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AI+ Security Level 1™

# AT-2101

Empowering Cybersecurity with AI

The AI+ Security Level 1™ certification course is a comprehensive program that dives deep into the integration of Artificial Intelligence (AI) in cybersecurity. Tailored for aspiring professionals, this course equips participants with skills to address modern security challenges by leveraging advanced AI-driven techniques. Beginning with Python programming basics and foundational cybersecurity principles, learners explore essential AI applications such as machine learning for anomaly detection, real-time threat analysis, and incident response automation. Core topics include user authentication using AI algorithms, GANs for cybersecurity solutions, and data privacy compliance. This course ensures participants gain hands-on experience through a Capstone Project, where real-world cybersecurity problems are tackled using AI-powered tools, leaving graduates well-prepared to secure digital infrastructures and protect sensitive data.

$495.00

High-Quality Video, E-book & Audiobook

Modules Quizzes

AI Mentor

Access for Tablet & Phone

Online Proctored Exam with One Free Retake

LABs Practices

Prerequisites

Python Programming: Familiarity with loops, functions, and variables.
Cybersecurity Basics: Understanding the CIA triad and common cyber threats (e.g., phishing, malware).
Machine Learning Concepts: Awareness of basic machine learning frameworks (prior knowledge preferred but not mandatory).
Basic Networking: Proficiency in IP addressing and TCP/IP protocols.
Linux/Command Line Skills: Ability to navigate and operate using 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).

Exam Details

Exam Blueprint

Modules	Percentage
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
Securing the Future	10
Capstone Project	10

Self Study Materials Included

Videos

Engaging visual content to enhance understanding and learning experience.

Podcasts

Insightful audio sessions featuring expert discussions and real-world cases.

Audiobooks

Listen and learn anytime with convenient audio-based knowledge sharing.

E-Books

Comprehensive digital guides offering in-depth knowledge and learning support.

Labs

Interactive lab sessions to apply concepts and strengthen technical skills.

Module Wise Quizzes

Interactive assessments to reinforce learning and test conceptual clarity.

Additional Resources

Supplementary references and list of tools to deepen knowledge and practical application.

Tools You'll Master

CrowdStrike

Flair.ai

ChatGPT

Pluralsight

What Will You Learn?

Automation of Security Processes

Master AI technologies to streamline routine tasks like monitoring, logging, and incident management for improved operational efficiency and accuracy.

Threat Detection and Response Using AI

Learn to deploy AI-powered tools for real-time threat detection, analysis, and mitigation of cyber risks.

Data Privacy and Compliance in AI Security

Explore regulatory requirements and implement data privacy measures using AI tools to ensure compliance and secure handling of sensitive data.

Real-Time Cyberattack Prevention with AI

Acquire predictive analytics skills to prevent cyberattacks before they occur, leveraging behavioral analysis and anomaly detection.

Certification Modules

Module 1: Introduction to Cybersecurity

1.1 Definition and Scope of Cybersecurity
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

Module 2: Operating System Fundamentals

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

Module 3: Networking Fundamentals

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

Module 4: Threats, Vulnerabilities, and Exploits

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)

Module 5: Understanding of AI and ML

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

Module 6: Python Programming Fundamentals

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

Module 7: Applications of AI in Cybersecurity

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

Module 8: Incident Response and Disaster Recovery

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

Module 9: Open Source Security Tools

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

Module 10: Securing the Future

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

Module 11: Capstone Project

11.1 Introduction
11.2 Use Cases: AI in Cybersecurity
11.3 Outcome Presentation

Optional Module: AI Agents for Security Level 1

1. Understanding AI Agents
2. What Are AI Agents
3. Key Capabilities of AI Agents in Cyber Security
4. Applications and Trends for AI Agents in Cyber Security
5. How Does an AI Agent Work
6. Core Characteristics of AI Agents
7. Types of AI Agents

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Industry Opportunities after Certification Completion

Median Salaries

$60,000

With AI+ Security Level 1™

$75,000

% Difference

AI Security Analyst

Monitor, detect, and respond to cybersecurity threats using AI-powered systems for robust defense mechanisms.

Cybersecurity Engineer (AI-focused)

Implement AI-driven solutions to safeguard networks and prevent potential cyberattacks.

Threat Intelligence Specialist

Use AI tools to analyze vulnerabilities and develop proactive threat mitigation strategies.

AI-Powered Incident Response Analyst

Resolve security incidents with AI-based recovery techniques and post-incident investigations.

Trusted Reviews by Our Learners

John Marcos

I’m happy to share that I’ve obtained a new certification: AI Integrated CyberSecurity Level 1 from AI CERTs®!

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Frequently Asked Questions

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.