AI+ Security Level 2™

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.

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Incluye:

Candidatos ideales para este curso:

Security Specialist. Secures AI systems against vulnerabilities, implements security protocols, conducts risk assessments, and ensures compliance with security standards.
Cybersecurity Analyst. Analyzes threats to AI infrastructure, monitors security breaches, develops defensive strategies, and responds to cybersecurity incidents effectively.
Data Security Engineer. Protects data within AI environments, designs secure data storage solutions, encrypts sensitive information, and manages data access controls.
Threat Intelligence Specialist. Gathers and analyzes intelligence on AI-targeted threats, predicts cyber-attacks, informs security strategies, and enhances organizational resilience.

Prerrequisitos

Completion of AI+ Security Level 1™, but not mandatory
Basic Python Skills: Familiarity with Python basics, including variables, loops, and functions.
Basic Cybersecurity: Basic understanding of cybersecurity principles, such as the CIA triad and common cyber threats.
Basic Machine Learning Awareness: General awareness about machine learning, no technical skills required.
Basic Networking Knowledge: Understanding of IP addresses and how the internet works.
Basic command line Skills: Comfort using the command line like Linux or Windows terminal for basic tasks
Interest in AI for Security: Willingness to explore how AI can be applied to detect and mitigate security threats.

““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).

Contenido

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 and Systems Against Diverse Cyber Threats

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.