AI+ Security Level 2
Protect and Secure: Leverage Advanced AI Solutions for Cybersecurity.
Description
The AI+ Security Level 2™ certification provides professionals with a deeper understanding of integrating Artificial Intelligence (AI) into modern cybersecurity practices.
Designed to enhance your knowledge of threat detection, data privacy, and advanced security measures, this program ensures participants master AI-based techniques for safeguarding digital ecosystems.
By exploring AI algorithms for penetration testing, user authentication, and anomaly detection, learners will gain insights into automating and optimizing critical security processes.
Key focus areas include Generative Adversarial Networks (GANs) for advanced security applications, real-time cyberattack prevention models, and hands-on projects that simulate real-world challenges.
By the end of the certification, learners will be prepared to tackle malware threats, strengthen network protocols, secure sensitive data, and build resilient cybersecurity frameworks.
Prerequisites
- Completion of AI+ Security Level 1™ (recommended but not mandatory).
- Familiarity with Python programming basics (variables, loops, and functions).
- Understanding of the CIA triad, common cybersecurity principles, and threats like malware.
- Awareness of basic machine learning concepts (technical skills not required).
- Proficiency in networking fundamentals (IP addressing, TCP/IP protocols).
- Basic Linux/command line skills for navigating tools and environments.
- Interest in applying AI for real-time cybersecurity solutions.
- 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).
Course Outline
Module 1: Introduction to Artificial Intelligence (AI) and Cyber Security
- 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
Module 2: Python Programming for AI and Cybersecurity Professionals
- 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
Module 3: Application of Machine Learning in Cybersecurity
- 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 an
Module 4: Detection of Email Threats with AI
- 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
Module 5: AI Algorithm for Malware Threat Detection
- 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
Module 6: Network Anomaly Detection using AI
- 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
Module 7: User Authentication Security with AI
- 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
Module 8: Generative Adversarial Network (GAN) for Cyber Security
- 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
Module 9: Penetration Testing with Artificial Intelligence
- 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
Module 10: Capstone Project
- 10.1 Introduction
- 10.2 Use Cases: AI in Cybersecurity
- 10.3 Outcome Presentation
Optional Module: AI Agents for Security Level 2
- 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