The SecAI+ Preparation Training Course is designed to prepare cybersecurity and IT professionals for security roles involving the practical application, governance, and protection of artificial intelligence technologies. The course combines foundational AI concepts with cybersecurity principles to help participants understand how AI can strengthen security operations while also introducing new risks, vulnerabilities, and attack surfaces.
Participants will explore AI-enabled threat detection, security automation, data protection, adversarial AI, prompt-related threats, AI system vulnerabilities, identity and access considerations, secure AI deployment, governance, risk management, and incident response. The program also addresses the responsible and secure use of generative AI and machine learning within enterprise environments.
The course is structured as a certification-preparation program, combining conceptual knowledge with practical security scenarios and review of key SecAI+ knowledge areas. It is suitable for professionals seeking to strengthen their AI security competencies and prepare for a SecAI+-aligned certification examination.
Duration 5 Days – 35 hrs
Objectives
- Explain fundamental artificial intelligence, machine learning, and generative AI concepts relevant to cybersecurity.
- Understand the relationship between AI technologies and modern cybersecurity operations.
- Identify security risks and attack surfaces associated with AI and machine learning systems.
- Recognize common threats against AI models, applications, data, and supporting infrastructure.
- Understand adversarial AI techniques and appropriate defensive controls.
- Apply security principles throughout the AI system lifecycle.
- Explain how AI can support threat detection, monitoring, investigation, and incident response.
- Understand AI-assisted security automation and orchestration.
- Identify risks involving prompts, sensitive information, model outputs, and generative AI applications.
- Apply identity, access control, data protection, and infrastructure security principles to AI environments.
- Understand AI security governance, risk management, compliance, privacy, and ethical considerations.
- Evaluate AI-related security incidents and appropriate response strategies.
- Interpret practical AI security scenarios and select appropriate security controls.
- Strengthen knowledge required for SecAI+-aligned certification preparation.
Target Audience
- Cybersecurity Professionals
- Security Analysts
- SOC Analysts
- Security Engineers
- Security Administrators
- Network Security Professionals
- Incident Response Personnel
- Threat Intelligence Analysts
- Vulnerability Management Professionals
- Security Operations Professionals
- Cloud Security Professionals
- DevSecOps Engineers
- AI/ML Engineers with security responsibilities
- IT Professionals transitioning into AI security
- Information Security Managers
- Risk and Compliance Professionals
- Security Architects
- Professionals preparing for SecAI+-related certification
Prerequisites
- Basic knowledge of cybersecurity concepts and terminology.
- Familiarity with networks, operating systems, applications, and cloud environments.
- Basic understanding of common cybersecurity threats and security controls.
- General knowledge of identity and access management, encryption, vulnerability management, and incident response.
- Basic familiarity with artificial intelligence or machine learning is beneficial but not mandatory.
- Previous cybersecurity training or professional experience is recommended for certification preparation.
Course Outline
Day 1 – AI Foundations and the AI Security Landscape
Module 1: Introduction to Artificial Intelligence and Cybersecurity
- Artificial intelligence fundamentals
- Machine learning concepts
- Deep learning overview
- Generative AI and large language models
- AI models, algorithms, and datasets
- Training, validation, inference, and deployment
- AI applications in enterprise environments
- Role of AI in modern cybersecurity
Module 2: AI and Machine Learning Architecture
- AI/ML system components
- Data pipelines
- Model training environments
- Model repositories and APIs
- AI application architecture
- Cloud-based AI services
- AI integrations and dependencies
- Understanding the AI technology stack
Module 3: AI Security Threat Landscape
- AI-specific attack surfaces
- Threat actors and motivations
- AI-enabled cyberattacks
- Attacks targeting AI systems
- Model and data vulnerabilities
- Supply chain considerations
- Emerging AI security threats
- Security implications of generative AI
Module 4: Security Principles for AI Systems
- Confidentiality, integrity, and availability
- Authentication and authorization
- Least privilege
- Zero Trust concepts
- Defense in depth
- Secure configuration
- Segmentation and isolation
- Security-by-design for AI
Day 2 – AI Threats, Vulnerabilities, and Adversarial Attacks
Module 5: Adversarial Artificial Intelligence
- Introduction to adversarial AI
- Adversarial examples
- Evasion attacks
- Model manipulation
- Model extraction
- Model inversion
- Membership inference
- Defensive strategies against adversarial attacks
Module 6: Data and Model Security
- Training data security
- Data poisoning
- Data integrity attacks
- Model poisoning
- Model theft
- Model tampering
- Sensitive information exposure
- Protecting AI datasets and model assets
Module 7: Generative AI and LLM Security
- Generative AI security architecture
- Prompt injection
- Direct and indirect prompt injection
- Jailbreaking
- Sensitive data disclosure
- Insecure output handling
- Excessive agency
- Model hallucinations and security implications
- Securing AI agents and AI-enabled applications
Module 8: AI Supply Chain and Application Security
- Third-party AI models
- Open-source models
- AI libraries and dependencies
- APIs and plugins
- Software supply chain risks
- Model provenance
- Dependency vulnerabilities
- Secure AI integration practices
Day 3 – Using AI for Cybersecurity Operations
Module 9: AI-Enabled Threat Detection
- AI in security monitoring
- Behavioral analytics
- Anomaly detection
- Network threat detection
- Endpoint threat detection
- Malware identification
- User and entity behavior analytics
- Identifying suspicious patterns
Module 10: AI in Security Operations Centers
- AI-assisted SOC operations
- Security event analysis
- Alert enrichment
- Alert prioritization
- False-positive reduction
- Security investigation support
- AI-assisted threat hunting
- Security analyst augmentation
Module 11: AI for Threat Intelligence and Vulnerability Management
- AI-assisted threat intelligence
- Threat data correlation
- Indicators of compromise
- Attack pattern identification
- Vulnerability identification
- Vulnerability prioritization
- Risk-based remediation
- AI-assisted security research
Module 12: Security Automation and Incident Response
- AI-driven security automation
- Security orchestration
- Automated response workflows
- Incident classification
- Incident triage
- Root cause investigation
- Containment and remediation
- Human oversight in automated response
Day 4 – Securing AI Infrastructure, Data, and Enterprise Deployment
Module 13: Identity and Access Security for AI
- Authentication for AI systems
- Authorization controls
- Role-based access control
- Privileged access
- Service identities
- API authentication
- Secrets and credential management
- Least-privilege access for AI resources
Module 14: AI Data Protection and Privacy
- AI data classification
- Personally identifiable information
- Sensitive and confidential information
- Data minimization
- Encryption
- Data masking and anonymization
- Data retention
- Privacy considerations for AI systems
Module 15: Cloud and Infrastructure Security for AI
- AI workloads in cloud environments
- Compute and storage security
- Containerized AI workloads
- Network segmentation
- API security
- Logging and monitoring
- Secure AI development environments
- AI infrastructure hardening
Module 16: Secure AI Development Lifecycle
- Security requirements
- Threat modeling for AI systems
- Secure design principles
- Secure development practices
- AI application testing
- Vulnerability identification
- Model validation and security testing
- Deployment and continuous monitoring
Day 5 – AI Governance, Risk Management, and SecAI+ Preparation
Module 17: AI Governance and Responsible AI
- AI governance fundamentals
- Responsible AI principles
- Transparency and explainability
- Accountability
- Fairness and bias
- Human oversight
- AI usage policies
- Enterprise AI governance structures
Module 18: AI Risk Management and Compliance
- AI risk identification
- Risk assessment
- Risk treatment
- Security and privacy risks
- Third-party AI risk
- Regulatory considerations
- Compliance requirements
- AI security control frameworks
Module 19: AI Security Incident Management
- Identifying AI-related incidents
- AI incident classification
- Investigation procedures
- Evidence collection
- Containment strategies
- Model isolation and recovery
- Post-incident activities
- Lessons learned and control improvements
Module 20: SecAI+ Knowledge Review and Preparation
- Review of key AI security concepts
- AI threat and vulnerability review
- Security operations review
- AI infrastructure and data security review
- Governance and risk review
- Scenario-based security questions
- Interpreting certification-style scenarios
- Exam preparation strategies
- Key terminology and concepts review
- Final SecAI+ preparation checklist
