AI+ Security Level 2™
AT-2102
Protect and Secure: Leverage Intelligent AI SolutionsTransform your security knowledge with our AI+ Security Level 2™ course and exam bundle. Learn essential AI-driven security strategies and safeguard next-gen technologies.
Why This Certification Matters
Transform your security knowledge with our AI+ Security Level 2™ course and exam bundle. Learn essential AI-driven security strategies and safeguard next-gen technologies.
At a Glance: Course + Exam Overview
- Instructor-Led: 5 days (live or virtual)
- Self-Paced: 40 hours of content
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 2™
- Comprehensive AI-Cybersecurity Integration: Understand how AI and Cybersecurity merge, enhancing your capability to combat evolving digital threats effectively.
- Practical Python Programming Skills Learn Python tailored for AI and Cybersecurity applications, gaining hands-on coding skills to address real-world security issues.
- Advanced Threat Detection Techniques Master ML techniques to identify and mitigate email threats, malware, and network anomalies, improving cybersecurity defense.
- Cutting-Edge AI Algorithms Utilize AI algorithms for advanced user authentication and explore Generative Adversarial Networks (GANs) to strengthen cybersecurity systems.
- Real-World Application Focus Apply your skills in a Capstone Project, solving real-world cybersecurity problems and preparing for advanced industry challenges.
Who Should Enroll?
- Cybersecurity Analyst: Analyzes threats to Al infrastructure, monitors security breaches, develops defensive strategies, and responds to cybersecurity incidents effectively.
- Data Security Engineer: Protects data within Al environments, designs secure data storage solutions, encrypts sensitive information, and manages data access controls.
- Threat Intelligence Specialist: Analyzes intelligence on Al-targeted threats, predicts cyber-attacks, informs security strategies, and enhances organizational resilience.
- Security Specialist: Secures Al systems against vulnerabilities, implements security protocols, conducts risk assessments, and ensures compliance with security standards.
What You'll Learn
- 1.1 Understanding the Cyber Security Artificial Intelligence (CSAI)
- 1.2 An Introduction to AI and its Applications in Cybersecurity
- 1.3 Overview of Cybersecurity Fundamentals
- 1.4 Identifying and Mitigating Risks in Real-Life
- 1.5 Building a Resilient and Adaptive Security Infrastructure
- 1.6 Enhancing Digital Defenses using CSAI
- 2.1 Python Programming Language and its Relevance in Cybersecurity
- 2.2 Python Programming Language and Cybersecurity Applications
- 2.3 AI Scripting for Automation in Cybersecurity Tasks
- 2.4 Data Analysis and Manipulation Using Python
- 2.5 Developing Security Tools with Python
- 3.1 Understanding the Application of Machine Learning in Cybersecurity
- 3.2 Anomaly Detection to Behaviour Analysis
- 3.3 Dynamic and Proactive Defense using Machine Learning
- 3.4 Safeguarding Sensitive Data and Systems Against Diverse Cyber Threats
- 4.1 Utilizing Machine Learning for Email Threat Detection
- 4.2 Analyzing Patterns and Flagging Malicious Content
- 4.3 Enhancing Phishing Detection with AI
- 4.4 Autonomous Identification and Thwarting of Email Threats
- 4.5 Tools and Technology for Implementing AI in Email Security
- 5.1 Introduction to AI Algorithm for Malware Threat Detection
- 5.2 Employing Advanced Algorithms and AI in Malware Threat Detection
- 5.3 Identifying, Analyzing, and Mitigating Malicious Software
- 5.4 Safeguarding Systems, Networks, and Data in Real-time
- 5.5 Bolstering Cybersecurity Measures Against Malware Threats
- 5.6 Tools and Technology: Python, Malware Analysis Tools
- 6.1 Utilizing Machine Learning to Identify Unusual Patterns in Network Traffic
- 6.2 Enhancing Cybersecurity and Fortifying Network Defenses with AI Techniques
- 6.3 Implementing Network Anomaly Detection Techniques
- 7.1 Introduction
- 7.2 Enhancing User Authentication with AI Techniques
- 7.3 Introducing Biometric Recognition, Anomaly Detection, and Behavioural Analysis
- 7.4 Providing a Robust Defence Against Unauthorized Access
- 7.5 Ensuring a Seamless Yet Secure User Experience
- 7.6 Tools and Technology: AI-based Authentication Platforms
- 7.7 Conclusion
- 8.1 Introduction to Generative Adversarial Networks (GANs) in Cybersecurity
- 8.2 Creating Realistic Mock Threats to Fortify Systems
- 8.3 Detecting Vulnerabilities and Refining Security Measures Using GANs
- 8.4 Tools and Technology: Python and GAN Frameworks
- 9.1 Enhancing Efficiency in Identifying Vulnerabilities Using AI
- 9.2 Automating Threat Detection and Adapting to Evolving Attack Patterns
- 9.3 Strengthening Organizations Against Cyber Threats Using AI-driven Penetration Testing
- 9.4 Tools and Technology: Penetration Testing Tools, AI-based Vulnerability Scanners
- 10.1 Introduction
- 10.2 Use Cases: AI in Cybersecurity
- 10.3 Outcome Presentation
- 1. What Are AI Agents
- 2. Key Capabilities of AI Agents in Advanced Cybersecurity
- 3. Applications and Trends for AI Agents in Advanced Cybersecurity
- 4. How Does an AI Agent Work
- 5. Core Characteristics of AI Agents
- 6. Types of AI Agents
Tools You'll Explore
CrowdStrike
Microsoft Cognitive Toolkit (CNTK)
Flair.ai
Prerequisites
- AI+ Security Level 1™ Completion (Optional), Python Skills, Cybersecurity Knowledge, ML Awareness, Networking Knowledge, 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 Artificial Intelligence (AI) and Cyber Security - 9%
- Module 2: Python Programming for AI and Cybersecurity Professionals - 9%
- Module 3: Application of Machine Learning in Cybersecurity - 9%
- Module 4: Detection of Email Threats with AI - 9%
- Module 5: AI Algorithm for Malware Threat Detection - 9%
- Module 6: Network Anomaly Detection using AI - 9%
- Module 7: User Authentication Security with AI - 9%
- Module 8: Generative Adversarial Network (GAN) for Cyber Security - 9%
- Module 9: Penetration Testing with Artificial Intelligence - 9%
- Module 10: Capstone Project - 9%
- Optional Module: AI Agents for Security Level 2 - 10%
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.