AI+ Security Level 1™ 

AI+ Security Level 1™ 

Category:
AI Technical / Security / All course / Available Now /
$370.00
AI+ Security Level 1™ 

$370.00

Duration: 40 hours (5 Days)
AT-2101

Empowering Cybersecurity with AI

 

Start your AI security journey with our all-in-one bundle. Explore core concepts in AI-driven protection, vulnerability management, and intelligent threat response.

Prerequisites

  • 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.
  • There are no mandatory prerequisites for certification. Certification is based solely on performance in the examination. However, candidates may choose to prepare through self-study or optional training offered by AI CERTS Authorized Training Partners (ATPs).
Number of Modules : 11
Module 1: Introduction to Cybersecurity
  1. 1.1 Definition and Scope of Cybersecurity
  2. 1.2 Key Cybersecurity Concepts
  3. 1.3 CIA Triad (Confidentiality, Integrity, Availability)
  4. 1.4 Cybersecurity Frameworks and Standards (NIST, ISO/IEC27001)
  5. 1.5 Cyber Security Laws and Regulations (e.g., GDPR, HIPAA)
  6. 1.6 Importance of Cybersecurity in Modern Enterprises
  7. 1.7 Careers in Cyber Security
Module 2: Operating System Fundamentals
  1. 2.1 Core OS Functions (Memory Management, Process Management)
  2. 2.2 User Accounts and Privileges
  3. 2.3 Access Control Mechanisms (ACLs, DAC, MAC)
  4. 2.4 OS Security Features and Configurations
  5. 2.5 Hardening OS Security (Patching, Disabling
    Unnecessary Services)
  6. 2.6 Virtualization and Containerization Security
    Considerations
  7. 2.7 Secure Boot and Secure Remote Access
  8. 2.8 OS Vulnerabilities and Mitigations
Module 3: Networking Fundamentals
  1. 3.1 Network Topologies and Protocols (TCP/IP, OSI Model)
  2. 3.2 Network Devices and Their Roles (Routers, Switches,
    Firewalls)
  3. 3.3 Network Security Devices (Firewalls, IDS/IPS)
  4. 3.4 Network Segmentation and Zoning
  5. 3.5 Wireless Network Security (WPA2, Open WEP
    vulnerabilities)
  6. 3.6 VPN Technologies and Use Cases
  7. 3.7 Network Address Translation (NAT)
  8. 3.8 Basic Network Troubleshooting
Module 4: Threats, Vulnerabilities, and Exploits
  1. 4.1 Types of Threat Actors (Script Kiddies, Hacktivists, Nation-States)
  2. 4.2 Threat Hunting Methodologies using AI
  3. 4.3 AI Tools for Threat Hunting (SIEM, IDS/IPS)
  4. 4.4 Open-Source Intelligence (OSINT) Techniques
  5. 4.5 Introduction to Vulnerabilities
  6. 4.6 Software Development Life Cycle (SDLC) and Security Integration with AI
  7. 4.7 Zero-Day Attacks and Patch Management Strategies
  8. 4.8 Vulnerability Scanning Tools and Techniques using AI
  9. 4.9 Exploiting Vulnerabilities (Hands-on Labs)
Module 5: Understanding of AI and ML
  1. 5.1 An Introduction to AI
  2. 5.2 Types and Applications of AI
  3. 5.3 Identifying and Mitigating Risks in Real-Life
  4. 5.4 Building a Resilient and Adaptive Security Infrastructure with AI
  5. 5.5 Enhancing Digital Defenses using CSAI
  6. 5.6 Application of Machine Learning in Cybersecurity
  7. 5.7 Safeguarding Sensitive Data and Systems Against Diverse Cyber Threats
  8. 5.8 Threat Intelligence and Threat Hunting Concepts
Module 6: Python Programming Fundamentals
  1. 6.1 Introduction to Python Programming
  2. 6.2 Understanding of Python Libraries
  3. 6.3 Python Programming Language for Cybersecurity
    Applications
  4. 6.4 AI Scripting for Automation in Cybersecurity Tasks
  5. 6.5 Data Analysis and Manipulation Using Python
  6. 6.6 Developing Security Tools with Python
Module 7: Applications of AI in Cybersecurity
  1. 7.1 Understanding the Application of Machine Learning in Cybersecurity
  2. 7.2 Anomaly Detection to Behavior Analysis
  3. 7.3 Dynamic and Proactive Defense using Machine Learning
  4. 7.4 Utilizing Machine Learning for Email Threat Detection
  5. 7.5 Enhancing Phishing Detection with AI
  6. 7.6 Autonomous Identification and Thwarting of Email Threats
  7. 7.7 Employing Advanced Algorithms and AI in Malware Threat Detection
  8. 7.8 Identifying, Analyzing, and Mitigating Malicious Software
  9. 7.9 Enhancing User Authentication with AI Techniques
  10. 7.10 Penetration Testing with AI
Module 8: Incident Response and Disaster Recovery
  1. 8.1 Incident Response Process (Identification, Containment, Eradication, Recovery)
  2. 8.2 Incident Response Lifecycle
  3. 8.3 Preparing an Incident Response Plan
  4. 8.4 Detecting and Analyzing Incidents
  5. 8.5 Containment, Eradication, and Recovery
  6. 8.6 Post-Incident Activities
  7. 8.7 Digital Forensics and Evidence Collection
  8. 8.8 Disaster Recovery Planning (Backups, Business Continuity)
  9. 8.9 Penetration Testing and Vulnerability Assessments
  10. 8.10 Legal and Regulatory Considerations of Security Incidents
Module 9: Open Source Security Tools
  1. 9.1 Introduction to Open-Source Security Tools
  2. 9.2 Popular Open Source Security Tools
  3. 9.3 Benefits and Challenges of Using Open-Source Tools
  4. 9.4 Implementing Open Source Solutions in Organizations
  5. 9.5 Community Support and Resources
  6. 9.6 Network Security Scanning and Vulnerability Detection
  7. 9.7 Security Information and Event Management (SIEM) Tools (Open-Source options)
  8. 9.8 Open-Source Packet Filtering Firewalls
  9. 9.9 Password Hashing and Cracking Tools (Ethical Use)
  10. 9.10 Open-Source Forensics Tools
Module 10: Securing the Future
  1. 10.1 Emerging Cyber Threats and Trends
  2. 10.2 Artificial Intelligence and Machine Learning in
    Cybersecurity
  3. 10.3 Blockchain for Security
  4. 10.4 Internet of Things (IoT) Security
  5. 10.5 Cloud Security
  6. 10.6 Quantum Computing and its Impact on Security
  7. 10.7 Cybersecurity in Critical Infrastructure
  8. 10.8 Cryptography and Secure Hashing
  9. 10.9 Cyber Security Awareness and Training for Users
  10. 10.10 Continuous Security Monitoring and Improvement
Module 11: Capstone Project
  1. 11.1 Introduction
  2. 11.2 Use Cases: AI in Cybersecurity
  3. 11.3 Outcome Presentation
Optional Module: AI Agents for Security Level 1
  1. 1. Understanding AI Agents
  2. 2. What Are AI Agents
  3. 3. Key Capabilities of AI Agents in Cyber Security
  4. 4. Applications and Trends for AI Agents in Cyber Security
  5. 5. How Does an AI Agent Work
  6. 6. Core Characteristics of AI Agents
  7. 7. Types of AI Agents

CrowdStrike

Flair.ai

ChatGPT

Pluralsight

Passing Score : 70% (35/50)

Number of Multiple Choice Question : 50 MCQs, 90 Minutes

Number of exams required for certification : 1

Exam Blueprint

  • Introduction to Cybersecurity – 6%
  • Operating System Fundamentals – 7%
  • Networking Fundamentals – 7%
  • Threats, Vulnerabilities, and Exploits – 10%
  • Understanding of AI and ML – 10%
  • Python Programming Fundamentals – 10%
  • Applications of AI in Cybersecurity – 10%
  • Incident Response and Disaster Recovery – 10%
  • Open Source Security Tools – 10%

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.

Cybersecurity Engineer (AI-focused)
Develops and implements Al-driven security solutions to protect networks and systems from potential cyberattacks

Al-Powered Incident Response Analyst
Specializes in AI-driven security incident management, post-incident investigations, and deploying AI-based recovery strategies

Al Security Analyst
Responsible for leveraging Al technologies to monitor, detect, and respond to cybersecurity threats, ensuring robust security measures are in place.

Threat Intelligence Specialist
Uses Al tools to analyze cyber threats, identify vulnerabilities, and provide insights for proactive threat prevention and mitigation

FAQ

What is AI+ Security Level 1™ certification?

The AI+ Security Level 1™ certification is a foundational course focusing on AI-powered security solutions, including threat detection, automated response, and incident management.

Who should enroll in this course?

This course is ideal for cybersecurity professionals, network engineers, IT managers, and AI enthusiasts aiming to enhance their knowledge of AI-driven security techniques.

What topics will the course cover?

You will learn about AI-based threat detection, machine learning for security automation, AI-driven incident response, and compliance with standards like GDPR, HIPAA, and NIST.

What resources will I get access to?

You’ll receive course materials, case studies, project guidance, and access to an online community of learners.

Is this certification recognized in the industry?

Yes, AI+ Security Level 1™ certification is widely recognized as a benchmark for foundational knowledge in AI-powered security solutions.

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