CompTIA SecAI+

This accelerated CompTIA SecAI+ course focuses on the practical realities of securing AI systems in production environments. It covers foundational AI concepts relevant to security teams, including how machine learning workflows, training processes, data pipelines, and deployment architectures affect risk and control.

The certification emphasizes securing AI systems across their full lifecycle, from protecting training data and model artifacts to securing build and deployment pipelines, maintaining model integrity, and monitoring live systems for misuse or degradation.

This course also addresses the use of AI within security operations, including how AI-driven detection, response, and automation tools introduce new benefits as well as risks such as false positives, blind spots, and model drift.

In addition, SecAI+ covers AI-specific threats such as adversarial machine learning, data poisoning, model manipulation, and prompt-based attacks, along with the governance, risk, and compliance requirements needed to manage AI systems responsibly in enterprise environments.

As part of this course you’ll also learn to:

• 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.

Through Firebrand’s Lecture | Lab | Review method, you’ll get certified faster than traditional training and access official courseware, learn from certified instructors, and train in a distraction-free environment.

Audience

This course is ideal for professionals who already hold or are working toward broader cybersecurity certifications and want to demonstrate additional depth in AI security—showcasing a valuable specialization that enhances their primary credentials.

Module 1: Basic AI concepts related to cybersecurity (17%)

• Explain core AI principles and terminology: Machine learning, deep learning, natural language processing, and automation.
• Identify AI applications in security: Use cases for AI in threat detection, defense, and security operations.
• Recognize AI-driven threats: Automated phishing, polymorphic malware, adversarial machine learning, and malicious use of generative AI.

Module 2: Securing AI systems (40%)

• Implement security controls: Protect AI systems, data, and models using robust technical safeguards.
• Secure AI deployment environments: Apply best practices across on-premises, cloud, and hybrid infrastructures.
• Mitigate adversarial risks: Defend against attacks targeting AI models, data pipelines, and inference layers. 

Module 3: Securing AI systems (40%)

• Implement security controls: Protect AI systems, data, and models using robust technical safeguards.
• Secure AI deployment environments: Apply best practices across on-premises, cloud, and hybrid infrastructures. 
• Mitigate adversarial risks: Defend against attacks targeting AI models, data pipelines, and inference layers. 

Module 4: Securing AI systems (40%)

• Implement security controls: Protect AI systems, data, and models using robust technical safeguards.
• Secure AI deployment environments: Apply best practices across on-premises, cloud, and hybrid infrastructures.
• Mitigate adversarial risks: Defend against attacks targeting AI models, data pipelines, and inference layers. 

Module 5: AI-assisted security (24%)

• Enhance detection and response: Use AI-driven tools to identify anomalies, detect threats, and accelerate incident remediation.
• Automate security workflows: Integrate AI for event triage, alert correlation, and response orchestration.
• Apply AI techniques in operations: Incorporate AI into threat modeling, behavior analysis, and continuous monitoring.

Module 6: AI governance, risk, and compliance (19%)

• Understand regulatory frameworks: Identify global governance requirements and their implications for AI adoption.
• Integrate GRC into AI projects: Incorporate governance, risk management, and compliance practices throughout the AI lifecycle.
• Ensure responsible AI use: Apply ethical guidelines, legal standards, and industry frameworks such as GDPR and NIST AI RMF.

Before attending this accelerated course, you should have experience of 3-4 years in IT, inclusive of 2+ years hands-on cybersecurity; Security+, CySA+, PenTest+, or equivalent recommended.

At the end of this accelerated course, you’ll sit the following exam at the Firebrand Training Centre:

CompTIA SecAI+ Exam series code: CY0-001 exam

• Duration: 60 minutes
• Format: multiple-choice and performance-based
• Number of questions: Maximum of 60, multiple-choice and performance-based
• Passing score: 600 (on a scale of 100–900)
• Languages: English
• Domains:
  • Basic AI concepts related to cybersecurity (17%)
  • Securing AI systems (40%)
  • AI-assisted security (24%)
  • AI governance, risk, and compliance (19%)