CompTIA SecAI Certification Crash Course

Course Lessons

Introduction and Overview

• Course Trailer
  
• Course Introduction
  
• CompTIA AI Certification Overview
  
• Importance of AI First
  
• Special Offer for Completing Course!
  
• Access my AI Clone and Weekly Live Q/A sessions.
• Content Downloads 

Domain 1 - Basic AI Concepts Related to Cybersecurity

• Section 1.1 - Compare and contrast various AI types and techniques used in cybersecurity
• What is AI/ML?
• What are the Machine Learning Types
• Compare and Contrasting AI/ML
• AI Categories Generative AI, Predictive AI, Agentic AI, and others.
• What is Deep Learning?
• What is Natural Language Processing (NLP)?
• Generative AI Services
• Use Cases for Generative AI
• Whiteboard - How Google Gemini Works
  
• Key Cloud offerings (AWS Bedrock,
• Google Vertex AI, Azure OpenAI).
  
• Key Players in AI Market
• Understanding the AI Tech Stack
• Demonstration - Google Cloud AI Technology Stack
  
• Model training techniques
  Model Validation
  
• Supervised and Unsupervised Learning
  
• Reinforcement Learning
  
• Whiteboard - Machine Learning Lifecycle
  
• What is Prompt engineering
  
• Systems and User Prompts
  
• Role: Assigning a persona to ChatGPT
  
• Task/Goal: Clearly defining what you want the AI to do.
  
• Context: Providing relevant background information, audience, purpose.
  
• Format: Specifying the desired output structure
  
• Tone/Style: Guiding the AI's voice
  Constraints/Limitations: Setting boundaries for the output
  
• Clarity, Specificity, and Affirmative Directives
  
• Iterative refinement: How to improve prompts based on outputs.
  
• Zero-Shot, One-Shot, and Few-Shot Prompting
  
• Use and Create GPTs
  
• Section 1.2 - Data security in relation to AI.
  
• Explain the Data Lifecycle
  Importance of Data to AI Applications
  
• Data Processing
  
• Data Types (Structured, Unstructured and Semi Structured)
  
• Watermarking
  
• Retrieval-augmented generation (RAG)
  
• Whiteboard - Retrieval-Augmented Generation (RAG)
  
• Section 1.3 - Importance of security throughout the life cycle of AI
  
• What is the AI Lifecycle?
  
• NIST AI Lifecycle
  
• Software Development Lifecycle (SDLC)
  
• Whiteboard - GenAI Lifecycles
  
• AI Governance & Policy Lifecycle
  
• Continuous Improvement & Learning Lifecycle
  
• AI Lifecycle and MLOps
  
• SDLC and AI Applications
  
• Cloud Computing Shared Model
  
• Module Review Summary
  
• Module Review Questions
   

Domain 2 - Securing AI Systems

• Section 2.1 - AI threat-modeling resources
  
• OWASP Top 10
  
• The Rise of LLMs and New Attack Surfaces
  
• What is the OWASP Top 10 for LLM Applications?
  
• Prioritizing Risk: Prevalence, Exploitability, and Impact
  
• Secure Development Lifecycle (SDLC) for LLM Applications
  
• Whiteboard Solution - Analyze and mitigate risk in your LLM applications
  
• Massachusetts Institute of Technology (MIT) AI Risk Repository
  
• MITRE Adversarial Threat Landscape for Artificial-Intelligence Systems (ATLAS)
  
• Common Vulnerabilities and Exposures (CVE) AI Working Group
  
• Threat-modeling frameworks
  
• 2.2 - Implement security controls for AI systems
  
• Defense in Depth
  
• Risk Management
  
• AI Lifecycle
  
• NIST AI Lifecycle
  
• LLM Security Testing
  
• Operational Security for AI/ML
  
• The Future of LLM Security and
• Emerging Threats
  
• Model Controls
  
• Gateway Controls
  
• Guardrails
  
• Whiteboard - Guardrail Scenarios
   
• 2.3 - Implement appropriate access controls for AI systems
  
• Model Access Controls
  Data Access Controls and Permissions
  
• Agent Access
  
• Demonstration - Google Gemini
  
• API and Networks
  Whiteboard
  
• 2.4 - Implement data security controls for AI systems
  
• Encryption Requirements
  
• Data Safety
  
• Whiteboard - AI and Encryption
  
• 2.5 - implement monitoring and auditing for AI systems
  
• Monitoring
  
• Logging
  
• Rate Monitoring
  
• Cost Monitoring
  
• Quality and Compliance
  
• Whiteboard
  
• 2.6 - Analyze the evidence of an attack and suggest compensating controls for AI systems
  
• Attack Types
  
• Compensating Controls
  
• Whiteboard
  
• Module Review Summary
  
• Module Review Questions
    

Domain 3 - AI-assisted Security
 

• Section 3.1 - Use AI-enabled tools to facilitate security tasks
  
• Tools and Applications
  
• Potential Use Cases
  
• Use Case - Fraud Detection
  
• Use Case - Threat Modeling
  
• Use Case - Anomaly Detection
  
• 3.2 - Explain how AI enables or enhances attack vectors.
  
• AI-generated content (deepfake)
  Adversarial networks
  
• Social engineering
  
• Obfuscation
  
• Automated attack generation
  
• Reconnaissance
  
• 3.3 - AI to automate security tasks
  
• Scripting tools
  
• Synthesis and summarization
  
• Incident Response
  
• Change Management
  
• Continuous integration/continuous deployment (CI/CD)
  
• Whiteboard - CI/CD Development AI Focused
  
• Module Review Summary
  
• Module Review Questions
  
• Domain 4 - AI Governance, Risk, and Compliance
  
• Section 4.1 - Organizational governance structures that support AI
  
• AI Center of Excellence
  
• Legal and Professional
• Accountability
  
• Code of Conduct
  
• Roles associated with AI
  
• Main Roles and Responsibilities of an AI Ethics Officer
  
• Creating Ethical Policies, Frameworks and Practices
  
• Whiteboard - Implement Responsible AI Principals
  
• Ethical AI in a Changing Landscape
  
• Ethical Review Boards and Committees
  
• Section 4.2 - Risks associated with AI.
  
• Defining AI Ethics
  
• Core AI Ethical Principles
  
• Responsible AI Ethics and Governance
  
• Seven C's of AI
  
• Understanding Transparency with AI
  
• Key AI Ethical Frameworks
  
• Case Study - AI Ethics Driven Strategy
  
• Sources of AI Bias
  
• The Moral and Social Implications of AI Deployment
  
• AI and Job Displacement
  
• 4.3 - Impact of compliance on business use and development of AI
  
• Defining Explainable AI
  
• Applying Explainable AI (Three Core Steps)
  
• Demonstration - AWS Compliance
  
• Legal Frameworks that Establish Requirements
  
• White Box vs Black Box
  
• NIST AI Framework
  
• ISO/IEC 27001
  
• EU General Data Protection Regulation (GDPR)
  
• EU Artificial Intelligence Act (EU AI Act)
  
• OECD AI Principles
  
• UNESCO's Recommendation on the Ethics of Artificial Intelligence (2021):
  
• EU Ethics Guidelines for Trustworthy AI (2019)
  
• Module Review Summary
  
• Module Review Questions
  
• Course Full Practice Exam
• Next Steps and Certification Process
  
• Access my AI Clone and Weekly Live Q/A
• Resources and Closeout