AI+ Security Level 1™

Empowering Cybersecurity with AI

Certificate Code: AT-2101

Duration: 40 hours (5 Days)

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Course Metrics Prerequisites Curriculum Outcomes Opportunities Salary Comparison FAQs Enroll

12+

Modules

40 hours (5 Days)

Duration

50 MCQs, 90 Minutes

Examination

70% (35/50)

Passing Score

About This Certification

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).

Tools Covered

CrowdStrike

Flair.ai

ChatGPT

Pluralsight

Course Curriculum

12 Modules 40 hours (5 Days)

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

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%

Securing the Future - 10%

Capstone Project - 10%

Learning Outcomes

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.

Career Opportunities

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

Salary Potential

Without AI Skills

$60,000

With AI Skills

$75,000

25% Higher Earning Potential

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.

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