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COMP-303

CompTIA SecAI+ Bootcamp

This course is designed to help you secure, govern, and responsibly integrate artificial intelligence into your cybersecurity operations.

Description

This course is designed to help you secure, govern, and responsibly integrate artificial intelligence into your cybersecurity operations. You’ll build the skills to defend AI systems, meet global compliance expectations, and use AI to enhance threat detection, automation, and innovation—so you can strengthen your expertise and help keep your organization’s systems and data secure. Prepares you for the CompTIA CY0-001 exam.

Prerequisite

3–4 years of experience in IT (including at least 2+ years hands-on cybersecurity). Security+, CySA+, PenTest+, or equivalent recommended.

Objectives

  • Apply AI concepts to strengthen your organization’s cybersecurity posture.
  • Secure AI systems using advanced controls and protections to safeguard data, models, and infrastructure.
  • Leverage AI technologies to automate workflows, accelerate incident response, and scale security operations.
  • Navigate global GRC frameworks to ensure ethical and compliant AI adoption across industries.
  • Defend against AI-driven threats like adversarial attacks, automated malware, and malicious use of generative AI.
  • Integrate AI securely into DevSecOps pipelines and enterprise security strategies.

Key Takeaways

  • Practical, vendor-neutral skills in understanding, securing, and governing AI-enabled environments
  • Understand how AI systems are built, deployed, and operated inside real organizations and how security teams engage with those systems daily
  • Skills to assess AI-related risks, evaluate third-party AI services, support enterprise AI governance programs, and respond to incidents involving AI components
  • Understand governance, risk, and compliance obligations associated with AI-enabled environments

Certificate of Completion

  • Certificate of Completion issued after successful completion of all chapters, hands-on exercises, and course evaluation.
  • Certificate is downloadable from the Ghost Team Academy Education Portal.

Training Outline

Module 1: Welcome

  • Topics:
    • Introductions and expectations
    • Course Overview
  • Labs/Exercises:
    • 1.1 Explain AI Concepts for Cybersecurity
    • 1.1.1 Live Lab: Explore The SecAI+ Lab Environment
    • 1.2 Understand AI Model Training and Prompt Engineering
    • 1.2.1 Live Lab: Perform Prompt Engineering and Bias Detection
    • 1.2.2 Live Lab: Prompt Design and Optimization
    • 1.3 Secure AI Data
    • 1.3.1 Live Lab: Examine RAG Solutions
    • 1.3.2 Live Lab: Verify Data Integrity

Module 2: Basic AI concepts related to cybersecurity (17%)

  • Topics:
    •  Core AI principles and terminology: Machine learning, deep learning, natural language processing, and
      automation
    • AI applications in security: Use cases for AI in threat detection, defense, and security operations
    • AI-driven threats: Automated phishing, polymorphic malware, adversarial machine learning, and malicious use
      of generative AI
  • Labs/Exercises:
    • 2.1.1 Live Lab: Analyze Threats using Public Resources
    • 2.1.2 Live Lab: Apply a Threat Modeling Framework to AI
    • 2.1.3 Live Lab: Create and Deploy an Azure OpenAI LLM
    • 2.2 Implement Security Controls for AI Systems
    • 2.2.1 Live Lab: Apply Structured Prompt Template
    • 2.2.2 Live Lab: Secure an Azure OpenAI LLM

Module 3: Securing AI systems (40%)

  • Topics:
    • Implementing Security Controls: Protect AI systems, data, and models using robust technical safeguards
    • AI Deployment Environments: Apply best practices across on-premises, cloud, and hybrid infrastructures
    • Adversarial Risks: Defend against attacks targeting AI models, data pipelines, and inference layers
  • Labs/Exercises:
    • 3.1 Apply Data Security Controls for AI Security
    • 3.1.1 Live Lab: Sanitize Data for AI Analysis
    • 3.2 Perform Monitoring and Auditing for AI Systems
    • 3.2.1 Live Lab: Analyze Logs with AI

Module 4: AI-assisted security (24%)

  • Topics:
    • Detection and Response: Use AI-driven tools to identify anomalies, detect threats, and accelerate incident
      remediation.
    • Automating Security Workflows: Integrate AI for event triage, alert correlation, and response orchestration.
    • AI Techniques in Operation: Incorporate AI into threat modeling, behavior analysis, and continuous monitoring.
  • Labs/Exercises:
    • 4.1 Analyze AI System Attacks and Utilize Compensating Controls
    • 4.1.1 Live Lab: Test Prompt Injection Attacks

Module 5: AI governance, risk, and compliance (19%)

  • Topics:
    • Regulatory frameworks: Identify global governance requirements and their implications for AI adoption
    • GRC in AI Projects: Incorporate governance, risk management, and compliance practices throughout the AI
      lifecycle
    • Responsible AI Use: Apply ethical guidelines, legal standards, and industry frameworks such as GDPR and NIST
      AI RMF
  • Labs/Exercises:
    • 5.1 Summarize AI-Enabled and AI-Enhanced Attack Vectors
    • 5.1.1 Live Lab: Explore AI-Assisted Attack Vector Identification
    • 5.2 Use AI to Automate Security Tasks
    • 5.2.1 Live Lab: Accelerate Scripting with AI
    • 5.2.2 Live Lab: Transform Documentation into Insights with AI
    • 5.2.3 Live Lab: Automate Workflows with AI

Module 6: Conclusion

  • Topics:
    • Course summary
    • Key takeaways
    • Q&A

Quick Info
  • Type: Hands-On, Workshop, Lecture, Demo
  • Delivery: In Person, Virtual, Hybrid
  • Level: Advanced
  • Duration: 5 days (8 hours per day)
  • CEU Hours: 40
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