Mimir, Keeper of the Well of Wisdom
SecAI+ Exam Prep: Complete Guide to CompTIA SecAI+
https://www.udemy.com/course/secai-exam-prep-complete-guide-to-
comptia-secai/
Year : 2026
Language : English
Level : Intermediate Level
Category : IT & Software
Subcategory : IT Certifications
Duration : 26h 38m
Lectures : 79
Rating : 5.0/5 (2 reviews)
Students : 121
INSTRUCTOR(S)
HEADLINE
Master AI security, adversarial attacks, governance, and
compliance to pass the CompTIA SecAI+ CY0-001 exam in 2026.
WHAT YOU'LL LEARN
* Compare and contrast AI types including generative AI, LLMs,
transformers, GANs, and deep learning in applied
cybersecurity
contexts.
* Implement security controls for AI systems including model
guardrails, prompt firewalls, rate and token limits, and
endpoint access controls.
* Analyze AI attack scenarios and select compensating controls
for prompt injection, model poisoning, model theft, and
excessive agency.
* Use AI-enabled tools including MCP servers, IDE plug-ins,
and
chatbots to automate threat detection, triage, and incident
response workflows.
* Defend against AI-driven threats including deepfakes,
adversarial attacks, AI-generated malware, and automated
social engineering at scale.
* Navigate the EU AI Act, NIST AI RMF, ISO 42001, and OECD AI
principles to meet enterprise AI governance and compliance
obligations.
* Apply data security controls including encryption,
anonymization, masking, redaction, and minimization to
protect
AI systems and pipelines.
* Use OWASP LLM Top 10, MITRE ATLAS, and the MIT AI Risk
Repository to conduct structured AI threat modeling and risk
assessments.
REQUIREMENTS
* 3?4 years of IT experience with approximately 2 years of
hands-on cybersecurity experience is recommended by CompTIA
for this exam.
* CompTIA Security+, CySA+, or PenTest+ certification or
equivalent knowledge is strongly recommended before starting
this course.
* Basic understanding of networking, operating systems, and
core
cybersecurity concepts such as access control, encryption,
and
incident response.
* Familiarity with cloud computing fundamentals and at least
one
cloud platform such as AWS, Azure, or Google Cloud is
beneficial.
* No prior AI or machine learning experience is required, all
AI
concepts including LLMs, transformers, and model training
are
taught from the ground up.
* No specific software, lab environment, or hardware is
required
to complete the exam prep content in this course.
* A willingness to engage with scenario-based questions and
apply concepts across multiple domains simultaneously, as
the
real exam demands.
* A current or target role in cybersecurity, analyst,
engineer,
architect, pentester, GRC, or DevSecOps,to contextualize the
course material throughout.
WHO IS THIS COURSE FOR
* Security analysts, engineers, and architects who are
actively
preparing to pass the CompTIA SecAI+ CY0-001 certification
exam in 2026.
* IT professionals with an existing CompTIA certification
stack,
Security+, CySA+, or PenTest+, who want to add a dedicated
AI
security credential.
* SOC analysts and threat hunters who want to leverage AI for
anomaly detection, alert triage, behavioral analysis, and
incident response acceleration.
* Pentesters and red team operators expanding into AI attack
vectors, AI red teaming, and AI-assisted offensive security
techniques and tooling.
* GRC professionals and compliance officers who need to
operationalize the EU AI Act, NIST AI RMF, and responsible
AI
frameworks within their organizations.
* DevSecOps engineers and platform engineers integrating AI
into
CI/CD pipelines who need to understand the full AI security
and governance implications.
* Security professionals in the GCC, India, the US, the EU,
and
Canada targeting the fast-growing AI security specialization
and its associated role premium.
* Anyone building a credentialed foundation at the
intersection
of artificial intelligence and cybersecurity before the
SecAI+
becomes a standard market requirement.
DESCRIPTION
The CompTIA SecAI+ (CY0-001) is the first vendor-neutral
certification built at the intersection of artificial
intelligence and cybersecurity. It launched February 17, 2026,
and validates that you can secure AI systems, defend against
AI-
driven threats, leverage AI for security operations, and
govern
AI responsibly within an enterprise environment. This course
is
the most complete exam preparation resource available for it.
Across 17 sections and 82 modules, every official CY0-001 exam
objective from Version 2.0 of the CompTIA SecAI+ syllabus is
covered in full. The course is structured to mirror the
official
domain weighting so you always know where your study time is
going: Basic AI Concepts Related to Cybersecurity (17%),
Securing AI Systems (40%), AI-Assisted Security (24%), and AI
Governance, Risk and Compliance (19%). Domain 1 builds your AI
foundation from scratch. You will cover AI types including
generative AI, machine learning, deep learning, transformers,
LLMs, SLMs, and GANs, followed by prompt engineering
techniques,
data security in AI pipelines, RAG architecture, vector
storage,
embeddings, and security across the full AI development
lifecycle. Domain 2, the most heavily weighted domain at 40%,
is
where most candidates struggle and where this course goes
deepest. You will work through AI-specific threat modeling
using
the OWASP LLM Top 10, OWASP ML Security Top 10, MITRE ATLAS,
and
the MIT AI Risk Repository. You will then cover every category
of security control for AI systems: model guardrails, prompt
firewalls, rate and token limits, access controls for models,
data, agents and APIs, data security including encryption in
transit, at rest and in use, and monitoring and auditing for
AI
quality and compliance. The AI attack taxonomy is covered in
full across dedicated modules: prompt injection, indirect
prompt
injection, jailbreaking, model poisoning, data poisoning,
hallucination exploitation, input manipulation, bias
injection,
guardrail bypass, model inversion, model theft, membership
inference, AI supply chain attacks, transfer learning attacks,
model skewing, output integrity attacks, insecure plug-in
design, excessive agency, and model denial of service. Every
attack type is paired with its specific compensating controls
so
you can answer scenario-based and performance-based questions
with confidence. Domain 3 covers how AI enables and enhances
both defensive and offensive security operations: AI-powered
tools including IDE plug-ins, browser plug-ins, CLI plug-ins,
MCP servers, and chatbots, AI-driven attack vectors including
deepfakes, AI-generated payloads, automated reconnaissance,
and
AI-enhanced social engineering, and automation of security
tasks
through low-code scripting, document synthesis, incident
ticket
management, AI agents, and CI/CD pipeline security. Domain 4
covers the governance, risk, and compliance frameworks that
now
govern enterprise AI adoption globally: the EU AI Act risk
tiers
and compliance obligations, OECD AI principles, ISO 42001 and
ISO 23894, the NIST AI Risk Management Framework across all
four
functions, responsible AI principles, AI risk categories
including shadow AI and autonomous system risk, corporate AI
policy, data sovereignty, private versus public model
governance, third-party AI compliance evaluation, and GDPR
applied to AI systems. Every module closes with scenario-based
application and targeted exam tips mapped to the specific
CY0-001 objective. The SecAI+ is a new certification. Passing
it
in 2026 positions you ahead of the vast majority of the
cybersecurity workforce in a specialization that is already
creating dedicated job roles and increasing salary premiums
across the industry.
COURSE CONTENT
Chapter 1: Course Introduction & Exam Overview
1. Welcome to the SecAI+ Exam Prep Course
2. CY0-001 Exam Format, Domain Weightings and Scoring
3. Recommended Experience, Study Strategy and Course Roadmap
Chapter 2: AI Types, Techniques & Prompt Engineering
4. Core AI Types: Generative AI, Machine Learning, Deep
Learning
5. Transformers, NLP, LLMs, SLMs and Generative Adversarial
Networks (GANs)
6. Model Training: Supervised, Unsupervised and
Reinforcement Learning
7. Model Validation, Fine-Tuning, Epochs, Pruning and
Quantization
8. Prompt Engineering Fundamentals
9. System Prompts, User Prompts, System Roles and Prompt
Templates
Chapter 3: Data Security in AI
10. Data Processing for AI: Cleansing, Verification and
Lineage
11. Data Integrity, Provenance, Augmentation and Balancing
12. AI Data Types: Structured, Semi-Structured and
Unstructured
13. AI Output Watermarking
14. Retrieval-Augmented Generation (RAG): Vector Storage and
Embeddings
Chapter 4: Security Across the AI Lifecycle
15. The AI Lifecycle: Business Use Case Alignment and
Corporate Objectives
16. Secure Data Collection: Trustworthiness and Authenticity
17. Data Preparation, Model Development, Selection and
Evaluation Security
18. Deployment, Validation, Monitoring, Feedback and
Iteration
19. Human-Centric AI Design: Human-in-the-Loop, Oversight
and Validation
Chapter 5: AI Threat Modeling Resources
20. OWASP LLM Top 10: Critical Large Language Model Security
Risks
21. OWASP ML Security Top 10: Machine Learning Attack
Taxonomy
22. MIT AI Risk Repository: Systemic and Societal AI Risks
23. MITRE ATLAS: Adversarial Threat Landscape for AI Systems
24. CVE AI Working Group and AI-Specific Threat-Modeling
Frameworks
Chapter 6: Implementing Security Controls for AI Systems
25. Model Controls: Model Evaluation, Guardrails and Prompt
Templates
26. Gateway Controls: Prompt Firewalls, Rate Limits and
Token Limits
27. Input Quotas: Data Size, Quantity and Modality Limits
28. Endpoint Access Controls for AI Systems
29. Guardrail Testing and Validation Techniques
Chapter 7: Access Controls for AI Systems
30. Model Access Control: Who Can Query and Interact with AI
31. Data Access Controls in AI Pipelines
32. AI Agent Access Controls and Least Privilege Enforcement
33. Network and API Access Controls for AI Endpoints
Chapter 8: Data Security Controls for AI
34. AI Encryption Requirements: In Transit, At Rest and In
Use
35. Data Anonymization, Classification Labels and Data
Redaction
36. Data Masking and Data Minimization in AI Contexts
37. Data Safety Controls: Applying the Right Control for the
Scenario
Chapter 9: Monitoring and Auditing AI Systems
38. Prompt Monitoring: Query and Response Surveillance
39. Log Monitoring, Log Sanitization and Log Protection for
AI
40. Response Confidence Levels and Rate Monitoring
41. AI Cost Monitoring: Prompts, Storage, Response and
Processing Costs
42. Auditing AI for Quality and Compliance: Hallucinations,
Accuracy, Bias
Chapter 10: AI Attack Analysis & Compensating Controls
43. Model Poisoning and Data Poisoning Attacks
44. Hallucinations as an Attack Vector, Input Manipulation
and Bias Injection
45. Circumventing AI Guardrails and Manipulating Application
Integrations
46. Model Inversion, Model Theft and Membership Inference
Attacks
47. AI Supply Chain Attacks and Transfer Learning Attacks
48. Model Skewing, Output Integrity Attacks, Model DoS,
Insecure Plug-in Design
49. Prompt Injection and Jailbreaking: How They Work and How
to Defend
Chapter 11: AI-Enabled Security Tools & Use Cases
50. AI Security Tools Overview: IDE Plug-ins, Browser Plug-
ins and CLI Plug-ins
51. Chatbots, Personal Assistants and MCP Servers in
Security Operations
52. AI for Signature Matching, Code Quality, Linting and
Vulnerability Analysis
53. Automated Penetration Testing and Anomaly Detection with
AI
54. AI for Threat Modeling, Pattern Recognition and Fraud
Detection
55. AI for Incident Management, Summarization and
Translation
Chapter 12: How AI Enables & Enhances Attack Vectors
56. AI-Generated Content and Deepfakes: IMD
57. Adversarial Networks and AI-Powered Reconnaissance
58. AI-Enhanced Social Engineering and Obfuscation
Techniques
59. Automated Data Correlation and AI-Driven Attack Vector
Discovery
60. AI-Generated Payloads, Malware, Honeypots and DDoS
Amplification
Chapter 13: Automating Security Tasks with AI
61. Low-Code and No-Code AI Scripting for Security Workflows
62. Document Synthesis, Summarization and Incident Response
Ticket Management
63. AI-Assisted Change Management: Approvals, Deployment and
Rollback
64. AI Agents in Security Operations: Architecture and Use
Cases
65. CI/CD Security Automation: Code Scanning, SCA, Testing
and Deployment
Chapter 14: AI Governance Structures
66. AI Center of Excellence: Building the Organizational
Governance Foundation
67. AI Policies, Procedures and Decision-Making Frameworks
68. AI Roles: Data Scientist, AI Architect, ML Engineer and
Platform Engineer
69. MLOps, AI Security Architect, AI Governance Engineer,
Risk Analyst
Chapter 15: AI Risk Management & Responsible AI
70. Responsible AI Principles: Fairness, Reliability, Safety
and Transparency
71. Privacy, Explainability, Inclusiveness, Accountability
and Consistency in AI
72. AI Risks: Bias, Accidental Data Leakage, Reputational
and IP-Related Risks
73. Autonomous System Risks, Shadow IT and Shadow AI
Chapter 16: AI Compliance & Regulatory Frameworks
74. The EU AI Act: Risk Tiers, Prohibited Uses and
Compliance Obligations
75. OECD AI Principles and ISO AI Standards (ISO 42001, ISO
23894)
76. NIST AI Risk Management Framework: Govern, Map, Measure,
Manage
77. Corporate AI Policies: Sanctioned vs. Unsanctioned AI
and Sensitive DG
78. Data Sovereignty and Private vs. Public Model Governance
79. Third-Party AI Compliance Evaluations and GDPR in AI
Contexts
DATES
Published : 2026-08-15
Last Updated : 2026-08-14
If you fear the truth, dont come to my well.