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AI+ Security Level 1™
AT-2101
Empowering Cybersecurity with AIOur comprehensive course, AI+ Security level 1 offers professionals a thorough exploration of the integration of AI and Cybersecurity. Beginning with fundamental Python programming tailored for AI and Cybersecurity applications, participants delve into essential AI principles before applying machine learning techniques to detect and mitigate cyber threats, including email threats, malware, and network anomalies. Advanced topics such as user authentication using AI algorithms and the application of Generative Adversarial Networks (GANs) for Cybersecurity purposes are also covered, ensuring participants are equipped with cutting-edge knowledge. Practical application is emphasized throughout, culminating in a Capstone Project where attendees synthesize their skills to address real-world cybersecurity challenges, leaving them adept in leveraging AI to safeguard digital assets effectively.
Buy Exam Bundle Download Blueprint Find a Training Partner Download Executive SummaryPrerequisites
- Basic Python Programming: Familiarity with loops, functions, and variables.
- Basic Cybersecurity Knowledge: Understanding of CIA triad and common threats (e.g., malware, phishing).
- Basic Machine Learning Concepts: Awareness of fundamental machine learning concepts, not mandatory.
- Basic Networking: Understanding of IP addressing and TCP/IP protocols.
- Linux/Command Line Skills: Ability to navigate and use the CLI effectively.
Modules
11
Examination
1
50 MCQs
90 Minutes
Passing Score
70%
Recertification Requirements
AI CERTs requires recertification every year to keep your certification valid. Notifications will be sent three months before the due date, and candidates must follow the steps in the candidate handbook to complete the process.
Need Help? If you have any questions or need assistance with recertification, please reach out to our support team at support@aicerts.ai
Certification Modules
- 1.1 Definition and Scope of Cyber Security
- 1.2 Key Cybersecurity Concepts
- 1.3 CIA Triad (Confidentiality, Integrity, Availability)
- 1.4 Cybersecurity Frameworks and Standards (NIST, ISO/IEC27001)
- 1.5 Cyber Security Laws and Regulations (e.g., GDPR, HIPAA)
- 1.6 Importance of Cybersecurity in Modern Enterprises
- 1.7 Careers in Cyber Security
- 2.1 Core OS Functions (Memory Management, Process Management)
- 2.2 User Accounts and Privileges
- 2.3 Access Control Mechanisms (ACLs, DAC, MAC)
- 2.4 OS Security Features and Configurations
- 2.5 Hardening OS Security (Patching, Disabling
Unnecessary Services) - 2.6 Virtualization and Containerization Security
Considerations - 2.7 Secure Boot and Secure Remote Access
- 2.8 OS Vulnerabilities and Mitigations
- 3.1 Network Topologies and Protocols (TCP/IP, OSI Model)
- 3.2 Network Devices and Their Roles (Routers, Switches,
Firewalls) - 3.3 Network Security Devices (Firewalls, IDS/IPS)
- 3.4 Network Segmentation and Zoning
- 3.5 Wireless Network Security (WPA2, Open WEP
vulnerabilities) - 3.6 VPN Technologies and Use Cases
- 3.7 Network Address Translation (NAT)
- 3.8 Basic Network Troubleshooting
- 4.1 Types of Threat Actors (Script Kiddies, Hacktivists,
Nation-States) - 4.2 Threat Hunting Methodologies using AI
- 4.3 AI Tools for Threat Hunting (SIEM, IDS/IPS)
- 4.4 Open-Source Intelligence (OSINT) Techniques
- 4.5 Introduction to Vulnerabilities
- 4.6 Software Development Life Cycle (SDLC) and Security
Integration with AI - 4.7 Zero-Day Attacks and Patch Management Strategies
- 4.8 Vulnerability Scanning Tools and Techniques using AI
- 4.9 Exploiting Vulnerabilities (Hands-on Labs)
- 5.1 An Introduction to AI
- 5.2 Types and Applications of AI
- 5.3 Identifying and Mitigating Risks in Real-Life
- 5.4 Building a Resilient and Adaptive Security Infrastructure
with AI - 5.5 Enhancing Digital Defenses using CSAI
- 5.6 Application of Machine Learning in Cybersecurity
- 5.7 Safeguarding Sensitive Data and Systems Against
Diverse Cyber Threats - 5.8 Threat Intelligence and Threat Hunting Concepts
- 6.1 Introduction to Python Programming
- 6.2 Understanding of Python Libraries
- 6.3 Python Programming Language for Cybersecurity
Applications - 6.4 AI Scripting for Automation in Cybersecurity Tasks
- 6.5 Data Analysis and Manipulation Using Python
- 6.6 Developing Security Tools with Python
- 7.1 Understanding the Application of Machine Learning in
Cybersecurity - 7.2 Anomaly Detection to Behavior Analysis
- 7.3 Dynamic and Proactive Defense using Machine
Learning - 7.4 Utilizing Machine Learning for Email Threat Detection
- 7.5 Enhancing Phishing Detection with AI
- 7.6 Autonomous Identification and Thwarting of Email
Threats - 7.7 Employing Advanced Algorithms and AI in Malware
Threat Detection - 7.8 Identifying, Analyzing, and Mitigating Malicious
Software - 7.9 Enhancing User Authentication with AI Techniques
- 7.10 Penetration Testing with AI
- 8.1 Incident Response Process (Identification, Containment,
Eradication, Recovery) - 8.2 Incident Response Lifecycle
- 8.3 Preparing an Incident Response Plan
- 8.4 Detecting and Analyzing Incidents
- 8.5 Containment, Eradication, and Recovery
- 8.6 Post-Incident Activities
- 8.7 Digital Forensics and Evidence Collection
- 8.8 Disaster Recovery Planning (Backups, Business
Continuity) - 8.9 Penetration Testing and Vulnerability Assessments
- 8.10 Legal and Regulatory Considerations of Security
Incidents
- 9.1 Introduction to Open-Source Security Tools
- 9.2 Popular Open Source Security Tools
- 9.3 Benefits and Challenges of Using Open-Source Tools
- 9.4 Implementing Open Source Solutions in Organizations
- 9.5 Community Support and Resources
- 9.6 Network Security Scanning and Vulnerability Detection
- 9.7 Security Information and Event Management (SIEM)
Tools (Open-Source options) - 9.8 Open-Source Packet Filtering Firewalls
- 9.9 Password Hashing and Cracking Tools (Ethical Use)
- 9.10 Open-Source Forensics Tools
- 10.1 Emerging Cyber Threats and Trends
- 10.2 Artificial Intelligence and Machine Learning in
Cybersecurity - 10.3 Blockchain for Security
- 10.4 Internet of Things (IoT) Security
- 10.5 Cloud Security
- 10.6 Quantum Computing and its Impact on Security
- 10.7 Cybersecurity in Critical Infrastructure
- 10.8 Cryptography and Secure Hashing
- 10.9 Cyber Security Awareness and Training for Users
- 10.10 Continuous Security Monitoring and Improvement
- 11.1 Introduction
- 11.2 Use Cases: AI in Cybersecurity
- 11.3 Outcome Presentation
Certification Modules
- 1.1 Definition and Scope of Cyber Security
- 1.2 Key Cybersecurity Concepts
- 1.3 CIA Triad (Confidentiality, Integrity, Availability)
- 1.4 Cybersecurity Frameworks and Standards (NIST, ISO/IEC27001)
- 1.5 Cyber Security Laws and Regulations (e.g., GDPR, HIPAA)
- 1.6 Importance of Cybersecurity in Modern Enterprises
- 1.7 Careers in Cyber Security
- 2.1 Core OS Functions (Memory Management, Process Management)
- 2.2 User Accounts and Privileges
- 2.3 Access Control Mechanisms (ACLs, DAC, MAC)
- 2.4 OS Security Features and Configurations
- 2.5 Hardening OS Security (Patching, Disabling
Unnecessary Services) - 2.6 Virtualization and Containerization Security
Considerations - 2.7 Secure Boot and Secure Remote Access
- 2.8 OS Vulnerabilities and Mitigations
- 3.1 Network Topologies and Protocols (TCP/IP, OSI Model)
- 3.2 Network Devices and Their Roles (Routers, Switches,
Firewalls) - 3.3 Network Security Devices (Firewalls, IDS/IPS)
- 3.4 Network Segmentation and Zoning
- 3.5 Wireless Network Security (WPA2, Open WEP
vulnerabilities) - 3.6 VPN Technologies and Use Cases
- 3.7 Network Address Translation (NAT)
- 3.8 Basic Network Troubleshooting
- 4.1 Types of Threat Actors (Script Kiddies, Hacktivists,
Nation-States) - 4.2 Threat Hunting Methodologies using AI
- 4.3 AI Tools for Threat Hunting (SIEM, IDS/IPS)
- 4.4 Open-Source Intelligence (OSINT) Techniques
- 4.5 Introduction to Vulnerabilities
- 4.6 Software Development Life Cycle (SDLC) and Security
Integration with AI - 4.7 Zero-Day Attacks and Patch Management Strategies
- 4.8 Vulnerability Scanning Tools and Techniques using AI
- 4.9 Exploiting Vulnerabilities (Hands-on Labs)
- 5.1 An Introduction to AI
- 5.2 Types and Applications of AI
- 5.3 Identifying and Mitigating Risks in Real-Life
- 5.4 Building a Resilient and Adaptive Security Infrastructure
with AI - 5.5 Enhancing Digital Defenses using CSAI
- 5.6 Application of Machine Learning in Cybersecurity
- 5.7 Safeguarding Sensitive Data and Systems Against
Diverse Cyber Threats - 5.8 Threat Intelligence and Threat Hunting Concepts
- 6.1 Introduction to Python Programming
- 6.2 Understanding of Python Libraries
- 6.3 Python Programming Language for Cybersecurity
Applications - 6.4 AI Scripting for Automation in Cybersecurity Tasks
- 6.5 Data Analysis and Manipulation Using Python
- 6.6 Developing Security Tools with Python
- 7.1 Understanding the Application of Machine Learning in
Cybersecurity - 7.2 Anomaly Detection to Behavior Analysis
- 7.3 Dynamic and Proactive Defense using Machine
Learning - 7.4 Utilizing Machine Learning for Email Threat Detection
- 7.5 Enhancing Phishing Detection with AI
- 7.6 Autonomous Identification and Thwarting of Email
Threats - 7.7 Employing Advanced Algorithms and AI in Malware
Threat Detection - 7.8 Identifying, Analyzing, and Mitigating Malicious
Software - 7.9 Enhancing User Authentication with AI Techniques
- 7.10 Penetration Testing with AI
- 8.1 Incident Response Process (Identification, Containment,
Eradication, Recovery) - 8.2 Incident Response Lifecycle
- 8.3 Preparing an Incident Response Plan
- 8.4 Detecting and Analyzing Incidents
- 8.5 Containment, Eradication, and Recovery
- 8.6 Post-Incident Activities
- 8.7 Digital Forensics and Evidence Collection
- 8.8 Disaster Recovery Planning (Backups, Business
Continuity) - 8.9 Penetration Testing and Vulnerability Assessments
- 8.10 Legal and Regulatory Considerations of Security
Incidents
- 9.1 Introduction to Open-Source Security Tools
- 9.2 Popular Open Source Security Tools
- 9.3 Benefits and Challenges of Using Open-Source Tools
- 9.4 Implementing Open Source Solutions in Organizations
- 9.5 Community Support and Resources
- 9.6 Network Security Scanning and Vulnerability Detection
- 9.7 Security Information and Event Management (SIEM)
Tools (Open-Source options) - 9.8 Open-Source Packet Filtering Firewalls
- 9.9 Password Hashing and Cracking Tools (Ethical Use)
- 9.10 Open-Source Forensics Tools
- 10.1 Emerging Cyber Threats and Trends
- 10.2 Artificial Intelligence and Machine Learning in
Cybersecurity - 10.3 Blockchain for Security
- 10.4 Internet of Things (IoT) Security
- 10.5 Cloud Security
- 10.6 Quantum Computing and its Impact on Security
- 10.7 Cybersecurity in Critical Infrastructure
- 10.8 Cryptography and Secure Hashing
- 10.9 Cyber Security Awareness and Training for Users
- 10.10 Continuous Security Monitoring and Improvement
- 11.1 Introduction
- 11.2 Use Cases: AI in Cybersecurity
- 11.3 Outcome Presentation
Exam Objectives
Automation of Security Processes
Learners will develop the ability to automate routine security tasks such as monitoring, logging, and incident response using AI technologies, improving efficiency and accuracy.
Data Privacy and Compliance in AI Security
Learners will understand the importance of data privacy and regulatory compliance when using AI in security, enabling them to develop and implement secure, legally compliant systems.
Threat Detection and Response Using AI
Learners will develop the skills to use AI-powered tools and techniques to detect, analyze, and respond to security threats in real-time
Real-Time Cyberattack Prevention with AI
Learners will acquire the ability to leverage AI to anticipate and prevent cyberattacks before they occur, using predictive models and behavioral analysis.
Hear it from the Learners
Marc H
Happy to share I've completed the AI+ Executive Certification from AI CERTs! This program has sharpened my skills in strategic AI application + implementation, further equipping me to lead AI-driven organizational transformation.
Georgia L
As VP Operations, my recent completion of the AI+ Executive exam through AI CERTs was a pivotal step in advancing my AI skill set as we embrace an AI-driven future. This certification not only deepened my understanding of AI's broad impact across various divisions but also equipped me with the tools to make informed, strategic decisions.
Antonio C
AI+ Executive™ Instructor Guide Certificate. Today, I am part of the team of #CompuEducación instructors to teach the #AI CERTs AI+Executive certification course . This 8-hour course is a new standard for business leaders who want to start a solid path in the adoption of AI for the transformation of their companies. The technological, business, ethical, legal and strategy foundations are covered. The examples of using “AI” are practical, up-to-date, and touch on the different variants of “AI.”
Doug F
Excited to successfully complete AI Cert's AI+ Marketing certification course! For us marketers, it's imperative to embrace AI and take an active effort in learning how to harness its capabilities to stay relevant and be on the cutting edge of tech.
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