The Certification FCP in SecAI+ Training Course is a comprehensive cybersecurity and artificial intelligence security program designed to develop the knowledge and practical skills required to understand, implement, manage, and secure AI-enabled environments.
The course combines fundamental cybersecurity principles with emerging concepts in artificial intelligence security, including AI-driven threat detection, machine learning security, generative AI risks, data protection, identity and access security, vulnerability management, security monitoring, incident response, and governance.
Participants will examine how artificial intelligence can enhance cybersecurity operations while also introducing new attack surfaces and security risks. The course covers both perspectives: using AI to strengthen cybersecurity and protecting AI systems from cyber threats.
It is suitable as a structured preparation program for cybersecurity professionals seeking to strengthen their knowledge of Secure AI (SecAI), AI-assisted security operations, and modern enterprise cybersecurity practices.
Duration 5 Days – 35 hrs.
Objectives
- Explain the fundamental concepts of cybersecurity, artificial intelligence, machine learning, and Secure AI.
- Understand how AI technologies are being applied within modern cybersecurity environments.
- Identify security risks and attack surfaces associated with AI and machine learning systems.
- Recognize common threats targeting AI models, applications, data, and infrastructure.
- Understand AI-driven threat detection and security analytics.
- Apply fundamental principles for protecting AI applications and workloads.
- Understand identity, authentication, authorization, and privileged access considerations for AI systems.
- Identify risks associated with generative AI, large language models, and AI-powered applications.
- Understand prompt injection, data leakage, model manipulation, and adversarial AI threats.
- Apply data security and privacy principles to AI environments.
- Understand vulnerability management and security hardening for AI-enabled infrastructure.
- Explain how AI can support security operations, monitoring, threat intelligence, and incident response.
- Understand responsible AI, AI governance, compliance, and risk-management principles.
- Analyze common Secure AI scenarios and determine appropriate security controls.
- Strengthen knowledge relevant to professional cybersecurity and Secure AI certification examinations.
Target Audience
- Cybersecurity Professionals
- Security Analysts
- SOC Analysts
- Security Engineers
- Network Security Engineers
- Information Security Officers
- IT Security Administrators
- Systems Administrators
- Cloud Security Professionals
- Security Operations Personnel
- Incident Response Team Members
- Vulnerability Management Professionals
- AI and Machine Learning Professionals
- AI Security Engineers
- DevSecOps Engineers
- IT Risk and Compliance Professionals
- Security Architects
- Technical Consultants
- IT Professionals transitioning into AI security roles
- Professionals preparing for Secure AI or cybersecurity certification examinations
Prerequisites
- Basic understanding of information technology and computer systems.
- Fundamental knowledge of networking concepts such as TCP/IP, firewalls, protocols, and network services.
- Basic understanding of cybersecurity concepts including threats, vulnerabilities, authentication, and access control.
- Familiarity with operating systems, applications, and enterprise IT environments.
- Basic awareness of cloud computing is beneficial.
- Basic knowledge of artificial intelligence and machine learning is helpful but not mandatory.
- Previous cybersecurity experience is recommended for participants pursuing certification-level knowledge.
Course Outline
Day 1 – Cybersecurity and Secure AI Foundations
Module 1: Introduction to Modern Cybersecurity
- Cybersecurity principles and objectives
- Confidentiality, integrity, and availability
- Modern cyber threat landscape
- Threat actors and attack motivations
- Attack surfaces and attack vectors
- Vulnerabilities, exploits, and security controls
- Defense-in-depth concepts
- Zero Trust security principles
Module 2: Artificial Intelligence and Machine Learning Fundamentals
- Introduction to artificial intelligence
- Machine learning fundamentals
- Deep learning concepts
- Generative AI
- Large language models
- AI models and inference
- Training data and datasets
- AI applications in enterprise environments
Module 3: Introduction to Secure AI (SecAI)
- Definition and scope of Secure AI
- AI for cybersecurity versus cybersecurity for AI
- AI security architecture
- AI attack surfaces
- AI system lifecycle
- Security responsibilities across the AI lifecycle
- Emerging AI security challenges
- Secure AI use cases
Module 4: AI in Cybersecurity Operations
- AI-assisted cybersecurity
- Automated threat identification
- Behavioral analytics
- Anomaly detection
- AI-assisted malware detection
- AI-based network monitoring
- Security event correlation
- Benefits and limitations of AI-driven security
Day 2 – AI Threats, Attacks, and Security Controls
Module 5: Threats Against AI Systems
- AI-specific threat landscape
- Adversarial machine learning
- Model manipulation
- Model poisoning
- Training data poisoning
- Model extraction
- Model inversion
- Evasion attacks
- AI supply-chain risks
Module 6: Generative AI and LLM Security
- Generative AI security architecture
- Large language model security risks
- Prompt injection
- Indirect prompt injection
- Jailbreaking techniques and risks
- Sensitive information disclosure
- Insecure output handling
- Excessive agency
- AI hallucination and security implications
- LLM application security considerations
Module 7: Protecting AI Models and Applications
- Secure AI development principles
- Model access controls
- AI application authentication
- API security
- Input validation
- Output validation
- Model integrity protection
- Secure model deployment
- Runtime security controls
- AI application monitoring
Module 8: Identity and Access Security
- Identity and access management fundamentals
- Authentication and authorization
- Role-based access control
- Least privilege
- Privileged access management
- Service identities
- API credentials and secrets
- Machine-to-machine authentication
- Zero Trust access for AI resources
Day 3 – Data, Network, Cloud, and Infrastructure Security
Module 9: AI Data Security and Privacy
- Importance of data in AI security
- Data classification
- Sensitive and regulated information
- Data-at-rest protection
- Data-in-transit protection
- Encryption fundamentals
- Data loss prevention
- Privacy risks associated with AI
- Data retention and disposal
- Protecting training and inference data
Module 10: Network Security for AI Environments
- Network segmentation
- Firewalls and security policies
- Intrusion detection and prevention
- Secure communication
- Network traffic inspection
- Application control
- DNS and web security
- Network-based threat detection
- Securing AI infrastructure connectivity
Module 11: Cloud and AI Workload Security
- Cloud security fundamentals
- Shared responsibility model
- Cloud-hosted AI workloads
- Securing AI services
- Containers and AI workloads
- API and service protection
- Cloud identity security
- Cloud configuration risks
- Hybrid and multi-cloud AI environments
Module 12: Vulnerability Management and Security Hardening
- Vulnerability identification
- Vulnerability assessment
- Risk-based prioritization
- Patch management
- Secure configuration
- System hardening
- AI application dependency risks
- Third-party libraries
- Software supply-chain security
- Continuous vulnerability management
Day 4 – AI-Enabled Security Operations and Incident Response
Module 13: Security Monitoring and Analytics
- Security monitoring fundamentals
- Logs and security telemetry
- Security information and event management
- AI-assisted event analysis
- Behavioral detection
- Threat correlation
- Detection engineering
- Indicators of compromise
- Alert prioritization
- Reducing false positives using AI
Module 14: AI-Driven Threat Intelligence
- Threat intelligence fundamentals
- Threat intelligence sources
- Indicators and behavioral intelligence
- AI-assisted intelligence analysis
- Threat classification
- Automated enrichment
- Threat hunting with AI
- Predictive security analytics
- Limitations of AI-generated intelligence
Module 15: Incident Detection and Response
- Incident response lifecycle
- Preparation
- Detection and analysis
- Containment
- Eradication
- Recovery
- Post-incident activities
- AI-assisted incident investigation
- Automated response
- Human oversight in automated security operations
Module 16: Security Automation and AI-Assisted SOC
- Security automation concepts
- Security orchestration
- Automated security workflows
- AI-assisted SOC operations
- Alert triage
- Case enrichment
- Automated investigation
- AI security copilots
- Human-in-the-loop security
- Risks of excessive automation
Day 5 – AI Governance, Risk Management, and Certification Preparation
Module 17: AI Governance and Responsible AI
- AI governance principles
- Responsible AI
- Transparency and explainability
- Accountability
- Fairness and bias
- Privacy considerations
- Human oversight
- AI lifecycle governance
- Organizational AI policies
- Secure and responsible AI adoption
Module 18: AI Security Risk Management
- AI risk identification
- Threat modeling for AI systems
- Risk assessment
- Risk likelihood and impact
- Security control selection
- Risk treatment
- Third-party AI risks
- Supply-chain considerations
- Continuous risk monitoring
- AI security maturity
Module 19: Secure AI Architecture and Enterprise Implementation
- Secure AI architecture principles
- Defense-in-depth for AI
- Secure data pipelines
- Identity architecture
- Network and workload protection
- Model protection
- Monitoring architecture
- Secure AI deployment lifecycle
- Integrating AI security with enterprise cybersecurity
- Building an organizational Secure AI strategy
Module 20: Certification Preparation and Domain Review
- Review of cybersecurity fundamentals
- Review of AI and machine learning security
- Review of AI-specific attacks
- Review of generative AI and LLM security
- Review of data protection concepts
- Review of identity and access security
- Review of network and cloud security
- Review of security operations
- Review of incident response
- Review of AI governance and risk management
- Certification-style scenario analysis
- Key concepts and terminology review
