CompTIA SecAI+ → Module 10.1: Prompt-Based Attacks - K2 Cyber Technologies

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21 Sections
61 Lessons
10 Weeks

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DOMAIN 1 Basic AI Concepts Related to Cybersecurity Exam Weight: 17%
0
CHAPTER 1: AI FUNDAMENTALS IN SECURITY
4
- 2.1
  Module 1.1: Overview of AI in Cybersecurity
- 2.2
  Module 1.2: Types of AI Technologies
- 2.3
  Module 1.3: Language Models in Security
- 2.4
  Module 1.4: Advanced AI Architectures
CHAPTER 2: MODEL TRAINING AND OPTIMIZATION
4
- 3.1
  Module 2.1: Model Training Fundamentals
- 3.2
  Module 2.2: Learning Paradigms
- 3.3
  Module 2.3: Model Optimization Techniques
- 3.4
  Module 2.4: Prompt Engineering Essentials
CHAPTER 3: DATA PROCESSING FOR AI SECURITY
3
- 4.1
  Module 3.1: Data Processing Pipeline
- 4.2
  Module 3.2: Data Enhancement Techniques
- 4.3
  Module 3.3: Data Types and Structures
CHAPTER 4: AI SYSTEM DEVELOPMENT LIFECYCLE
5
- 5.1
  Module 4.1: Business Use Case Development
- 5.2
  Module 4.2: Data Collection Security
- 5.3
  Module 4.3: Secure Model Development
- 5.4
  Module 4.4: Deployment and Operations
- 5.5
  Module 4.5: Human-Centric AI Design
DOMAIN 2 Securing AI Systems Exam Weight: 40%
0
CHAPTER 5: SECURITY FRAMEWORKS FOR AI
4
- 7.1
  Module 5.1: OWASP Resources for AI
- 7.2
  Module 5.2: Industry Frameworks
- 7.3
  Module 5.3: Threat Modeling Frameworks
- 7.4
  Module 5.4: Practical Threat Modeling
CHAPTER 6: MODEL CONTROLS AND GUARDRAILS
3
- 8.1
  Module 6.1: Model Controls Implementation
- 8.2
  Module 6.2: Gateway Controls
- 8.3
  Module 6.4: Testing and Validation
CHAPTER 7: ACCESS CONTROL FOR AI SYSTEMS
4
- 9.1
  Module 7.1: Model Access Security
- 9.2
  Module 7.2: Data Access Controls
- 9.3
  Module 7.3: Agent Access Management
- 9.4
  Module 7.4: Network and API Security
CHAPTER 8: DATA PROTECTION FOR AI
4
- 10.1
  Module 8.1: Encryption Implementation
- 10.2
  Module 8.2: Data Safety Measures
- 10.3
  Module 8.3: Privacy-Preserving Techniques
- 10.4
  Module 8.4: Compliance Integration
CHAPTER 9: AI MONITORING AND AUDITING
4
- 11.1
  Module 9.1: Prompt Monitoring
- 11.2
  Module 9.2: Log Management
- 11.3
  Module 9.3: Performance Monitoring
- 11.4
  Module 9.4: Quality and Compliance Auditing
CHAPTER 10: AI THREATS AND ATTACKS
5
- 12.1
  Module 10.1: Prompt-Based Attacks
- 12.2
  Module 10.2: Model Attacks
- 12.3
  Module 10.3: System-Level Attacks
- 12.4
  Module 10.4: Application Vulnerabilities
- 12.5
  Module 10.5: Compensating Controls
DOMAIN 3 AI-Assisted Security Exam Weight: 24%
0
CHAPTER 11: AI SECURITY TOOLS
4
- 14.1
  Module 11.1: Tool Categories
- 14.2
  Module 11.2: Security Use Cases
- 14.3
  Module 11.3: Operational Applications
- 14.4
  Module 11.4: Tool Integration
CHAPTER 12: AI-POWERED THREATS
3
- 15.1
  Module 12.1: AI-Generated Content Threats
- 15.2
  Module 12.2: Advanced Attack Techniques
- 15.3
  Module 12.3: Attack Generation and Discovery
Module 12.4: Infrastructure Attacks
0
CHAPTER 13: SECURITY AUTOMATION WITH AI
4
- 17.1
  Module 13.1: Automation Tools
- 17.2
  Module 13.2: Documentation and Communication
- 17.3
  Module 13.3: Operational Automation
- 17.4
  Module 13.4: Development Security
DOMAIN 4 AI Governance, Risk, and Compliance Exam Weight: 19%
0
CHAPTER 14: AI GOVERNANCE STRUCTURES
4
- 19.1
  Module 14.1: Organizational Structures
- 19.2
  Module 14.2: AI Policies and Procedures
- 19.3
  Module 14.3: AI-Related Roles
- 19.4
  Module 14.4: Governance Implementation
CHAPTER 15: AI RISK MANAGEMENT
3
- 20.1
  Module 15.1: Responsible AI Principles
- 20.2
  Module 15.2: Risk Categories
- 20.3
  Module 15.3: Shadow IT and AI
CHAPTER 16: AI REGULATORY COMPLIANCE
3
- 21.1
  Module 16.1: International Regulations
- 21.2
  Module 16.2: Corporate Compliance
- 21.3
  Module 16.3: Third-Party Compliance

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Module 9.4: Quality and Compliance Auditing

Module 10.2: Model Attacks

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