SecAI+ (CY0-001) - Master AI Security - 900+ MCQs & 45 PBQs

Name: SecAI+ (CY0-001) - Master AI Security - 900+ MCQs & 45 PBQs
Rating: 5.0 (9 reviews)

AI Security Certification Prep | Risk Management, Governance, SecOps & Threat Detection Simplified

Created byCyber Pulse Academy, Cyber Pulse Academy

Last updated 5/2026

English

What you'll learn

Identify and explain core AI security concepts, terminology, and frameworks using real-world scenarios instead of dry memorization.
Analyze AI-related threats and vulnerabilities from both a Red Team (attacker) and Blue Team (defender) perspective.
Apply AI risk management and governance principles to practical cybersecurity situations encountered in the field.
Validate and measure their own knowledge through 700+ quiz questions, 2 full practice exams, and 15 graded assignments.
Recognize common mistakes and anti-patterns made by cybersecurity professionals when securing AI systems.
Confidently approach the CompTIA SecAI+ (CY0-001) certification exam with a structured, objective-by-objective preparation strategy.

Course content

6 sections • 36 lectures • 6h 35m total length

Course Introduction6:23
Introduction1:25

SecAI+ - 1.1 - AI Types and Techniques in Cybersecurity8:39
By the end of this section, you will be able to:
• Differentiate between Generative AI, Machine Learning, Deep Learning, and other core AI paradigms
• Explain how supervised, unsupervised, and reinforcement learning models are trained and validated
• Describe the role of Transformers, NLP, and LLMs in cybersecurity applications
• Apply prompt engineering techniques to interact with AI systems effectively
• Identify security implications of each AI type and training technique
SecAI+ - 1.1 - AI Types and Techniques in Cybersecurity - Part 1/213:02
SecAI+ - 1.1 - AI Types and Techniques in Cybersecurity - Part 2/211:38
SecAI+ - 1.1 - AI Types and Techniques in Cybersecurity - A
SecAI+ - 1.1 - AI Types and Techniques in Cybersecurity - B
SecAI+ - 1.1 - AI Types and Techniques in Cybersecurity - C
SecAI+ - 1.1 - AI Types and Techniques in Cybersecurity
SecAI+ - 1.2 - Data Security in AI Systems9:53
By the end of this section, you will be able to:
• Explain data cleansing, verification, and integrity concepts in the context of AI training data
• Differentiate between structured, semi-structured, and unstructured data and their security implications
• Describe data augmentation and balancing techniques and their role in security AI
• Understand AI watermarking techniques and their security applications
• Explain Retrieval-Augmented Generation (RAG) including vector storage and embeddings
SecAI+ - 1.2 - Data Security in AI Systems12:31
SecAI+ - 1.2 - Data Security in AI Systems - A
SecAI+ - 1.2 - Data Security in AI Systems - B
SecAI+ - 1.2 - Data Security in AI Systems - C
SecAI+ - 1.2 - Data Security in AI Systems
SecAI+ - 1.3 - Security Across the AI Lifecycle7:52
By the end of this section, you will be able to:
• Describe each phase of the AI lifecycle and its security considerations
• Explain the importance of business alignment and use case definition for AI security
• Apply human-centric AI design principles including HITL, human oversight, and validation
• Identify security controls and best practices for each phase of the AI lifecycle
• Understand the importance of monitoring, feedback, and iteration for maintaining AI security
SecAI+ - 1.3 - Security Across the AI Lifecycle8:54
SecAI+ - 1.3 - Security Across the AI Lifecycle - A
SecAI+ - 1.3 - Security Across the AI Lifecycle - B
SecAI+ - 1.3 - Security Across the AI Lifecycle

SecAI+ - 2.1 - AI Threat Modeling7:44
By the end of this section, you will be able to:
• Explain the purpose and structure of the OWASP Top 10 for LLM Applications and for Machine Learning
• Describe the MIT AI Risk Repository and how it categorizes AI risks
• Navigate MITRE ATLAS to identify adversarial tactics, techniques, and procedures targeting AI systems
• Explain the role of the CVE AI Working Group in tracking AI/ML vulnerabilities
• Apply traditional threat modeling frameworks, STRIDE, LINDDUN, PASTA, and Attack Trees, to AI systems
• Select the appropriate threat modeling framework for a given AI security scenario
SecAI+ - 2.1 - AI Threat Modeling - Part 1/319:06
SecAI+ - 2.1 - AI Threat Modeling - Part 2/211:14
SecAI+ - 2.1 - AI Threat Modeling - A
SecAI+ - 2.1 - AI Threat Modeling - B
SecAI+ - 2.1 - AI Threat Modeling - C
SecAI+ - 2.1 - AI Threat Modeling
SecAI+ - 2.2 - Implementing Security Controls for AI Systems10:12
By the end of this section, you will be able to:
• Explain the role of model evaluation in AI security, including red-teaming, adversarial testing, and bias assessment
• Design and implement model guardrails including input, output, and contextual guardrails
• Create secure prompt templates that prevent template injection and enforce input boundaries
• Architect gateway controls including prompt firewalls, rate limiting, input quotas, and modality restrictions
• Implement endpoint access controls for AI services
• Validate security controls through adversarial testing and continuous monitoring
• Compare attacker and defender perspectives on AI security controls
SecAI+ - 2.2 - Implementing Security Controls for AI Systems14:01
SecAI+ - 2.2 - Implementing Security Controls for AI Systems - A
SecAI+ - 2.2 - Implementing Security Controls for AI Systems - B
SecAI+ - 2.2 - Implementing Security Controls for AI Systems
SecAI+ - 2.3 - Access Controls for AI Systems7:01
By the end of this section, you will be able to:
• Design role-based and attribute-based access control models for AI models
• Implement data access controls for training data, inference data, and model outputs
• Design agent access controls including autonomous permissions, tool restrictions, and human approval gates
• Implement network and API security for AI systems including authentication, authorization, and zero-trust architecture
• Evaluate the attacker vs. defender perspective on AI access controls
• Identify common anti-patterns in AI access control implementation
SecAI+ - 2.3 - Access Controls for AI Systems6:27
SecAI+ - 2.3 - Access Controls for AI Systems - A
SecAI+ - 2.3 - Access Controls for AI Systems - B
SecAI+ - 2.3 - Access Controls for AI Systems
SecAI+ - 2.4 - Data Security Controls for AI Systems8:58
By the end of this section, you will be able to:
• Explain the encryption requirements for AI data in transit, at rest, and in use
• Implement data anonymization techniques including k-anonymity, l-diversity, and differential privacy
• Apply data classification labels to drive security controls for AI training and inference data
• Design data redaction and masking strategies for AI system inputs and outputs
• Apply data minimization principles throughout the AI lifecycle
• Evaluate the security implications of different data protection techniques in AI contexts
SecAI+ - 2.4 - Data Security Controls for AI Systems16:15
SecAI+ - 2.4 - Data Security Controls for AI Systems - A
SecAI+ - 2.4 - Data Security Controls for AI Systems - B
SecAI+ - 2.4 - Data Security Controls for AI Systems
SecAI+ - 2.5 - Monitoring and Auditing AI Systems9:19
By the end of this section, you will be able to:
• Design prompt monitoring systems for both user inputs (queries) and AI outputs (responses)
• Implement log sanitization to prevent PII leakage while preserving security value
• Interpret AI response confidence levels and implement threshold-based alerting
• Monitor rate patterns and cost anomalies as security indicators
• Detect and score AI hallucinations for security-relevant applications
• Conduct bias and fairness audits across AI model demographics and outcomes
• Integrate AI system monitoring with existing SIEM and security operations workflows
SecAI+ - 2.5 - Monitoring and Auditing AI Systems16:47
SecAI+ - 2.5 - Monitoring and Auditing AI Systems - A
SecAI+ - 2.5 - Monitoring and Auditing AI Systems - B
SecAI+ - 2.5 - Monitoring and Auditing AI Systems
SecAI+ - 2.6 - AI Attack Analysis and Compensating Controls8:03
By the end of this section, you will be able to:
• Identify and explain 21 distinct AI-specific attack types, including their mechanisms and real-world examples
• Analyze each attack from both red team (attacker) and blue team (defender) perspectives
• Map specific compensating controls to each attack type
• Implement prompt firewalls, model guardrails, access controls, and rate limiting to defend AI systems
• Apply the principle of least privilege to AI system design and operation
• Recognize attack combinations and multi-vector scenarios
• Design a comprehensive AI defense strategy using layered compensating controls
SecAI+ - 2.6 - AI Attack Analysis and Compensating Controls - Part 1/221:22
SecAI+ - 2.6 - AI Attack Analysis and Compensating Controls - Part 2/318:14
SecAI+ - 2.6 - AI Attack Analysis and Compensating Controls - Part 3/311:24
SecAI+ - 2.6 - AI Attack Analysis and Compensating Controls - A
SecAI+ - 2.6 - AI Attack Analysis and Compensating Controls - B
SecAI+ - 2.6 - AI Attack Analysis and Compensating Controls - C
SecAI+ - 2.6 - AI Attack Analysis and Compensating Controls

SecAI+ - 3.1 - AI-Enabled Security Tools and Use Cases7:30
By the end of this section, you will be able to:
• Identify and categorize the major types of AI-enabled security tools (IDE plug-ins, browser plug-ins, CLI plug-ins, chatbots/personal assistants, and MCP servers)
• Describe at least ten distinct security use cases where AI provides defensive capabilities
• Evaluate the strengths, limitations, and security risks of different AI tool categories
• Recommend appropriate AI security tools for given organizational contexts
• Recognize the security implications of integrating AI tools into development and operations workflows
SecAI+ - 3.1 - AI-Enabled Security Tools and Use Cases16:31
SecAI+ - 3.1 - AI-Enabled Security Tools and Use Cases - A
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SecAI+ - 3.1 - AI-Enabled Security Tools and Use Cases - C
SecAI+ - 3.1 - AI-Enabled Security Tools and Use Cases
SecAI+ - 3.2 - AI-Enhanced Attack Vectors8:21
By the end of this section, you will be able to:
• Describe how AI-generated content (deepfakes) is used for impersonation and disinformation
• Explain the role of adversarial networks in creating more effective attacks
• Identify how AI powers reconnaissance, social engineering, and obfuscation techniques
• Analyze the components of automated attack generation including payload creation and malware development
• Evaluate defensive strategies for countering AI-enhanced attacks
• Recognize the security implications of AI-driven DDoS and honeypot evasion techniques
SecAI+ - 3.2 - AI-Enhanced Attack Vectors11:10
SecAI+ - 3.2 - AI-Enhanced Attack Vectors - A
SecAI+ - 3.2 - AI-Enhanced Attack Vectors - B
SecAI+ - 3.2 - AI-Enhanced Attack Vectors - C
SecAI+ - 3.2 - AI-Enhanced Attack Vectors
SecAI+ - 3.3 - Automating Security Workflows with AI9:12
By the end of this section, you will be able to:
• Describe how AI scripting tools (including low-code and no-code approaches) automate security tasks
• Explain how AI powers document synthesis and summarization in security operations
• Outline the role of AI in incident response ticket management
• Describe AI-assisted change management including automated approvals and deployment rollback
• Explain how AI agents operate in security operations environments
• Identify AI applications across the CI/CD pipeline security lifecycle
SecAI+ - 3.3 - Automating Security Workflows with AI8:09
SecAI+ - 3.3 - Automating Security Workflows with AI
SecAI+ - 3.3 - Automating Security Workflows with AI

SecAI+ - 4.1 - Organizational AI Governance Structures9:14
By the end of this section, you will be able to:
• Explain the purpose and function of an AI Center of Excellence (AI CoE)
• Design comprehensive AI policies and procedures that address the full AI lifecycle
• Describe the responsibilities, skills, and security relevance of 10 key AI organizational roles
• Create governance structures that bridge technical AI teams with business and legal stakeholders
• Map AI roles to a RACI matrix for clear accountability
SecAI+ - 4.1 - Organizational AI Governance Structures15:57
SecAI+ - 4.1 - Organizational AI Governance Structures
SecAI+ - 4.1 - Organizational AI Governance Structures
SecAI+ - 4.2 - AI Risk Identification and Management7:14
By the end of this section, you will be able to:
• Articulate and apply the nine Responsible AI principles (fairness, reliability, transparency, privacy, explainability, inclusiveness, accountability, consistency, and awareness training)
• Identify and categorize the six major AI risk categories: bias, data leakage, reputational loss, accuracy degradation, IP risks, and autonomous system risks
• Explain the unique dangers of shadow IT and shadow AI
• Map AI risks to appropriate mitigation strategies using structured frameworks
• Evaluate real-world case studies for governance failures and risk management gaps
SecAI+ - 4.2 - AI Risk Identification and Management16:18
SecAI+ - 4.2 - AI Risk Identification and Management
SecAI+ - 4.2 - AI Risk Identification and Management
SecAI+ - 4.3 - Compliance Frameworks for AI7:35
By the end of this section, you will be able to:
• Explain the key requirements, risk classifications, and penalties of the EU AI Act
• Describe the principles and recommendations of the OECD AI Standards
• Identify relevant ISO AI standards and their applications
• Apply the four core functions of the NIST AI Risk Management Framework (AI RMF) to organizational scenarios
• Develop corporate AI policies that address sanctioned vs. unsanctioned use, public vs. private models, and sensitive data governance
• Evaluate third-party AI compliance using structured assessment frameworks
• Explain data sovereignty requirements and their impact on AI system design and deployment
SecAI+ - 4.3 - Compliance Frameworks for AI12:22
SecAI+ - 4.3 - Compliance Frameworks for AI
SecAI+ - 4.3 - Compliance Frameworks for AI

Requirements

No prior AI or cybersecurity certification is required — this course is designed to welcome complete beginners.
A basic understanding of general IT concepts (such as networks, operating systems, or the internet) is helpful but not mandatory.
A willingness to learn through stories, examples, and scenarios rather than textbook-style memorization is all you need.
A free CompTIA account is recommended so you can cross-reference the official exam objectives as you progress.

Description

Are you tired of staring at flashcards full of definitions you don't truly understand? Do you want to pass the CompTIA SecAI+ (CY0-001) exam, and actually know what you're talking about when you walk out of that testing center?

This course was built with one mission: make AI security genuinely easy to understand, for every type of learner, especially beginners.

Most exam prep courses dump acronyms and bullet points at you and call it teaching. I took the opposite approach. Every chapter in this course opens with a Real-World Mission Brief that places you inside a realistic scenario. From there, you'll follow a Story-Driven Scenario that walks the concept through lived experience. Keywords are broken down in Vocabulary Without Pain, plain-language definitions tied to real-life analogies, not dictionary entries. You'll sharpen your instincts with the Threat Hunter's Eye lens, challenge your perspective with Red Team vs. Blue Team Views, and protect yourself from exam failure by studying the most Common Mistakes & Anti-Patterns that trip up even experienced professionals.

Here's what's packed inside:

800+ quiz questions to reinforce learning after every section
2 mock exams that simulate the real CY0-001 experience
15 assignments (45 scenario-based questions) to test applied understanding
Complete coverage of all official CompTIA SecAI+ exam objectives

Whether you're a first-timer in cybersecurity or a seasoned pro adding AI security to your credentials, this course meets you where you are. By the end, you won't just be ready to pass, you'll be ready to apply what you've learned on the job.
Enroll now and start your AI security journey the right way, with understanding, not memorization.

NOTE!! The purpose of this Udemy course is to serve as a supplementary resource, designed specifically to allow students to practice and reinforce the concepts they have learned from the official CompTIA SecAI+ certification course. This course should be used as a practical tool to apply theoretical knowledge, enhance understanding, and improve retention as you prepare for the CompTIA SecAI+ exam. It is recommended to use this course in conjunction with the official materials for a comprehensive learning experience.

Who this course is for:

Absolute beginners in cybersecurity who want to enter the field through one of the most forward-looking certifications available.
IT professionals and security practitioners who want to formalize their AI security knowledge with a CompTIA credential.
Students who have struggled with traditional study methods and are looking for a concept-first, story-driven learning approach.
CompTIA certificate holders (Security+, CySA+, Network+) who want to add AI security expertise to their existing portfolio.
Career changers who want a structured, exam-ready path into the fast-growing field of AI-powered cybersecurity.

SecAI+ (CY0-001) - Master AI Security - 900+ MCQs & 45 PBQs

What you'll learn

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Course content

Introduction2 lectures • 8min

Domain 1: Basic AI concepts related to cybersecurity7 lectures • 1hr 12min

Domain 2: Securing AI systems15 lectures • 3hr 6min

Domain 3: AI-assisted security6 lectures • 1hr 1min

Domain 4: AI governance, risk, and compliance6 lectures • 1hr 9min

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Requirements

Description

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