AI+ Security Level 1™
AT-2101
Empowering Cybersecurity with AIStart your AI security journey with our all-in-one bundle. Explore core concepts in AI-driven protection, vulnerability management, and intelligent threat response.
Why This Certification Matters
Start your AI security journey with our all-in-one bundle. Explore core concepts in AI-driven protection, vulnerability management, and intelligent threat response.
At a Glance: Course + Exam Overview
Program Name
AI+ Security Level 1™
Included
Instructor-led OR Self-paced course + Official exam + Digital badge
Duration
- Instructor-Led: 5 days (live or virtual)
- Self-Paced: 40 hours of content
Prerequisites
Basic Python Programming, Cybersecurity Knowledge, Basic Machine Learning Concepts, Basic Networking, Linux/Command Line Skills
Exam Format
50 questions, 70% passing, 90 minutes, online proctored exam
Delivery
Online labs, projects, case studies
Outcome
Industry-recognized credential + hands-on experience
Job Roles & Industry Outlook
Cyber Security Analyst
Protect digital assets by identifying vulnerabilities, monitoring threats, and applying AI to strengthen security defenses.
Business Analyst
Analyze business processes and ensure AI initiatives comply with legal, ethical, and regulatory standards.
Business Intelligence Analyst
Transform data into actionable insights by applying AI-driven analytics to guide smarter business decisions.
Industry Growth: AI+ Security Level 1™
- Comprehensive Learning Explore AI and cybersecurity integration through Python, machine learning, and threat mitigation to build a strong technical foundation.
- Hands-on Approach Apply concepts in a Capstone Project, solving real-world cybersecurity challenges by leveraging AI tools and practical problem-solving skills.
- Cutting-Edge Knowledge Dive into advanced topics like AI-based authentication and GANs to understand next-gen cybersecurity strategies and innovations.
- Boost Strategic Decision-Making with AI Analytics Master AI models to analyze business data, predict outcomes, and enable more informed, real-time decisions that enhance competitive advantage.
- AI-Driven Threat Detection Learn to detect malware, phishing, and anomalies using machine learning, enhancing your ability to predict and prevent attacks.
- Industry Relevance Stay ahead in cybersecurity by mastering AI applications, making you a valuable asset for future-focused security roles and organizations.
Who Should Enroll?
- Cybersecurity Professionals: Enhance your skills by learning AI-driven methods for advanced threat detection and security measures.
- Network Engineers: Gain expertise in integrating AI to improve network defense, threat analysis, and anomaly detection.
- IT Managers: Equip yourself with the knowledge to manage AI-driven security solutions for your organization’s protection and risk management.
- AI Enthusiasts: Explore the intersection of AI and cybersecurity, learning how AI technologies are transforming digital security landscapes.
- Security Analysts: Deepen your understanding of AI-powered tools to identify and mitigate complex cybersecurity risks in modern infrastructures.
What You'll Learn
- 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
- 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
- 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
Tools You'll Explore
CrowdStrike
Flair.ai
ChatGPT
Pluralsight
Prerequisites
- Basic Python Programming, Cybersecurity Knowledge, Basic Machine Learning Concepts, Basic Networking, Linux/Command Line Skills
Exam Details
Duration
90 minutes
Passing Score
70% (35/50)
Format
50 multiple-choice/multiple-response questions
Delivery Method
Online via AI proctored exam platform (flexible scheduling)
Exam Blueprint
- Module 1: Introduction to Cybersecurity - 8%
- Module 2: Operating System Fundamentals - 8%
- Module 3: Networking Fundamentals - 8%
- Module 4: Threats, Vulnerabilities, and Exploits - 8%
- Module 5: Understanding of AI and ML - 8%
- Module 6: Python Programming Fundamentals - 8%
- Module 7: Applications of AI in Cybersecurity - 8%
- Module 8: Incident Response and Disaster Recovery - 8%
- Module 9: Open Source Security Tools - 9%
- Module 10: Securing the Future - 9%
- Module 11: Capstone Project - 9%
- Optional Module: AI Agents for Security Level 1 - 9%
Choose the Format That Fits Your Schedule
What's Included (One-Year Subscription + All Updates):
- High-Quality Videos, E-book (PDF & Audio), and Podcasts
- AI Mentor for Personalized Guidance
- Quizzes, Assessments, and Course Resources
- Online Proctored Exam with One Free Retake
- Comprehensive Exam Study Guide
- Access for Tablet & Phone
Frequently Asked Questions
The course includes a mix of theoretical knowledge and practical applications, culminating in an interactive capstone project. This structure ensures that participants gain both conceptual understanding and hands-on experience.
This course is ideal for developers, IT professionals, and anyone with a foundational understanding of AI and cloud computing who wants to enhance their skills in integrating AI with cloud platforms like AWS, Azure, or Google Cloud.
Participants will learn to develop, deploy, and manage AI models on leading cloud platforms. Skills include optimizing AI model performance, ensuring security, meeting compliance standards, and applying AI and cloud concepts to solve real-world problems.
This certification enhances your professional profile by demonstrating proficiency in integrating AI with cloud computing. It equips you with in-demand skills, giving you a competitive edge in the job market and opening doors to lucrative career opportunities.
The certification includes an interactive capstone project where participants apply their knowledge to design and implement AI solutions within cloud environments. This project is designed to simulate real-world scenarios and challenges.