- Legal & Ethical Foundation
- Data Privacy, Bias, Transparency & Accountability
- Categories of AI Law & Regulatory Approaches
- Emerging Trends in AI Legislation
- Industry Impact of AI Regulations
- Key Global AI Frameworks & Standards
- OECD AI Principles
- EU AI Act
- ISO/IEC 42001:2021
- Regulatory Impact Assessment on AI Systems
- Cross-Border AI Compliance Management
- Intellectual Property Rights in AI
- Copyright & Patents for AI Models & Data
- Ownership of AI-Generated Content
- Liability & Accountability in AI
- Liability for AI-Related Harms
- Algorithmic Accountability & Auditability
- AI System Auditing & Regulatory Review Mechanisms
- Overview of Global AI Laws & Regulations
- Information Security Professionals
- Cloud Security Architects
- Enterprise Risk Management Professionals
- Cloud Managers & Platform Owners
- Governance, Risk & Compliance (GRC) Professionals
- CISOs, Security Managers & IT Directors
- Data Protection & Privacy Officers
- AI Program Managers & Digital Transformation Leaders
- Compliance & Internal Audit Professionals
- Technology Risk & Advisory Consultants
- 3-5 years in cloud security, governance, or IT risk
- Familiarity with IAM, encryption, monitoring
- Basic AI/ML knowledge (not mandatory)
- GRC, compliance, audit experience beneficial
This course aims to:
- Understand Cloud and AI Architectures and Service Models
- Apply Cloud and AI Security Governance Principles
- Assess Cloud and AI Risks Using Recognized Frameworks
- Design Governance Controls Across Cloud and AI Lifecycles
- Implement IAM and Zero Trust for Cloud and AI
- Govern AI Data Security, Privacy, and Lineage
- Establish Cloud and AI Compliance and Audit Readiness
- Monitor AI Drift, Bias, and Security Posture
- Respond to Cloud and AI Security Incidents
- Interpret Global AI Laws and Accountability Requirements
How We Help You Succeed
Vision
Goal
Skill-Building
Mentoring
Direction
Support
Success
Benefits of InfosecTrain’s Certified Enterprise Cloud and AI Security Governance Training
Govern Cloud and AI systems across data, identity, workloads, and models
Build governance controls using IAM, encryption, monitoring, and lifecycle policies
Conduct Cloud and AI risk assessments aligned with regulatory frameworks
Strengthen compliance, audit readiness, and evidence-based assurance
Advance enterprise careers in Cloud Security, AI Governance, and GRC
Average Salary
Average Salary
Hiring Companies
"Source: Indeed, Glassdoor"
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19+ Years of Experience | Microsoft & CSA Authorized Instructor
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It was a very good experience with the team. The class was clear and understandable, and it benefited me in learning all the concepts and gaining valuable knowledge.
I loved the overall training! Trainer is very knowledgeable, had clear understanding of all the topics covered. Loved the way he pays attention to details.
I had a great experience with the team. The training advisor was very supportive, and the trainer explained the concepts clearly and effectively. The program was well-structured and has definitely enhanced my skills in AI. Thank you for a wonderful learning experience.
The class was really good. The instructor gave us confidence and delivered the content in an impactful and easy-to-understand manner.
The program helped me understand several areas I was unfamiliar with. The instructor was exceptionally skilled and confident in delivering content.
The program was well-structured and easy to follow. The instructor’s use of real-life AI examples made it easier to connect with and understand the concepts.
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Frequently Asked Questions
What is cloud and AI security governance?
Cloud and AI security governance refers to the structured approach of defining policies, controls, roles, and oversight mechanisms to manage security, risk, compliance, and accountability across cloud platforms and AI systems throughout their lifecycle.
Why is AI governance important for enterprises?
AI governance is critical for enterprises to manage AI-related risks such as data misuse, bias, model drift, regulatory non-compliance, and accountability issues while aligning AI systems with business objectives and legal requirements.
Who should take cloud and AI security governance training?
This course is ideal for:
- Information Security Professionals
- Cloud Security Architects
- Enterprise Risk Management Professionals
- Cloud Managers & Platform Owners
- Governance, Risk & Compliance (GRC) Professionals
- CISOs, Security Managers & IT Directors
- Data Protection & Privacy Officers
- AI Program Managers & Digital Transformation Leaders
- Compliance & Internal Audit Professionals
- Technology Risk & Advisory Consultants
Is this program suitable for CISOs and GRC professionals?
Yes. The course focuses on enterprise-level governance, risk oversight, regulatory readiness, audit assurance, and decision-making, making it highly relevant for CISOs, security leaders, and GRC professionals.
Does the course cover enterprise AI risk management?
Yes. The course covers AI risk categories, AI risk assessment methodologies, AI impact assessments, bias identification and mitigation, third-party AI risk management, and AI governance maturity models.
Is AI compliance and regulatory governance included?
Yes. The program covers cloud and AI compliance programs, regulatory requirements, global AI laws, EU AI Act risk tiers, compliance evidence management, and audit readiness.
Does this training focus on cloud security governance?
Yes. The course includes cloud governance foundations, shared responsibility models, cloud risk assessment, IAM governance, data governance, workload security, monitoring, and incident response in cloud environments.
Is this an enterprise-level AI governance certification?
Yes. The training is designed for enterprise environments and prepares participants to contribute to and lead cloud and AI security governance initiatives across large organizations and regulated industries.
Does the course address responsible and ethical AI practices?
Yes. The course addresses ethical and societal AI risks, data governance, bias identification, responsible data use, accountability, and regulatory expectations related to responsible AI.
Will I learn governance frameworks for AI and cloud systems?
Yes. The course aligns with recognized governance frameworks and standards, including ISO/IEC 42001, NIST AI RMF, EU AI Act, CSA AICM, and cloud security governance models.
Is this program aligned with modern AI governance standards?
Yes. The curriculum reflects current AI governance standards, regulatory frameworks, and industry best practices relevant to enterprise cloud and AI deployments.
Does InfosecTrain provide a course completion certificate?
Yes. Participants receive a course completion certificate from InfosecTrain upon successful completion of the training program.
