The Artificial Intelligence Chief Security Officer (AICSO) Certification Training Course is an advanced professional program designed to prepare cybersecurity leaders, security managers, IT professionals, and AI governance stakeholders to manage the security risks associated with Artificial Intelligence systems.

The course provides a comprehensive understanding of AI security governance, AI-related cyber threats, secure AI lifecycle management, risk management, data protection, regulatory considerations, and organizational security strategy. Participants will explore the security implications of machine learning, generative AI, Large Language Models (LLMs), AI agents, and enterprise AI applications.

The program emphasizes the responsibilities of an AI security leader in establishing governance frameworks, assessing AI risks, protecting AI models and data, managing third-party AI services, responding to AI-related security incidents, and aligning AI initiatives with organizational cybersecurity and risk-management objectives.

Participants will also examine emerging AI attack techniques such as prompt injection, model manipulation, data poisoning, adversarial attacks, sensitive-information disclosure, model theft, and AI-enabled cyber threats. By the end of the course, participants will be prepared to develop and oversee an enterprise-level AI security program and support preparation for an AICSO certification examination or equivalent professional assessment.

 

Duration 5 Days – 35 hrs.

 

Objectives

• Understand the role and responsibilities of an Artificial Intelligence Chief Security Officer.
• Explain fundamental AI, machine learning, generative AI, and LLM concepts from a cybersecurity perspective.
• Identify security threats and vulnerabilities affecting AI systems.
• Establish an enterprise AI security governance framework.
• Develop policies and controls for responsible and secure AI adoption.
• Conduct AI-specific security and risk assessments.
• Understand threats involving prompt injection, data poisoning, adversarial manipulation, and model theft.
• Address security risks associated with generative AI and Large Language Models.
• Protect AI training data, models, APIs, infrastructure, and sensitive information.
• Apply security principles throughout the AI system development lifecycle.
• Evaluate security risks associated with third-party AI platforms and service providers.
• Integrate AI security into enterprise cybersecurity and risk-management programs.
• Develop AI incident response and resilience strategies.
• Understand privacy, regulatory, ethical, and compliance considerations surrounding AI.
• Establish AI security metrics, reporting, monitoring, and executive oversight.
• Develop a strategic AI security roadmap for the organization.
• Prepare for an AICSO certification examination or equivalent assessment.

 

Target Audience

• Chief Information Security Officers (CISOs)
• Aspiring AI Chief Security Officers
• Chief Security Officers
• Cybersecurity Managers and Directors
• Information Security Managers
• IT Security Managers
• Security Architects
• AI Security Professionals
• AI and Machine Learning Leaders
• AI Governance Professionals
• Enterprise Architects
• Risk Management Professionals
• Governance, Risk, and Compliance (GRC) Professionals
• Data Protection and Privacy Professionals
• Security Operations Leaders
• IT Managers and Directors
• Technology Executives
• AI Program and Project Managers
• Consultants responsible for AI governance and cybersecurity
• Professionals responsible for enterprise adoption of generative AI and AI technologies

 

Prerequisites

• Basic understanding of cybersecurity and information-security concepts.
• Familiarity with enterprise IT environments and security controls.
• General knowledge of risk management, governance, and compliance.
• Basic awareness of Artificial Intelligence, machine learning, or generative AI concepts.
• Familiarity with cloud computing and enterprise technology environments is beneficial.
• Previous management, security, risk, IT, or AI experience is recommended but not mandatory.
• No advanced AI programming or machine-learning development experience is required.

  

Course Outline

Day 1 – AI Foundations, Security Leadership, and Governance

Module 1: Introduction to Artificial Intelligence Security

• Evolution of Artificial Intelligence
• AI, Machine Learning, Deep Learning, and Generative AI
• Large Language Models and foundation models
• AI agents and enterprise AI applications
• AI technology ecosystem
• AI opportunities and organizational risks
• Why traditional cybersecurity approaches must evolve for AI

Module 2: The Role of the Artificial Intelligence Chief Security Officer

• Role and responsibilities of the AICSO
• AICSO versus CISO responsibilities
• AI security leadership principles
• Establishing organizational accountability
• Working with executive management and boards
• Collaboration with IT, cybersecurity, data, legal, privacy, and business teams
• Establishing an AI security operating model
• Building an AI security strategy

Module 3: AI Governance and Responsible AI

• Principles of AI governance
• Responsible and trustworthy AI
• Accountability and oversight
• AI policies, standards, and procedures
• Establishing acceptable AI usage
• AI system inventory and classification
• Human oversight and decision-making
• AI governance committees and organizational structures
• Managing shadow AI and unauthorized AI usage

Module 4: AI Security Governance Frameworks

• Introduction to AI risk-management frameworks
• NIST AI Risk Management Framework concepts
• ISO/IEC 42001 AI management system concepts
• Relationship with information-security management frameworks
• OWASP guidance for AI and LLM applications
• Mapping AI governance to existing cybersecurity programs
• Selecting controls based on organizational risk

 

Day 2 – AI Threats, Vulnerabilities, and Risk Management

Module 5: AI Threat Landscape

• Understanding the AI attack surface
• Threat actors targeting AI environments
• AI-enabled cyberattacks
• Attacks against AI models and applications
• Threats to AI infrastructure
• AI supply-chain risks
• Emerging AI security threats
• Threat modeling for AI systems

Module 6: Machine Learning and AI Model Security

• Adversarial machine learning
• Training-data poisoning
• Model poisoning
• Evasion attacks
• Model extraction and model theft
• Model inversion
• Membership inference
• Backdoor attacks
• Protecting model intellectual property
• Securing model training and deployment environments

Module 7: Generative AI and LLM Security

• Generative AI security architecture
• Prompt injection attacks
• Direct and indirect prompt injection
• Jailbreaking and manipulation
• Sensitive-information disclosure
• Insecure output handling
• Excessive agency
• Model denial-of-service considerations
• Retrieval-Augmented Generation security
• AI agent and tool integration risks
• Securing enterprise LLM applications

Module 8: AI Risk Assessment and Management

• Establishing an AI risk-management process
• AI asset identification and classification
• Threat and vulnerability assessment
• Impact and likelihood analysis
• AI risk registers
• Risk treatment strategies
• Control selection and implementation
• Risk acceptance and escalation
• Continuous AI risk monitoring

 

Day 3 – Secure AI Architecture, Data Protection, and Development

Module 9: Secure AI Architecture

• AI system architecture and security boundaries
• Secure-by-design principles
• Defense-in-depth for AI systems
• Identity and access management
• Privileged access controls
• API security
• Network and infrastructure security
• Cloud AI security considerations
• Securing AI endpoints and interfaces
• Secrets and credential management

Module 10: AI Data Security and Privacy

• AI data lifecycle
• Training, validation, and inference data
• Data classification
• Data minimization
• Protecting sensitive and confidential information
• Personally identifiable information and privacy risks
• Data leakage through generative AI
• Encryption and access controls
• Data lineage and provenance
• Data retention and deletion
• Privacy-preserving AI considerations

Module 11: Secure AI Development Lifecycle

• Integrating security into the AI lifecycle
• AI security requirements
• Secure model development
• Secure coding considerations for AI applications
• Dependency and component management
• AI software supply-chain security
• Security testing of AI applications
• Model validation and verification
• Red teaming AI systems
• Pre-deployment security reviews
• Continuous security after deployment

Module 12: Third-Party and AI Supply-Chain Security

• Risks of external AI platforms
• SaaS and cloud AI services
• Open-source models and libraries
• Third-party model evaluation
• Vendor security assessment
• AI procurement security requirements
• Contractual and data-protection considerations
• Supply-chain dependencies
• Continuous third-party monitoring

 

Day 4 – Operations, Incident Response, Compliance, and Resilience

Module 13: AI Security Operations and Monitoring

• Security monitoring for AI environments
• AI-specific logging requirements
• Detecting suspicious AI activity
• Model behavior monitoring
• Detecting abnormal inputs and outputs
• Security analytics
• Integration with SOC operations
• Continuous control monitoring
• AI security metrics and indicators

Module 14: AI Security Incident Response

• AI-specific incident scenarios
• Preparing an AI incident response plan
• Identification and triage
• Containment of compromised AI systems
• Model isolation and rollback
• Investigation and forensic considerations
• Recovery and restoration
• Stakeholder communications
• Post-incident review
• Updating AI controls after incidents

Module 15: Business Continuity and AI Resilience

• AI system availability risks
• Resilience of AI-dependent business processes
• Model and service failure scenarios
• Backup and recovery considerations
• Dependency on external AI providers
• AI service continuity planning
• Operational resilience
• Crisis-management considerations

Module 16: AI Legal, Regulatory, Privacy, and Compliance Considerations

• AI regulatory landscape
• Data-protection obligations
• Security and privacy responsibilities
• AI transparency and explainability
• Accountability and human oversight
• Intellectual-property considerations
• Regulatory risk management
• Documentation and evidence requirements
• AI audit readiness
• Managing evolving regulatory requirements

 

Day 5 – Enterprise AI Security Strategy and Certification Preparation

Module 17: Building an Enterprise AI Security Program

• Defining AI security objectives
• Establishing AI security policies
• Developing governance structures
• Assigning roles and responsibilities
• AI security control framework
• Integration with enterprise security programs
• Security awareness for AI users
• Building organizational AI security capability
• Establishing an AI security center of excellence

Module 18: AI Security Metrics and Executive Reporting

• Establishing AI security KPIs and KRIs
• Measuring AI security maturity
• Risk dashboards
• Reporting AI risks to senior management
• Board-level AI security reporting
• Communicating technical AI risks in business terms
• Tracking remediation and control effectiveness
• Continuous improvement

Module 19: Developing the AI Security Roadmap

• Assessing current-state AI security maturity
• Identifying security gaps
• Defining target-state capabilities
• Prioritizing AI security initiatives
• Short-, medium-, and long-term objectives
• Resource and capability planning
• Building the AI security roadmap
• Continuous governance and improvement

Module 20: AICSO Certification Review and Preparation

• Review of AI security fundamentals
• Review of governance and risk management
• Review of AI and LLM security threats
• Review of secure AI lifecycle principles
• Review of data security and privacy
• Review of incident response and resilience
• Review of compliance and governance concepts
• Scenario-based certification questions
• Key concepts and terminology review
• Certification examination preparation