The FCP in SecAI+ Training Course is designed for cybersecurity professionals who want to develop practical knowledge and skills in applying artificial intelligence, machine learning, automation, and advanced analytics to modern security operations.
The course focuses on the use of AI-assisted security technologies for threat detection, investigation, incident response, vulnerability management, behavioral analysis, and security operations. Participants will learn how AI can enhance traditional cybersecurity controls by analyzing large volumes of security data, identifying suspicious patterns, prioritizing threats, and supporting faster and more informed security decisions.
The training also addresses the security risks associated with AI systems, including adversarial attacks, data poisoning, prompt-based threats, model vulnerabilities, and responsible AI considerations. Through a structured progression from cybersecurity and AI fundamentals to AI-enhanced security operations, participants will develop competencies relevant to the FCP in SecAI+ certification track and AI-driven cybersecurity environments.
Note: Certification names, requirements, and associated exams can change. The exact current FCP in SecAI+ certification requirements should be verified against the certification provider before scheduling an exam.
Duration 5 Days – 35 hrs.
Objectives
- Understand the role of artificial intelligence and machine learning in cybersecurity.
- Explain how AI technologies can enhance security monitoring and threat detection.
- Understand AI-assisted threat intelligence and security analytics.
- Analyze security events using behavioral and anomaly-detection concepts.
- Apply AI concepts to Security Operations Center (SOC) activities.
- Understand AI-assisted incident investigation and response.
- Use automation concepts to improve security operations and response workflows.
- Understand AI applications in vulnerability and risk management.
- Recognize common attacks and security risks targeting AI systems.
- Understand adversarial AI, model manipulation, and data poisoning.
- Identify security considerations for generative AI and Large Language Models (LLMs).
- Apply appropriate security controls for protecting AI systems and data.
- Understand responsible and secure use of AI in cybersecurity environments.
- Integrate AI-assisted security capabilities into an organization’s broader cybersecurity strategy.
- Prepare for concepts and competencies associated with the FCP in SecAI+ certification track.
Target Audience
- Cybersecurity Professionals
- Security Engineers
- Security Analysts
- SOC Analysts
- SOC Engineers
- Network Security Engineers
- Security Administrators
- Incident Response Professionals
- Threat Intelligence Analysts
- Threat Hunters
- Vulnerability Management Professionals
- Information Security Officers
- Security Architects
- Cybersecurity Consultants
- IT Security Administrators
- Technical Professionals responsible for AI-enabled security solutions
- Professionals preparing for the FCP in SecAI+ certification track
Prerequisites
- Fundamental knowledge of cybersecurity concepts and terminology.
- Basic understanding of network security, firewalls, authentication, and access control.
- Familiarity with common cyber threats, vulnerabilities, malware, phishing, and attack techniques.
- Basic understanding of security monitoring and incident response.
- General familiarity with Security Information and Event Management (SIEM) and SOC environments is recommended.
- Basic knowledge of artificial intelligence and machine learning is helpful but not mandatory.
- Previous experience with enterprise security technologies is recommended for participants seeking certification.
Course Outline
Day 1 – Cybersecurity and Artificial Intelligence Foundations
Module 1: Introduction to SecAI+
- Evolution of cybersecurity technologies
- Traditional security versus AI-assisted security
- Role of AI in modern cybersecurity
- Security challenges addressed by AI
- AI-enabled cybersecurity use cases
- Understanding the SecAI+ security landscape
Module 2: Artificial Intelligence and Machine Learning Fundamentals
- Artificial intelligence fundamentals
- Machine learning concepts
- Supervised and unsupervised learning
- Deep learning fundamentals
- Natural Language Processing
- Generative AI and Large Language Models
- AI models and training data
- AI inference and decision-making
Module 3: Cybersecurity Data and AI Analytics
- Security telemetry and cybersecurity datasets
- Network, endpoint, identity, and application data
- Log collection and normalization
- Data quality considerations
- Feature identification
- Pattern recognition
- Security analytics
- AI-assisted correlation and prioritization
Day 2 – AI-Driven Threat Detection and Security Operations
Module 4: AI-Assisted Threat Detection
- Signature-based versus behavioral detection
- Machine learning-based threat detection
- Anomaly detection
- Behavioral analytics
- User and Entity Behavior Analytics
- Detecting suspicious activities
- Malware and malicious behavior identification
- Reducing false positives
- Detection confidence and risk scoring
Module 5: AI in Security Operations Centers
- Modern SOC architecture
- AI-assisted SOC operations
- Security event monitoring
- Alert enrichment
- Alert correlation
- Event prioritization
- AI-assisted investigation
- Security analyst decision support
- Improving SOC efficiency using AI
Module 6: AI-Powered Threat Intelligence
- Threat intelligence fundamentals
- Indicators of Compromise
- Tactics, techniques, and procedures
- Threat intelligence enrichment
- AI-assisted intelligence analysis
- Identifying emerging threats
- Threat classification and prioritization
- Integrating threat intelligence with security operations
Day 3 – Incident Response, Automation, and Threat Hunting
Module 7: AI-Assisted Incident Detection and Response
- Incident response lifecycle
- AI-assisted incident identification
- Incident classification
- Root cause investigation
- Attack timeline reconstruction
- Automated evidence correlation
- Incident prioritization
- Containment and remediation recommendations
- Human oversight in AI-assisted response
Module 8: Security Automation and Orchestration
- Cybersecurity automation fundamentals
- Security orchestration concepts
- Automated response workflows
- Playbooks and response actions
- Integration with security technologies
- Automated enrichment
- Automated containment
- Benefits and risks of security automation
- Human-in-the-loop security operations
Module 9: AI-Assisted Threat Hunting
- Threat hunting principles
- Hypothesis-driven threat hunting
- Behavioral indicators
- Hunting for anomalous activity
- AI-assisted pattern discovery
- Identifying hidden attack relationships
- Using historical security data
- Prioritizing threat-hunting investigations
Day 4 – Securing AI Systems and Managing AI Threats
Module 10: AI Security Threat Landscape
- Understanding attacks against AI systems
- AI attack surfaces
- Model vulnerabilities
- Adversarial machine learning
- Evasion attacks
- Data poisoning
- Model manipulation
- Model extraction
- AI supply-chain risks
Module 11: Generative AI and LLM Security
- Generative AI security fundamentals
- Large Language Model security risks
- Prompt injection
- Indirect prompt injection
- Sensitive information disclosure
- Insecure AI-generated outputs
- AI hallucination and security implications
- Data leakage risks
- Securing AI-enabled applications
- Access control for AI services
Module 12: Protecting AI Infrastructure and Data
- AI security architecture
- Protecting training and inference environments
- AI data security
- Identity and access management
- Encryption and data protection
- API security
- Monitoring AI systems
- Model integrity
- Secure AI lifecycle
- AI security governance
Day 5 – Advanced SecAI+, Governance, and Certification Preparation
Module 13: AI for Vulnerability and Risk Management
- AI-assisted vulnerability identification
- Vulnerability prioritization
- Risk-based vulnerability management
- Threat exposure analysis
- Attack-path analysis
- Predictive security analytics
- AI-assisted remediation recommendations
- Continuous security posture assessment
Module 14: Responsible AI and Cybersecurity Governance
- Responsible AI principles
- AI governance
- Privacy and data protection
- Transparency and explainability
- AI bias and cybersecurity implications
- Human accountability
- Security policies for AI adoption
- AI risk management
- Regulatory and compliance considerations
- Establishing secure AI governance
Module 15: Enterprise SecAI+ Architecture and Integration
- Designing AI-enhanced security operations
- Integrating AI into existing security architecture
- Network and endpoint security integration
- Cloud security considerations
- Identity security integration
- SIEM and security analytics integration
- Security automation integration
- Operational considerations
- Measuring effectiveness of AI-assisted security
Module 16: FCP in SecAI+ Certification Review
- Review of key SecAI+ concepts
- AI and machine learning security concepts
- AI-assisted detection and response review
- Security operations concepts
- AI threat and vulnerability review
- Generative AI security review
- AI governance and risk review
- Certification-focused knowledge review
- Scenario-based review questions
- Final certification preparation guidance
