AI+ Security Level 1™ – Course Outline
Program Overview
The AI+ Security Level 1™ certification is a foundational cybersecurity and Artificial Intelligence (AI) security program designed to equip learners with essential knowledge and practical skills to understand, identify, and respond to modern cyber threats in AI-driven and digital environments.
As organizations increasingly adopt AI systems, cloud platforms, and automated digital infrastructure, cybersecurity risks have become more complex, intelligent, and adaptive. This program introduces participants to the intersection of Artificial Intelligence and cybersecurity, focusing on how AI is used both to strengthen security systems and how it is also targeted by cyber threats.
The course provides a strong foundation in cybersecurity principles, including network security, threat detection, incident response, identity management, encryption, and risk mitigation. It also introduces AI-powered security concepts such as anomaly detection, behavioral analytics, automated threat response, and security monitoring systems.
Participants will gain practical awareness of how Security Operations Centers (SOCs) function, how cyberattacks are detected and prevented, and how AI enhances real-time security decision-making. The program emphasizes hands-on understanding, scenario-based learning, and real-world cyber threat simulations.
By the end of the program, learners will be able to understand core cybersecurity principles and apply AI-enhanced security concepts to improve organizational cyber resilience.
Course Objectives
• Understand core principles of cybersecurity and information security
• Identify common cyber threats, vulnerabilities, and attack vectors
• Understand how AI is used in modern cybersecurity systems
• Apply basic threat detection and incident response concepts
• Understand network security fundamentals and defense mechanisms
• Recognize phishing, malware, ransomware, and social engineering attacks
• Learn the basics of encryption and secure communication methods
• Understand Security Operations Center (SOC) functions and workflows
• Apply AI concepts for anomaly detection and threat monitoring
• Develop foundational awareness of cybersecurity governance and risk management
Target Audience
• Beginners in cybersecurity and IT security
• IT support and technical staff
• System administrators and network support professionals
• Students and graduates in IT, computer science, or engineering
• Business professionals seeking cybersecurity awareness
• AI beginners interested in security applications
• Government and public sector employees
• Healthcare, finance, and enterprise staff handling sensitive data
• Professionals transitioning into cybersecurity roles
• Anyone interested in foundational cyber defense and AI security
Course Duration
• Instructor-Led: 5 days (live or virtual)
• Self-Paced: 40 hours of content
Assessment
• Module-based quizzes covering cybersecurity and AI security fundamentals
• Case study analysis of real-world cyber incidents and breaches
• Practical exercises on identifying threats and vulnerabilities
• Hands-on simulations of phishing detection and incident response
• Scenario-based assessments for cyberattack response decision-making
• Final capstone project demonstrating a basic AI-supported cybersecurity solution (e.g., threat detection workflow, security monitoring dashboard, or incident response simulation model)
Certification
Upon successful completion of all assessments and the final capstone project, participants will be awarded the AI+ Security Level 1™ Certification.
This certification validates the learner’s foundational knowledge of cybersecurity principles and AI-enhanced security concepts, preparing them for advanced cybersecurity and AI security specialization pathways.
Training Methodology
• Instructor-led virtual or classroom training sessions
• Interactive lectures covering cybersecurity fundamentals and AI security concepts
• Real-world case studies of cyberattacks and security breaches
• Hands-on labs for threat identification and basic security operations
• Scenario-based learning simulating cyberattack situations
• Guided exercises on incident response and risk identification
• Security awareness simulations (phishing, malware, and social engineering)
• Continuous engagement through quizzes, discussions, and practical security drills
Course Modules
Module 1: Introduction to Cybersecurity
• Definition and scope of cybersecurity
• Key cybersecurity concepts
• CIA Triad (Confidentiality, Integrity, Availability)
• Cybersecurity frameworks and standards (NIST, ISO/IEC 27001)
• Cybersecurity laws and regulations (e.g., GDPR, HIPAA)
• Importance of cybersecurity in modern enterprises
• Careers in cybersecurity
Module 2: Operating System Fundamentals
• Core OS functions (memory management, process management)
• User accounts and privileges
• Access control mechanisms (ACLs, DAC, MAC)
• OS security features and configurations
• OS hardening (patching, disabling unnecessary services)
• Virtualization and container security considerations
• Secure boot and secure remote access
• OS vulnerabilities and mitigations
Module 3: Networking Fundamentals
• Network topologies and protocols (TCP/IP, OSI model)
• Network devices and their roles (routers, switches, firewalls)
• Network security devices (firewalls, IDS/IPS)
• Network segmentation and zoning
• Wireless network security (WPA2, WEP vulnerabilities)
• VPN technologies and use cases
• Network address translation (NAT)
• Basic network troubleshooting
Module 4: Threats, Vulnerabilities, and Exploits
• Types of threat actors (script kiddies, hacktivists, nation-states)
• Threat hunting methodologies using AI
• AI tools for threat hunting (SIEM, IDS/IPS)
• Open-source intelligence (OSINT) techniques
• Introduction to vulnerabilities
• SDLC and security integration with AI
• Zero-day attacks and patch management strategies
• Vulnerability scanning tools and techniques using AI
• Exploiting vulnerabilities (hands-on labs)
Module 5: Understanding AI and ML
• Introduction to AI
• Types and applications of AI
• Identifying and mitigating risks in real life
• Building a resilient security infrastructure with AI
• Enhancing digital defenses using CSAI
• Application of machine learning in cybersecurity
• Protecting sensitive data and systems from cyber threats
• Threat intelligence and threat hunting concepts
Module 6: Python Programming Fundamentals
• Introduction to Python programming
• Python libraries overview
• Python for cybersecurity applications
• AI scripting for cybersecurity automation
• Data analysis and manipulation using Python
• Developing security tools with Python
Module 7: Applications of AI in Cybersecurity
• Machine learning in cybersecurity
• Anomaly detection and behavior analysis
• Dynamic defense using machine learning
• Email threat detection using ML
• Phishing detection with AI
• Malware detection and mitigation using AI
• AI-based user authentication
• Penetration testing with AI
Module 8: Incident Response and Disaster Recovery
• Incident response process (identification, containment, eradication, recovery)
• Incident response lifecycle
• Incident detection and analysis
• Post-incident activities
• Digital forensics and evidence collection
• Disaster recovery planning (business continuity, backups)
• Penetration testing and vulnerability assessment
• Legal and regulatory considerations
Module 9: Open Source Security Tools
• Introduction to open-source security tools
• Popular open-source security tools
• Benefits and challenges of open-source tools
• Implementation in organizations
• Community support and resources
• Network scanning tools
• Open-source SIEM tools
• Packet filtering firewalls
• Ethical password hashing and cracking tools
• Open-source forensics tools
Module 10: Securing the Future
• Emerging cyber threats and trends
• AI and machine learning in cybersecurity
• Blockchain for security
• IoT security
• Cloud security
• Quantum computing impact on security
• Critical infrastructure cybersecurity
• Cryptography and secure hashing
• Continuous security monitoring and improvement
Module 11: Capstone Project
• Project introduction
• AI use cases in cybersecurity
• Development and implementation
• Final presentation and evaluation
Optional Module: AI Agents in Cybersecurity
• What are AI agents
• Key capabilities of AI agents
• Applications and trends in cybersecurity
• How AI agents work
• Core characteristics of AI agents
• Types of AI agents
