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This course builds on foundational AI security knowledge and provides advanced techniques to safeguard AI-driven applications and infrastructure.

Prerequisites

  • Prior completion of AI+ Security Level 1™ is beneficial but not required.
  • Basic Python Knowledge: Familiarity with variables, loops, and functions in Python.
  • Cybersecurity Fundamentals: Understanding of key principles like the CIA triad and common cyber threats.
  • Machine Learning Awareness: General knowledge of machine learning concepts, no coding expertise needed.
  • Networking Basics: Understanding of IP addresses and internet functionality.
  • Command Line Proficiency: Comfort with basic tasks using Linux or Windows terminal.
  • Interest in AI for Security: Enthusiasm for exploring AI applications in threat detection and mitigation.

 
Recertification Requirements
    To maintain the validity of your certification, The Whiteboard, in affiliation with AI CERTs, requires annual recertification. A notification will be sent three months before the due date, guiding candidates through the process outlined in the candidate handbook. Need Assistance? For any questions or support regarding recertification, please contact The Whiteboard's team at info@thewhiteboard.co.in.

Course Modules and Curriculum

  • 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

Tools

CrowdStrike

CrowdStrike

Flare

Flare

Microsoft Cognitive Toolkit (CNTK)

Microsoft Cognitive Toolkit (CNTK)

Who Should Enroll?

This certification is ideal for:

  • Cybersecurity Professionals
    - Enhance your expertise in AI-specific security challenges.
  • AI & Machine Learning Engineers
    - Learn how to build secure AI models and mitigate risks.
  • IT Security Managers & Analysts
    - Understand AI-driven threat detection and defense strategies.
  • Compliance & Risk Officers
    - Ensure AI governance, compliance, and regulatory alignment.
Key Learning Outcomes
  • Identify emerging security threats targeting AI models and data.
  • Develop AI risk mitigation strategies and adversarial defense techniques.
  • Understand AI governance, compliance, and ethical security frameworks.
  • Implement secure AI deployment practices to protect against vulnerabilities.

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Exam Objectives

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AI-Driven Threat Detection

Learners will develop skills in utilizing AI algorithms to identify cybersecurity threats, including email-based attacks, malware, and network anomalies, strengthening security monitoring systems.

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Machine Learning Applications in Cybersecurity

Participants will gain the ability to apply machine learning techniques to predict, detect, and mitigate cyber threats, leveraging data-driven insights for proactive defense.

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Advanced AI-Based User Authentication

Learners will acquire expertise in implementing AI-powered authentication methods, enhancing security protocols for accurate identity verification and fraud prevention.

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AI-Assisted Penetration Testing

Students will learn how to integrate AI tools into penetration testing, automating vulnerability detection for more efficient and comprehensive security assessments.

Career Opportunities Post-Certification

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Median Salaries

$90,000

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With AI Skills

$1,15,000

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% Difference

28

Product Manager

Security Specialist

Responsible for safeguarding AI systems by identifying vulnerabilities, implementing security measures, conducting risk assessments, and ensuring compliance with security standards.

Data Analyst

Cybersecurity Analyst

Monitors and analyzes threats to AI infrastructure, detects security breaches, develops defense mechanisms, and responds effectively to cybersecurity incidents.

AI Architect

Data Security Engineer

Secures data within AI-driven environments by designing protected storage solutions, encrypting sensitive information, and managing access controls to prevent unauthorized access.

ML Engineer

Threat Intelligence Specialist

Collects and evaluates intelligence on AI-focused threats, predicts potential cyberattacks, informs security policies, and strengthens organizational defense strategies.

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Frequently Asked Questions

The Level 2 Certification focuses on advanced AI security concepts, including adversarial attacks, AI risk management, and governance frameworks. It builds on fundamental security principles covered in Level 1 and prepares professionals for real-world AI security challenges.

While completing Level 1 is recommended, it is not mandatory. However, you should have a basic understanding of AI security concepts or experience in cybersecurity, AI development, or risk management.

This is a self-paced course, typically taking 4-8 weeks depending on your schedule and experience level.

Yes, the AI Security Level 2 Certification is designed in alignment with industry best practices. It provides advanced AI security skills that are highly valued in sectors such as finance, healthcare, government, and tech enterprises.

Absolutely! AI security is becoming a critical skill across industries. Whether you're in IT security, risk management, AI development, or compliance, this certification provides actionable skills to enhance AI system protection.