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You're an IT professional, cybersecurity expert, or AI enthusiast, this course provides the essential knowledge to secure AI systems and mitigate risks.

Prerequisites

  • Basic knowledge of AI concepts and applications for understanding advanced topics.
  • Familiarity with Programming Languages such as Python or R
  • Proficiency in Data Analysis and Interpretation
  • Knowledge of Machine Learning Algorithms and Techniques
  • Awareness of Ethical Issues and Considerations in AI Development

 
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 Key Cybersecurity Concepts
  • 1.2 AI’s role in modern security solutions
  • 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

Tools

CrowdStrike

CrowdStrike

Flare

Flare

ChatGPT

ChatGPT

Pluralsight

Pluralsight

Who Should Enroll?

This certification is ideal for:

  • Professionals & Business Leaders
    - Improve AI-driven content generation, customer interactions, and automation.
  • Writers, Marketers & Content Creators
    - Leverage AI tools to streamline content production.
  • Developers & Data Analysts
    - Enhance AI automation and optimize chatbot responses.
  • Students & AI Enthusiasts
    - Gain hands-on experience in prompt engineering to stay ahead in the AI landscape.
Key Learning Outcomes
  • Understand how AI models interpret and generate responses based on prompts.
  • Learn to design effective, structured, and optimized prompts for various AI applications.
  • Explore prompt refinement techniques to improve AI accuracy and reliability.
  • Apply AI prompt engineering to content creation, business strategy, and automation workflows.

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Exam Objectives

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Automation of Security Processes

Learners will gain expertise in automating security operations, including monitoring, logging, and incident response, using AI technologies to improve efficiency and accuracy.

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Data Privacy and Compliance in AI Security

Learners will understand the significance of data privacy and regulatory compliance in AI-driven security, ensuring the development of secure and legally compliant systems.

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Threat Detection and Response Using AI

Learners will develop proficiency in utilizing AI-powered tools to detect, analyze, and respond to security threats in real-time, enhancing threat mitigation strategies.

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Real-Time Cyberattack Prevention with AI

Learners will acquire the ability to apply AI for proactive cybersecurity, using predictive models and behavioral analysis to identify and prevent potential cyberattacks before they occur.

Career Opportunities Post-Certification

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Median Salaries

$60,000

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With AI Skills

$75,000

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% Difference

25

Product Manager

AI Security Analyst

Oversees AI-driven security operations, utilizing advanced technologies to monitor, detect, and respond to cyber threats while maintaining strong security protocols.

Data Analyst

Cybersecurity Engineer (AI-Focused)

Designs and implements AI-powered security solutions to safeguard networks and systems from cyber threats, enhancing overall defense strategies

AI Architect

Threat Intelligence Specialist

Leverages AI tools to assess cyber threats, identify system vulnerabilities, and provide data-driven insights for proactive threat prevention and mitigation.

ML Engineer

AI-Powered Incident Response Analyst

Utilizes AI technologies to manage and investigate security incidents, deploying AI-driven recovery strategies and strengthening post-incident response measures.

Join AI Course & Become Future-Ready!

Stay ahead in the AI-driven world. Enroll now to gain in-demand skills, hands-on experience, and industry-recognized certification for a future-ready career.

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Frequently Asked Questions

The AI+ Security Level 1™ certification is an introductory course that covers AI-driven security solutions, focusing on threat detection, automated response, and incident management.

This course is ideal for cybersecurity professionals, network engineers, IT managers, and AI enthusiasts who want to expand their knowledge of AI-powered security techniques.

Participants will explore AI-based threat detection, machine learning applications for security automation, AI-driven incident response, and compliance with security regulations such as GDPR, HIPAA, and NIST.

Enrollees will have access to course materials, case studies, hands-on project guidance, and a collaborative online learning community.

Yes, the AI+ Security Level 1™ certification is widely recognized as a foundational credential for professionals working with AI-driven security solutions.