The AI Chief Security Officer (AI CSO) Program Training Course is a comprehensive leadership and technical program designed to prepare security professionals, technology leaders, and senior managers to govern and secure artificial intelligence within modern organizations.

The program addresses the evolving responsibilities of security leaders as organizations adopt generative AI, machine learning, AI assistants, autonomous agents, cloud-based AI services, and AI-enabled business applications. Participants will learn how to establish AI security governance, identify and assess AI-specific risks, protect AI systems and data, manage third-party AI exposure, respond to AI-related incidents, and integrate AI security into the organization’s broader cybersecurity and enterprise risk management programs.

The course also examines how AI can strengthen cybersecurity operations through intelligent threat detection, security automation, vulnerability management, incident response, and security analytics. Particular attention is given to emerging threats such as prompt injection, data leakage, model manipulation, adversarial attacks, insecure AI integrations, model supply-chain risks, and misuse of generative AI.

By the end of the program, participants will be equipped to develop an enterprise-level AI Security Strategy and Governance Roadmap aligned with business objectives, cybersecurity principles, responsible AI practices, and organizational risk appetite.

Duration 5 Days – 35 hrs.

Objectives

• Understand the evolving role of the Chief Security Officer in AI-enabled organizations.
• Explain fundamental AI, machine learning, generative AI, large language model, and AI agent concepts from a security perspective.
• Identify cybersecurity threats and vulnerabilities affecting AI systems.
• Understand the AI attack surface across data, models, applications, APIs, infrastructure, and users.
• Establish an enterprise AI security governance framework.
• Define security policies and controls for organizational AI adoption.
• Conduct AI security risk assessments and threat modeling.
• Address prompt injection, data leakage, model manipulation, adversarial attacks, and AI misuse.
• Establish security controls for generative AI and large language model applications.
• Protect sensitive information used by AI systems.
• Apply identity, access management, Zero Trust, and least-privilege principles to AI environments.
• Evaluate third-party AI providers and AI supply-chain risks.
• Integrate AI security into cloud, application, data, and enterprise security architectures.
• Develop secure AI lifecycle and AI security-by-design practices.
• Use AI capabilities to enhance security operations and threat detection.
• Establish AI incident detection, response, recovery, and reporting procedures.
• Develop AI security metrics, governance reporting, and executive-level risk communication.
• Build an enterprise AI Security Strategy and implementation roadmap.

Target Audience

• Chief Security Officers (CSOs)
• Chief Information Security Officers (CISOs)
• Deputy CISOs and Security Directors
• Chief Information Officers (CIOs)
• Chief Technology Officers (CTOs)
• IT Security Managers
• Cybersecurity Managers
• Information Security Managers
• Security Architects
• Enterprise Architects
• AI and Machine Learning Leaders
• AI Governance Professionals
• Risk Management Professionals
• Governance, Risk, and Compliance (GRC) Professionals
• Data Protection and Privacy Professionals
• SOC and Incident Response Leaders
• IT Governance Managers
• Digital Transformation Leaders
• Technology Risk Managers
• Senior IT and Security Professionals preparing for leadership responsibilities

Prerequisites

• General knowledge of information technology and enterprise systems.
• Basic understanding of cybersecurity concepts, risks, threats, and security controls.
• Familiarity with organizational risk management or IT governance concepts.
• General awareness of cloud computing, data management, and digital transformation.
• Basic awareness of artificial intelligence or generative AI is helpful but not mandatory.
• Previous programming or machine learning development experience is not required.

 

Course Outline

Day 1 – AI, Cybersecurity, and the Role of the AI Security Leader

Module 1: The Emerging AI Security Landscape

• Evolution of artificial intelligence in the enterprise
• AI, machine learning, deep learning, and generative AI
• Large language models and foundation models
• AI assistants, copilots, and autonomous AI agents
• Enterprise AI use cases
• AI adoption and organizational transformation
• Opportunities and security implications of enterprise AI

Module 2: The AI Chief Security Officer Role

• Evolution of security leadership in the AI era
• Responsibilities of the AI security leader
• Relationship between CSO, CISO, CIO, CTO, data, privacy, and AI leadership
• Security leadership and business alignment
• Establishing organizational accountability for AI security
• AI security operating models
• Building cross-functional AI governance

Module 3: Understanding the AI Technology Stack

• AI applications and interfaces
• Models and model services
• Training, fine-tuning, and inference
• Retrieval-Augmented Generation (RAG)
• Embeddings and vector databases
• AI APIs and integrations
• AI agents and tools
• Cloud and infrastructure dependencies
• AI data pipelines
• Understanding the end-to-end AI attack surface

Module 4: AI Threat Landscape

• Traditional cybersecurity threats affecting AI
• AI-specific security threats
• Prompt injection
• Jailbreaking and guardrail bypass
• Sensitive information disclosure
• Model manipulation
• Data poisoning
• Model theft and extraction
• Adversarial attacks
• AI-generated cyber threats
• Shadow AI and unauthorized AI usage

 

Day 2 – AI Governance, Risk Management, and Compliance

Module 5: Enterprise AI Security Governance

• Principles of AI governance
• AI security governance structures
• Roles, responsibilities, and accountability
• AI acceptable-use policies
• AI system inventory and classification
• Approval processes for AI applications
• Governance of public and enterprise generative AI
• Human oversight and accountability
• Responsible and trustworthy AI principles

Module 6: AI Risk Management

• Understanding AI risk
• Identifying AI assets and business dependencies
• AI risk identification
• Risk likelihood and impact
• AI risk classification and prioritization
• AI risk registers
• Risk treatment strategies
• Residual risk and risk acceptance
• Integration with enterprise risk management

Module 7: AI Threat Modeling and Security Assessment

• AI system architecture review
• Identifying assets and trust boundaries
• AI attack surface mapping
• Threat scenarios
• Data, model, application, infrastructure, and user risks
• Threat modeling for generative AI applications
• Security assessment of AI agents
• Prioritizing security controls

Module 8: AI Compliance, Privacy, and Regulatory Considerations

• AI security and regulatory landscape
• Data privacy considerations
• Personal and sensitive information in AI systems
• Data sovereignty and residency
• AI transparency and accountability
• Documentation and auditability
• AI governance standards and frameworks
• Integrating security, privacy, compliance, and responsible AI

 

Day 3 – Securing AI Systems, Data, Models, and Applications

Module 9: Secure AI Architecture

• Security-by-design for AI systems
• Defense-in-depth for AI
• Zero Trust principles for AI environments
• Network and infrastructure security
• Secure AI APIs
• Authentication and authorization
• Secrets and credential management
• Environment segregation
• Logging and monitoring
• Security architecture for enterprise AI solutions

Module 10: AI Data Security

• AI data lifecycle
• Training and inference data security
• Data classification
• Data minimization
• Sensitive data protection
• Encryption and key management
• Data access controls
• Data leakage prevention
• Securing RAG knowledge sources
• Protecting vector databases and embeddings

Module 11: Model and Generative AI Security

• Model security fundamentals
• Protecting model artifacts
• Prompt injection defenses
• Input and output validation
• Guardrails and content controls
• Model access controls
• Model extraction and theft prevention
• Adversarial machine learning considerations
• Securing LLM-based applications
• Monitoring model behavior

Module 12: Securing AI Agents and Integrations

• Understanding agentic AI security
• AI agent permissions and privileges
• Tool and API access
• Agent identity and authentication
• Human-in-the-loop controls
• Preventing unauthorized agent actions
• Managing autonomous decision-making risks
• Securing plugins, connectors, and integrations
• Monitoring AI agent activities
• Containment and kill-switch considerations

 

Day 4 – AI Security Operations, Incident Response, and Third-Party Risk

Module 13: AI-Enabled Security Operations

• AI applications in cybersecurity
• AI-assisted threat detection
• Security analytics
• Intelligent alert prioritization
• AI-assisted vulnerability management
• Threat intelligence enrichment
• AI-assisted investigation
• Security automation
• Benefits and risks of AI-enabled SOC operations
• Maintaining human oversight

Module 14: Monitoring and Detecting AI Security Threats

• AI security telemetry
• Logging requirements
• Detecting abnormal AI behavior
• Monitoring prompts and outputs
• Detecting misuse and abuse
• Data leakage detection
• Model and application monitoring
• AI security dashboards
• Security metrics and indicators
• Continuous AI security monitoring

Module 15: AI Security Incident Response

• Defining AI security incidents
• AI-specific incident scenarios
• Incident identification and classification
• Containment strategies
• Compromised AI applications
• Data exposure through AI
• Prompt injection incidents
• Model or agent compromise
• Evidence preservation
• Recovery and remediation
• Post-incident review and lessons learned

Module 16: Third-Party and AI Supply-Chain Security

• AI vendor risk management
• Evaluating AI service providers
• Cloud AI security considerations
• Model provenance
• Open-source models and dependencies
• Third-party datasets
• AI APIs and external services
• Software and model supply-chain risks
• Contractual security requirements
• Continuous third-party monitoring
• Exit and transition considerations

 

Day 5 – AI Security Leadership, Strategy, and Enterprise Roadmap

Module 17: Secure AI Lifecycle Management

• Integrating security throughout the AI lifecycle
• AI project security requirements
• Secure data acquisition and preparation
• Secure model development and integration
• AI security testing
• Pre-deployment security validation
• Deployment controls
• Continuous monitoring
• Change and configuration management
• Model retirement and secure decommissioning

Module 18: Building an Enterprise AI Security Program

• Defining AI security vision
• AI security program objectives
• Establishing governance structures
• AI security policies and standards
• Security control frameworks
• Roles and responsibilities
• AI security capability maturity
• Resource and technology requirements
• Building organizational AI security awareness
• Continuous improvement

Module 19: AI Security Metrics and Executive Reporting

• Establishing AI security KPIs and KRIs
• AI risk dashboards
• Security maturity indicators
• Incident and vulnerability metrics
• Third-party AI risk metrics
• Measuring policy compliance
• Reporting AI risks to executive leadership
• Communicating technical AI risks in business terms
• Board-level AI security reporting
• Supporting risk-based decision-making

Module 20: Developing the AI Security Strategy and Roadmap

• Assessing the organization’s current AI security posture
• Identifying strategic gaps
• Prioritizing AI security initiatives
• Quick wins versus long-term initiatives
• Developing short-, medium-, and long-term priorities
• Aligning security investments with business objectives
• Building the AI Security Roadmap
• Defining ownership and accountability
• Establishing continuous governance and improvement
• Creating an enterprise AI security action plan