AI Risk & Security · CompTIA SecAI+

AI Risk & Security

Understand AI.
Secure Its Use.

Explore how AI changes security decisions. Connect AI foundations, system protection, security applications, and governance while building a structured SecAI+ preparation path.

Try The Learning PreviewExplore The Full Curriculum

Course Access And All Access OptionsOnline · Self Paced · Connected Study Tools

Two colleagues examining information on a computer.
CompTIA SecAI+ certification logo
Learn With ContextUnderstand The Reason Behind The Response.

An AI output still needs context, verification, and accountable human decisions. Useful security begins with understanding where trust is being placed.

Learn. Connect. Apply.
15Complete Lessons
810Flashcards
720Practice Questions And Activities
120Days Of Individual Access

AI Risk & Security gives you a structured place to begin and a useful place to return. Clear lessons establish the concepts. Flashcards keep important distinctions close. Practice helps you explain why one response fits a situation better than another.

01 A Foundation With Practical Value

Build Understanding You Can Use.

AI adds new dependencies to familiar security responsibilities. Understanding the data, the system, the output, and the people using it helps you ask more useful questions about risk.

Explore AI techniques, data security, and the AI lifecycle before examining the risks that arise around models and their connected systems. Build the vocabulary needed to ask useful questions about AI adoption.

Study access, controls, monitoring, attacks, and AI-assisted security alongside governance and responsible AI principles. The learning path connects technical protection to the roles and processes that support safer use.

What Makes The Learning Useful

An AI output still needs context, verification, and accountable human decisions. Useful security begins with understanding where trust is being placed.

Colleagues discussing a whiteboard covered with planning notes.
Connect Knowledge With A Real Decision.
One Situation. Several Connected Questions.

A Team Wants To Put An AI Assistant Into Its Workflow.

The demonstration looks helpful. The next questions concern the information it can access, the actions it can take, and how people will judge its output.

  1. Understand

    What Is The System Being Asked To Do?

    Define the intended task, inputs, outputs, and limits. Connect the AI capability to the actual business use before choosing safeguards.

  2. Protect

    Where Could Data Or Instructions Be Exposed?

    Consider the model, prompts, data sources, integrations, and permissions. Follow the trust boundaries around information and actions.

  3. Validate

    How Will People Check The Result?

    Identify where outputs require review, how failures could appear, and which monitoring or evaluation practices support responsible use.

  4. Govern

    Who Is Responsible As It Changes?

    Connect ownership, policy, risk assessment, lifecycle controls, and documentation to the system’s continuing use.

02 Your Course At A Glance

4 Connected Areas. One Learning Path.

The 15 lessons bring AI security into a clear sequence. Explore each area, follow the full outline, and use the connections between topics to guide a more useful review.

01

AI Concepts, Data And Lifecycle

Build the vocabulary for AI systems, data, models, and their security implications. Understand the components before evaluating their use.

3 Lessons · Lessons, Flashcards And PracticeExplore This Area
02

Protecting AI Systems

Examine protection across the AI lifecycle, including data, systems, interactions, and the trust boundaries around integrations.

6 Lessons · Lessons, Flashcards And PracticeExplore This Area
03

AI In Security Operations

Explore where AI can support security work and why validation, oversight, and operational context still matter.

3 Lessons · Lessons, Flashcards And PracticeExplore This Area
04

AI Governance And Risk

Connect policies, risk decisions, accountability, and compliance considerations to responsible AI adoption.

3 Lessons · Lessons, Flashcards And PracticeExplore This Area

You can follow the sequence from the beginning, explore a familiar area, or use the full curriculum to find a topic that needs more review.

03 A Topic You Can Try Now

Learn It. Recall It.
Apply It.

Try one course topic in three different ways. Start with the lesson explanation, turn over the flashcard, then choose an answer and read the reasoning. Explore the material before deciding how you want to study.

CompTIA SecAI+
CERTIFY ACADEMY · LESSONAI Risk & SecurityCompTIA SecAI+ Preparation
Lesson 1.1

AI Types and Techniques in Cybersecurity

33Topics Inside

AI is changing how cybersecurity teams examine threats, interpret evidence, and support critical decisions. Lesson 1.1 introduces the AI types and techniques that shape this work and connects them to realistic security scenarios. The discussion shows where these capabilities provide value and where their limitations create risk. Responsible use depends on secure data handling and effective monitoring. Human judgment and clear governance remain essential. This foundation prepares readers to evaluate AI-enabled security practices with greater precision and confidence.

Explore All 15 Lessons
You Will Explore
  • Explain the role of Introduction to AI in Cybersecurity in secure and responsible AI-enabled cybersecurity workflows.
  • Compare Generative AI Fundamentals with related AI security capabilities, risks, and governance controls.
  • Apply access control and data protection to the lesson scenarios. Use monitoring and validation to assess AI behavior. Maintain human oversight of the resulting decisions.
Why Three Ways?

Reading develops understanding. Recall shows what you can bring back without the explanation in front of you. Practice asks you to use the idea in a situation. Moving between the three makes your next review more focused.

04 Your Starting Point

Bring Your Experience.
Build From Where You Are.

You do not have to arrive with the same background as another learner. The broad course structure gives you several ways to make the material relevant to your next step.

Hover, focus, or tap a card to explore the learning approach.

Building AI Literacy

Understand The Security Questions.

Give AI terminology a practical context without treating every new feature as a solved problem.

Working In Security

Extend Your Existing Perspective.

Connect established security responsibilities with AI systems and their dependencies.

Preparing For SecAI+

Organize A Broad New Subject.

Follow a course sequence that connects foundations, protection, application, and oversight.

A Course You Can Return To

Learning Fits Better
When The Next Step Is Clear.

The course gives you more than a collection of pages. Use its structure to turn a broad subject into a series of study sessions you can follow, revisit, and adapt.

Make The Time Yours.
A Focused Session

Begin With One Topic.

Choose a lesson and follow the explanation far enough to understand the distinction being made. Write down the idea in your own words, then use a flashcard or a practice question to see what you can explain without the page.

A clear session goal makes the next return easier. You know where you stopped and what deserves another look.

A More Useful Second Pass

Bring A Question Back To The Lesson.

Come back with a question: can you explain the difference between a useful generated response and a verified security finding? Revisit the lesson, test your recall, and compare the reasoning in practice before moving on.

The same lesson can become more useful on a second visit because you now know the part you want to understand better.

Give The Difficult Topic Time.

05 The Complete Course Library

See The Depth
Before You Begin.

Explore all 15 lessons across 4 course areas. Open a lesson to see its learning goals and detailed topics, or search for a concept you want to understand better. The outline stays visible so you can judge the depth before enrolling.

15Lessons To ExploreComplete Outline Below
15 Of 15 Lessons
01

AI Concepts, Data And Lifecycle

3 Lessons
1.1
AI Types And Techniques In Cybersecurity33 Topics · Lessons, Flashcards & Practice

What You Will Learn

  • Explain the role of Introduction to AI in Cybersecurity in secure and responsible AI-enabled cybersecurity workflows.
  • Compare Generative AI Fundamentals with related AI security capabilities, risks, and governance controls.
  • Apply access control and data protection to the lesson scenarios. Use monitoring and validation to assess AI behavior. Maintain human oversight of the resulting decisions.
  • Identify data leakage and model misuse across the lesson topics. Recognize overreliance and compliance concerns. Assess the operational risks as well.
  • Select defensible actions that align AI security decisions with policy, business value, and accountable ownership.

Inside The Lesson

  • Introduction to AI in Cybersecurity
  • Generative AI Fundamentals
  • Machine Learning Fundamentals
  • Statistical Learning Fundamentals
  • Machine Learning vs Statistical Learning
  • Deep Learning and Neural Networks
  • Transformers and Attention-Based Models
  • Comparing AI Approaches in Cybersecurity Scenarios
  • Scenario-Based Quiz
  • Natural Language Processing in Cybersecurity
  • Large Language Models
  • Small Language Models
  • Generative Adversarial Networks
  • Language Models for Security Analysis
  • AI for Log, Alert, and Report Interpretation
  • AI for Security Communication and Summarization
  • Selecting the Right AI Type for a Security Use Case
  • Overview of AI Training Processes
  • Supervised Learning
  • Unsupervised Learning
  • Reinforcement Learning
  • Model Validation Techniques
  • Fine-Tuning Strategies
  • Epochs in Model Training
  • Pruning, Quantization, and Training Optimization
  • Fundamentals of Prompt Engineering
  • System Prompts
  • User Prompts
  • System Roles and Context Control
  • One-Shot Prompting
  • Multi-Shot Prompting
  • Zero-Shot Prompting
  • Prompt Templates and Secure Prompt Reusability
Open AI Risk & Security In Student Center
1.2
Data Security In AI Systems33 Topics · Lessons, Flashcards & Practice

What You Will Learn

  • Explain how data security supports reliable and responsible AI operations. Account for sources and models as well as services and users.
  • Compare data classification and handling requirements across common AI workflows and environments.
  • Apply encryption and access restrictions to realistic AI data scenarios. Use retention controls and provenance checks. Select suitable privacy safeguards.
  • Identify exposure paths that can compromise sensitive data during AI development and operational use.
  • Select defensible protections that support business priorities. Align data decisions with policy and risk. Keep ownership clear.

Inside The Lesson

  • Data Processing Fundamentals
  • Data Cleansing Techniques
  • Data Verification Methods
  • Data Lineage and Traceability
  • Data Integrity in AI Systems
  • Data Provenance and Trust
  • Data Augmentation Techniques
  • Data Balancing Strategies
  • Scenario-Based Quiz
  • Introduction to AI Data Types
  • Structured Data
  • Semi-Structured Data
  • Unstructured Data
  • Comparing Structured, Semi-Structured, and Unstructured Data
  • Data Type Selection for AI Security Use Cases
  • Security Risks by Data Type
  • Data Type Handling Best Practices
  • Data Protection in AI Systems
  • Watermarking Fundamentals
  • Watermarking for AI-Generated Content
  • Watermarking for Data Traceability
  • Data Trust and Content Authenticity
  • Limitations of Watermarking
  • Watermarking in Security Operations
  • Protecting AI Data Sources
  • Introduction to Retrieval-Augmented Generation
  • RAG Architecture Fundamentals
  • Vector Storage Systems
  • Embeddings Fundamentals
  • Semantic Search in AI Systems
  • Secure Retrieval Design
  • Data Leakage Risks in RAG
  • Protecting AI Knowledge Sources
Open AI Risk & Security In Student Center
1.3
Security In The AI Lifecycle33 Topics · Lessons, Flashcards & Practice

What You Will Learn

  • Explain how lifecycle security supports trustworthy AI operations from initial design through controlled retirement and disposal.
  • Compare security responsibilities across planning, development, validation, deployment, operation, and secure retirement activities.
  • Apply reviews, testing, documentation, monitoring, incident response, and change controls to realistic AI lifecycle scenarios and operational decisions.
  • Identify lifecycle gaps allowing vulnerabilities, drift, misuse, or unclear accountability to persist in operations.
  • Select defensible actions that align lifecycle decisions with policy, evidence, ownership, governance, and operational priorities.

Inside The Lesson

  • AI Use Case Identification
  • Aligning AI with Corporate Objectives
  • Security Value of AI Use Cases
  • Risk vs Value Analysis
  • Stakeholder and Business Impact Considerations
  • Security Requirements Planning
  • Ethical and Operational Considerations
  • Strategic AI Implementation
  • Scenario-Based Quiz
  • Overview of the MIT AI Risk Repository
  • AI Risk Categorization Models
  • Identifying AI System Risks
  • Mapping Risks to Business Use Cases
  • Risk Scoring Approaches
  • Risk Documentation Practices
  • Integrating AI Risk into Security Strategy
  • Real-World AI Risk Scenarios
  • Model Development Lifecycle
  • Model Selection Fundamentals
  • Secure Model Development Practices
  • Model Evaluation Fundamentals
  • Validation Processes
  • Deployment Planning
  • Secure AI Deployment Considerations
  • Lifecycle Risk Review Before Production
  • Monitoring AI Systems
  • AI System Maintenance
  • Feedback and Iteration
  • Continuous Improvement
  • Human-Centric AI Design Principles
  • Human-in-the-Loop Systems
  • Human Oversight
  • Human Validation
Open AI Risk & Security In Student Center
02

Protecting AI Systems

6 Lessons
2.1
AI Threat Modeling Resources And Frameworks34 Topics · Lessons, Flashcards & Practice

What You Will Learn

  • Explain clearly how threat modeling clarifies AI assets, trust boundaries, adversaries, attack paths, impacts, and mitigations.
  • Compare established threat modeling resources and frameworks for AI system contexts and risks.
  • Apply asset analysis, misuse cases, trust boundaries, attack paths, and mitigation mapping to realistic AI scenarios and workflows.
  • Identify hidden assumptions that weaken AI threat models and distort security priorities or ownership.
  • Select defensible methods that align threat analysis with evidence, scope, accountability, and business impact priorities.

Inside The Lesson

  • Introduction to OWASP for AI
  • OWASP LLM Top 10 Overview
  • Prompt Injection Risks
  • Sensitive Data Exposure Risks
  • Model Misuse and Abuse Cases
  • OWASP Machine Learning Security Top 10
  • Common ML Vulnerabilities
  • Applying OWASP Frameworks in Practice
  • Scenario-Based Quiz
  • Overview of the MIT AI Risk Repository
  • AI Risk Categorization Models
  • Identifying AI System Risks
  • Mapping Risks to Business Use Cases
  • Risk Scoring Approaches
  • Risk Documentation Practices
  • Integrating AI Risk into Security Strategy
  • Real-World AI Risk Scenarios
  • Scenario Quiz
  • Introduction to MITRE ATLAS
  • Adversarial AI Tactics
  • Techniques and Procedures
  • Mapping AI Attacks to ATLAS
  • Detection Strategies
  • Defensive Countermeasures
  • Threat Intelligence Integration
  • Applying ATLAS in Security Operations
  • Introduction to the CVE AI Working Group
  • AI Vulnerability Identification
  • Reporting AI Vulnerabilities
  • Tracking and Classifying AI Vulnerabilities
  • Exploitability Considerations
  • AI-Specific Threat Modeling Methods
  • Attack Surface Identification
  • Continuous Threat Modeling for AI Systems
Open AI Risk & Security In Student Center
2.2
Security Controls For AI Systems33 Topics · Lessons, Flashcards & Practice

What You Will Learn

  • Explain how layered security controls reliably reduce AI risk across models, data, infrastructure, applications, and operations.
  • Compare preventive, detective, corrective, and compensating controls for AI system exposures and needs.
  • Apply control selection, defense-in-depth, validation, monitoring, oversight, and governance to realistic AI security scenarios and complex operational workflows.
  • Identify control gaps that create unmanaged exposure or false confidence in AI system protection.
  • Select defensible safeguards that align technical risk with documented policy, evidence, ownership, and operational priorities.

Inside The Lesson

  • Model Security Fundamentals
  • Model Evaluation Techniques
  • Model Guardrails
  • Prompt Template Controls
  • Output Filtering Mechanisms
  • Bias and Safety Controls
  • Secure Model Configuration
  • Model Control Best Practices
  • Scenario-Based Quiz
  • Gateway Security Overview
  • Prompt Firewalls
  • Rate Limiting Strategies
  • Token Limit Controls
  • Input Quotas
  • Data Size Controls
  • Quantity Controls
  • Securing Prompt Input Channels
  • Modality Limit Fundamentals
  • Text Input Restrictions
  • File Upload Restrictions
  • Image and Audio Input Restrictions
  • Endpoint Access Controls
  • API Access Enforcement
  • Abuse Prevention Controls
  • Secure Input Enforcement Best Practices
  • Guardrail Testing Fundamentals
  • Validation Techniques
  • Adversarial Testing
  • Prompt Boundary Testing
  • Safety Testing
  • Performance Impact Testing
  • Continuous Guardrail Validation
  • Reporting Guardrail Test Results
Open AI Risk & Security In Student Center
2.3
Access Control In AI Systems33 Topics · Lessons, Flashcards & Practice

What You Will Learn

  • Explain how access control protects AI data, models, tools, actions, services, administrative functions, accounts, and users.
  • Compare authentication, authorization, least privilege, separation of duties, and approvals in AI environments.
  • Apply role design, privilege reviews, secret protection, service controls, and monitoring to realistic AI access scenarios and workflows.
  • Identify excessive permissions that directly enable misuse, escalation, data exposure, or unapproved AI actions.
  • Select defensible access decisions that align documented operational need with policy, evidence, ownership, and accountability.

Inside The Lesson

  • Model Access Fundamentals
  • Role-Based Model Access
  • Attribute-Based Model Access
  • Model Access Policies
  • Privileged Model Access
  • Model Authentication Requirements
  • Model Access Monitoring
  • Secure Model Exposure
  • Scenario-Based Quiz
  • Data Access Fundamentals
  • Data Segmentation
  • Least Privilege for AI Data
  • Data Access Policies
  • Sensitive Data Handling
  • Monitoring Data Access
  • Auditing Data Usage
  • Data Access Enforcement
  • AI Agent Fundamentals
  • Agent Permissions
  • Task-Based Agent Access
  • Agent Identity Management
  • Secure Agent Communication
  • Monitoring Agent Behavior
  • Limiting Agent Capabilities
  • Securing Autonomous Actions
  • Network Access Control Fundamentals
  • API Access Control Fundamentals
  • API Authentication
  • API Authorization
  • Rate Limiting for APIs
  • Network Segmentation
  • API Monitoring
  • Securing AI Endpoints
Open AI Risk & Security In Student Center
2.4
Data Security Controls For AI Systems33 Topics · Lessons, Flashcards & Practice

What You Will Learn

  • Explain how data controls protect AI confidentiality, integrity, availability, privacy, provenance, and authorized use across operations.
  • Compare masking, encryption, isolation, validation, retention, recovery, and loss prevention for AI data.
  • Apply layered data safeguards across ingestion, storage, training, inference, sharing, and deletion in realistic AI scenarios and workflows.
  • Identify control weaknesses that expose AI data to leakage, corruption, misuse, or unauthorized retention.
  • Select defensible data protections that align risk treatment with policy, evidence, ownership, and operational needs.

Inside The Lesson

  • Encryption Fundamentals
  • Encryption in Transit
  • Encryption at Rest
  • Encryption in Use
  • Key Management
  • Cryptographic Standards
  • Secure Encryption Implementation
  • Encryption Best Practices for AI Systems
  • Scenario-Based Quiz
  • Data Anonymization Fundamentals
  • Privacy Principles for AI Data
  • Data De-Identification
  • Risk of Re-Identification
  • Privacy-Preserving AI Techniques
  • Secure Data Sharing
  • Anonymization Limitations
  • Anonymization Best Practices
  • Data Classification Fundamentals
  • Data Sensitivity Levels
  • Data Classification Labels
  • Metadata and Label Management
  • Automated Classification
  • Data Minimization Principles
  • Limiting AI Data Collection
  • Reducing Data Exposure in AI Systems
  • Data Redaction Fundamentals
  • Data Masking Strategies
  • Tokenization
  • Data Obfuscation
  • Secure Data Presentation
  • Preventing Data Leakage
  • Secure Data Retention and Disposal
  • Data Safety Control Best Practices
Open AI Risk & Security In Student Center
2.5
Monitoring And Auditing AI Systems33 Topics · Lessons, Flashcards & Practice

What You Will Learn

  • Explain clearly how monitoring and auditing create visibility into AI behavior, access, changes, controls, and outcomes.
  • Compare logs, metrics, traces, alerts, reviews, and audit evidence for AI oversight needs.
  • Apply event collection, retention, correlation, alerting, review, and escalation practices to realistic AI monitoring scenarios and response operations.
  • Identify critical visibility gaps that conceal misuse, drift, control failure, or unauthorized AI activity.
  • Select defensible monitoring decisions that align evidence needs with risk, policy, ownership, and response priorities.

Inside The Lesson

  • Prompt Monitoring Fundamentals
  • Query Monitoring
  • Response Monitoring
  • Detecting Malicious Prompts
  • Monitoring Prompt Patterns
  • Behavioral Analysis
  • Alerting Mechanisms
  • Prompt Monitoring Best Practices
  • Scenario-Based Quiz
  • AI Logging Fundamentals
  • Log Collection
  • Log Monitoring
  • Log Sanitization
  • Log Protection
  • Secure Log Storage
  • Log Integrity Controls
  • Logging Best Practices for AI Systems
  • Response Confidence Level Fundamentals
  • Confidence Score Interpretation
  • Rate Monitoring
  • Prompt Cost Monitoring
  • Storage Cost Monitoring
  • Response Cost Monitoring
  • Processing Cost Monitoring
  • Cost Optimization and Abuse Detection
  • AI Auditing Fundamentals
  • Auditing for Hallucinations
  • Accuracy Auditing
  • Bias and Fairness Auditing
  • Access Auditing
  • Compliance Auditing
  • Audit Reporting
  • Continuous AI Auditing
Open AI Risk & Security In Student Center
2.6
AI Attacks And Compensating Controls34 Topics · Lessons, Flashcards & Practice

What You Will Learn

  • Explain how common AI attacks directly target data, models, prompts, interfaces, tools, dependencies, and human decisions.
  • Compare preventive safeguards and compensating controls for different AI attack paths and constraints.
  • Apply validation, isolation, monitoring, rate limits, approvals, and fallback measures to realistic AI attack scenarios and operational workflows.
  • Identify residual risks that remain when preferred AI security controls cannot be implemented fully.
  • Select defensible compensating controls that align constraints with evidence, ownership, monitoring, and risk tolerance requirements.

Inside The Lesson

  • Prompt Injection Fundamentals
  • Jailbreaking Techniques
  • Input Manipulation
  • Circumventing AI Guardrails
  • Prompt Exploitation Scenarios
  • Insecure Output Handling
  • Detection Techniques
  • Prevention Strategies
  • Scenario-Based Quiz
  • Data Poisoning Fundamentals
  • Model Poisoning Techniques
  • Training Data Manipulation
  • Introducing Biases
  • Hallucination Risks
  • Output Integrity Attacks
  • Poisoning Detection and Impact Analysis
  • Mitigation Strategies
  • Scenario Quiz
  • Model Inversion Attacks
  • Model Theft
  • Membership Inference
  • Transfer Learning Attacks
  • Model Skewing
  • Sensitive Information Disclosure
  • Exploitation Evidence Analysis
  • Defensive Controls
  • AI Supply Chain Attacks
  • Insecure Plug-in Design
  • Manipulating Application Integrations
  • Model Denial of Service
  • Excessive Agency
  • Overreliance on AI
  • Prompt Firewalls and Model Guardrails
  • Access Controls and Rate Limiting
Open AI Risk & Security In Student Center
03

AI In Security Operations

3 Lessons
3.1
AI-Enabled Security Tools And Use Cases33 Topics · Lessons, Flashcards & Practice

What You Will Learn

  • Explain how AI-enabled tools support detection, triage, investigation, intelligence, vulnerability management, automation, and decisions across operations.
  • Compare AI security use cases by value, risk, evidence quality, and oversight requirements.
  • Apply workflow design, validation, human review, monitoring, and outcome measures to realistic AI-enabled security scenarios and operational workflows.
  • Identify use cases where unreliable outputs or unclear ownership can weaken security operations significantly.
  • Select defensible tools that align operational value with evidence, risk, accountability, and measurable security outcomes.

Inside The Lesson

  • Introduction to AI-Enabled Security Tools
  • IDE Plug-Ins for Security Workflows
  • Browser Plug-Ins for Security Tasks
  • CLI Plug-Ins and Command-Line AI Support
  • Chatbots in Security Operations
  • Personal AI Assistants for Security Teams
  • Model Context Protocol Servers
  • Integrating AI Tools into Security Workflows
  • Scenario-Based Quiz
  • Signature Matching Techniques
  • Anomaly Detection Fundamentals
  • Pattern Recognition in Security Data
  • Behavioral Analysis with AI
  • Threat Detection Use Cases
  • Security Event Correlation
  • Fraud Detection with AI
  • Real-World Detection and Analysis Scenarios
  • Code Quality and Linting with AI
  • AI-Assisted Vulnerability Analysis
  • Automated Penetration Testing
  • AI-Assisted Threat Modeling
  • Exploit Identification Support
  • Security Testing Automation
  • Ethical Boundaries for Offensive AI Use
  • Real-World Security Testing Scenarios
  • Incident Management with AI
  • AI-Assisted Security Reporting
  • Translation for Security Operations
  • Summarization for Alerts and Reports
  • Knowledge Extraction from Security Data
  • Decision Support for Security Teams
  • Workflow Support Across Security Operations
  • Enterprise AI Use Case Selection
Open AI Risk & Security In Student Center
3.2
AI-Driven Attack Vectors And Threats33 Topics · Lessons, Flashcards & Practice

What You Will Learn

  • Distinguish convincing synthetic content from verified identity and authority.
  • Trace credential, recovery, retrieval, and tool-use attacks through their trust boundaries.
  • Interpret AI-assisted reconnaissance and social engineering without overstating the evidence.
  • Evaluate automated attack claims against observed behavior and operational impact.
  • Select layered defenses and verify containment through the systems that own the affected resources.

Inside The Lesson

  • Introduction to AI-Generated Content Threats
  • Deepfake Fundamentals
  • Impersonation Attacks
  • Misinformation Campaigns
  • Disinformation Strategies
  • Detecting AI-Generated Content
  • Security Implications of Synthetic Media
  • Mitigation Strategies for AI-Generated Content Threats
  • Scenario-Based Quiz
  • Trust Boundaries and Attack Preconditions
  • Credential and Session Abuse
  • Account Recovery and Enrollment Abuse
  • Delegated Access and Permission Abuse
  • Indirect Prompt Injection
  • Retrieval and Knowledge Contamination
  • Agent and Tool-Use Abuse
  • Investigation and Recovery for AI-Enabled Abuse
  • AI in Reconnaissance
  • Automated Data Gathering
  • Automated Data Correlation
  • Target Profiling
  • AI-Driven Phishing
  • Social Engineering Automation
  • Attack Personalization
  • Defensive Controls Against AI-Enhanced Social Engineering
  • Obfuscation Techniques
  • Automated Attack Vector Discovery
  • Automated Payload Generation
  • AI-Generated Malware
  • Honeypot Detection and Evasion
  • Distributed Denial-of-Service with AI
  • Attack Scaling Techniques
  • Mitigation Strategies for Automated AI Attacks
Open AI Risk & Security In Student Center
3.3
Automating Security Tasks with AI34 Topics · Lessons, Flashcards & Practice

What You Will Learn

  • Explain how AI automation supports analysis, enrichment, triage, reporting, orchestration, response, and operational consistency across operations.
  • Compare suitable automation tasks with activities requiring judgment, approval, or direct human control.
  • Apply validation, access restrictions, monitoring, approvals, fallback paths, and testing to realistic AI automation scenarios and operational workflows.
  • Identify automation failures that can directly amplify errors, unsafe actions, or unclear operational accountability.
  • Select defensible automation designs that align efficiency with documented evidence, control, ownership, and risk tolerance.

Inside The Lesson

  • Security Automation Fundamentals
  • AI-Assisted Scripting Tools
  • Low-Code Security Automation
  • No-Code Security Automation
  • Automation Workflow Design
  • Integration with Security Platforms
  • Efficiency Gains from AI Automation
  • Security Risks of Automation
  • Scenario-Based Quiz
  • Document Synthesis Fundamentals
  • Automated Summarization
  • Security Knowledge Base Creation
  • Report Generation
  • Data Extraction from Security Sources
  • Natural Language Interfaces for Security Teams
  • Accuracy Validation
  • Documentation Automation Best Practices
  • Scenario Quiz
  • Incident Response Ticket Management
  • AI-Assisted Ticket Generation
  • Alert Prioritization
  • Workflow Orchestration
  • AI-Assisted Change Approvals
  • Automated Deployment
  • Automated Rollback
  • Governance Controls for AI Automation
  • AI Agent Fundamentals
  • Autonomous Task Execution
  • Agent Scope and Permission Limits
  • Monitoring AI Agent Behavior
  • CI/CD Security Automation
  • Code Scanning and Software Composition Analysis
  • Unit Testing, Regression Testing, and Model Testing
  • Secure Automated Deployment and Rollback
Open AI Risk & Security In Student Center
04

AI Governance And Risk

3 Lessons
4.1
AI Governance Structures And Roles34 Topics · Lessons, Flashcards & Practice

What You Will Learn

  • Explain how governance structures establish authority, accountability, oversight, escalation, and consistent AI security decisions across organizations.
  • Compare executive, security, risk, data, development, legal, operational, and review responsibilities across organizations.
  • Apply decision rights, committees, policies, escalation paths, documentation, and ownership models to realistic AI governance scenarios and decisions.
  • Identify governance gaps that create conflicting authority, delayed action, or unowned AI security risks.
  • Select defensible structures that align oversight with policy, evidence, responsibility, clear accountability, and organizational priorities.

Inside The Lesson

  • Governance Fundamentals
  • AI Centers of Excellence
  • AI Policy Development
  • AI Procedure Implementation
  • Governance Operating Models
  • Oversight and Accountability
  • Integration with Cybersecurity and GRC
  • AI Governance Best Practices
  • Scenario-Based Quiz
  • Data Scientist Role
  • AI Architect Role
  • Machine Learning Engineer Role
  • Platform Engineer Role
  • MLOps Engineer Role
  • Data Engineer Role
  • Collaboration Across AI Engineering Teams
  • Secure AI Delivery Responsibilities
  • Scenario Quiz
  • AI Security Architect Role
  • AI Governance Engineer Role
  • AI Risk Analyst Role
  • AI Auditor Role
  • Role Separation and Accountability
  • Control Ownership
  • Evidence Collection Responsibilities
  • Governance Role Mapping Scenarios
  • Building an AI Governance Charter
  • AI Inventory and Use Case Registration
  • Policy Lifecycle Management
  • Procedure Enforcement
  • Risk Acceptance and Escalation
  • Governance Metrics and Reporting
  • Awareness Training Alignment
  • Continuous Governance Improvement
Open AI Risk & Security In Student Center
4.2
Risks And Responsible AI Principles33 Topics · Lessons, Flashcards & Practice

What You Will Learn

  • Explain how responsible AI principles consistently guide fairness, transparency, privacy, safety, reliability, accountability, and human oversight.
  • Compare responsible AI principles across technical, operational, ethical, legal, and business risk contexts.
  • Apply impact assessment, evidence review, controls, documentation, governance, and oversight to realistic responsible AI scenarios and operational decisions.
  • Identify tradeoffs that place responsible AI principles in tension with performance or business goals.
  • Select defensible actions that align AI risk decisions with documented evidence, policy, impact, and accountability.

Inside The Lesson

  • Fairness
  • Reliability and Safety
  • Transparency
  • Privacy and Security
  • Explainability
  • Inclusiveness
  • Accountability
  • Consistency and Awareness Training
  • Scenario-Based Quiz
  • Introduction of Bias
  • Accidental Data Leakage
  • Reputational Loss
  • Model Accuracy Risk
  • Model Performance Risk
  • Intellectual Property Risk
  • Autonomous System Risk
  • AI Risk Mitigation Strategies
  • Shadow IT vs Shadow AI
  • Unauthorized AI Usage
  • Unsanctioned AI Tools
  • Hidden Workflow and Model Risk
  • Data Exposure from Shadow AI
  • Detecting Shadow AI Activity
  • Policy Enforcement
  • Organizational Risk Reduction Controls
  • Responsible AI Risk Assessment
  • Risk Scoring and Prioritization
  • Control Selection for AI Risks
  • Human Oversight Requirements
  • Documentation Requirements
  • Training and Awareness Operations
  • Monitoring Responsible AI Risks
  • Continuous Responsible AI Review
Open AI Risk & Security In Student Center
4.3
AI Compliance And Regulatory Impact32 Topics · Lessons, Flashcards & Practice

What You Will Learn

  • Explain how laws, standards, contracts, sector rules, and policies shape accountable AI security operations and decisions.
  • Compare compliance obligations across data use, transparency, oversight, testing, records, and incident response.
  • Apply requirement mapping, evidence collection, documentation, control testing, and accountability to realistic AI compliance scenarios and operational workflows.
  • Identify regulatory gaps that directly expose organizations to legal, contractual, operational, or reputational consequences.
  • Select defensible compliance actions that align obligations with documented evidence, ownership, risk, and operational practice.

Inside The Lesson

  • EU AI Act Overview
  • OECD AI Standards and Principles
  • ISO AI Standards
  • NIST AI Risk Management Framework
  • Regulatory Trends in AI
  • Cross-Border Compliance Considerations
  • Business Impact of AI Compliance
  • AI Compliance Implementation Strategies
  • Scenario-Based Quiz
  • Corporate AI Policies
  • Sanctioned vs Unsanctioned AI
  • Private vs Public AI Models
  • Sensitive Data Governance
  • Policy Enforcement for AI Users
  • Policy Enforcement for AI Developers
  • Internal AI Compliance Audits
  • AI Policy Improvement Best Practices
  • Third-Party Compliance Evaluations
  • Vendor Due Diligence
  • Supplier and AI Service Provider Risk
  • External Audit Evidence
  • Contract and Assurance Requirements
  • Continuous Third-Party Validation
  • Data Sovereignty Fundamentals
  • Jurisdiction, Localization, and Cross-Border Data Risk
  • Jurisdictional Requirements for AI Data
  • Cross-Border Data Transfer Controls
  • Data Localization Requirements
  • Privacy and Sensitive Data Obligations
  • Legal Constraints on AI Development and Use
  • Compliance Evidence and Documentation
  • Compliance Enforcement and Legal Risk Management
Open AI Risk & Security In Student Center

Lesson, flashcard, and practice links open the corresponding Certify Academy resources. Sign in when prompted to continue your enrolled learning.

06 The Learning Environment

Keep The Tools Together.
Keep Your Next Step Clear.

The Student Center gives the course a home. Move from a topic into its lesson, review the matching flashcards, then open practice while keeping the selected course in view. The recording below shows Certify Academy’s own learning environment.

Certify Academy Student Center23 Seconds

Your overview keeps the next lesson, course progress, and study tools together.

Learning

Stay With The Explanation.

Use the lesson to understand the concept, the environment, and the reasons behind the response.

Review

Make Recall More Focused.

Match the review set to the topic so related ideas stay connected as you revisit them.

Practice

Check How You Apply It.

Use the question and explanation to decide what belongs in your next study session.

07 Put The Knowledge To Work

Bring The Lesson Into A Real Situation.

The value of AI security knowledge is how it helps you understand a situation and choose the next step. These examples connect the course topics with the work behind them.

Knowledge Becomes Useful In Context.
01

Understand AI Techniques In Security

Connect The Concept With The Work.

Compare AI techniques used in cybersecurity so you can understand how models support analysis and recognize the limits that require human oversight.

Included Lesson: Lesson 1.1: AI Types And Techniques In Cybersecurity. Start with the lesson, explain the idea in your own words, and use the matching review tools to check the distinctions that matter.

Explore The Related Course Area
02

Protect Data Across AI Systems

Connect The Concept With The Work.

Follow data through AI workflows so you can identify exposure risks, apply appropriate protections and account for sensitive information during model use.

Included Lesson: Lesson 1.2: Data Security In AI Systems. Start with the lesson, explain the idea in your own words, and use the matching review tools to check the distinctions that matter.

Explore The Related Course Area
Knowledge Becomes Useful In Context.
03

Secure The AI Lifecycle

Connect The Concept With The Work.

Examine security responsibilities throughout the AI lifecycle so you can connect development, deployment and monitoring decisions with the risks each stage introduces.

Included Lesson: Lesson 1.3: Security In The AI Lifecycle. Start with the lesson, explain the idea in your own words, and use the matching review tools to check the distinctions that matter.

Explore The Related Course Area
04

Who Is Responsible As It Changes?

Keep The Decision In Context.

Connect ownership, policy, risk assessment, lifecycle controls, and documentation to the system’s continuing use.

An AI output still needs context, verification, and accountable human decisions. Useful security begins with understanding where trust is being placed. Return to the relevant lesson when a practice explanation reveals a connection you want to understand more clearly.

Explore The Related Course Area

Understanding You Can Share

Learn To Explain
The Work Behind The Answer.

Knowledge becomes more useful when another person can follow your explanation. Connect the terminology of AI security, the situation, and the reason for the selected response.

Make The Reason Clear

Describe The Trust Boundary.

Explain what data enters the system, which integrations it can reach, and where an output becomes an action. This makes a broad AI concern concrete enough to assess.

Use the course to connect technical behavior with permissions, validation, and human oversight.

Keep The Discussion Useful

Make The Limits Visible.

A useful recommendation explains what a system can support, where uncertainty remains, and how the organization will check its behavior. Avoid treating a convincing response as evidence of correctness.

Bring risk and governance into the conversation before a pilot becomes a dependency.

08 Make Review More Useful

A Better Routine
Than Read And Repeat.

Working through the material is the first step. Coming back to it with a clear question is what makes the next pass more useful.

Ask yourself what you can explain, what still sounds similar, and what changes when the situation changes. For example, can you distinguish a useful generated response and a verified security finding? Use the lesson, flashcards, and practice explanations to make the distinction clear.

  1. 01

    Start With One Clear Topic.

    Read the explanation and name the main distinction you want to remember. Keep the context around the term, rather than collecting a definition alone.

  2. 02

    Explain It Without The Page.

    Try the flashcard first. Compare your explanation with the answer and note the part that was missing or unclear.

  3. 03

    Use It In A Situation.

    Choose a practice response and explain why it fits. Read the feedback even when your answer is correct.

  4. 04

    Give The Next Session A Purpose.

    Return to the lesson for the specific gap. A focused second look is easier to organize than an unfocused restart.

Build UnderstandingGive The Explanation A Second Look.
Apply The IdeaUse Practice To Find Your Next Focus.

09 Make Room In Your Week

A Study Plan That
Leaves Room For Life.

A workable pace includes time for new learning, review, and practice. Use the planner to see how the 15 lesson sequence could fit across your study window, then adjust it to your needs.

Keep The Session Manageable

One Topic.
A Clear Next Step.

Consistency grows from a routine you can return to, even when your week changes.

Give Each Week A Learning Rhythm.

Start a new lesson when you have time to follow the explanation. Use a shorter session to revisit flashcards, review your notes, or work through a focused practice set. Leave space to return to a topic that needs another pass.

A lesson count is a planning guide, rather than a promise about the time every topic will take. Your background, available time, and comfort with the material will shape your pace. Progress includes understanding a difficult concept more clearly, even when that means slowing down for a session.

Individual course access lasts 120 days. A study window longer than that may require an active All Access membership.

12 Weeks36 Weeks
1.0Average Lessons Per Week
1Lessons To Aim For Each Week

Use the suggested pace as a starting point. Keep review and practice in the plan as you work through the course.

10 Think Beyond The Exam

See Where The Knowledge Can Contribute.

AI Risk & Security connects CompTIA SecAI+ topics with the people responsible for protecting AI-enabled systems and assessing their risks. Explore established security, systems analysis and compliance occupations that can involve AI responsibilities without treating an emerging job title as a separate salary benchmark.

Related Work To Explore

Information Security Analysts

Investigate threats, strengthen protective controls and explain security findings so an organization can act before a small issue becomes a larger incident.

Role To Explore: AI Security Analyst

Related Work To Explore

Computer Systems Analysts

Connect business requirements with technical design by evaluating systems, improving workflows and helping teams make well-supported technology decisions.

Role To Explore: AI Governance Analyst

Related Work To Explore

Compliance Officers

Review policies, investigate concerns and organize evidence to help an organization meet its legal and regulatory responsibilities.

Role To Explore: AI Risk Analyst

Explore The Work Behind The Titles.

Use current job listings to compare responsibilities and employer requirements as you plan your development.

Search LinkedIn Jobs ↗Search Indeed Jobs ↗Search USAJOBS ↗

11 The Value Of A Complete Course

Enough Depth
To Keep Building.

The course combines a broad sequence with resources you can revisit. That matters when you want to review a familiar subject, connect a new idea, or give a difficult topic a more focused second look.

You can inspect the full outline before you start. As you learn, the same structure becomes a reference: a place to find the concept behind a practice question or the topic that needs more attention.

15Lessons With A Connected Sequence

Follow 4 course areas through a structured SecAI+ preparation path.

498Detailed Curriculum Topic Entries

Inspect the complete lesson outline, then return to the topic that needs a closer second look.

810Flashcards For Focused Recall

Check the definitions and distinctions that benefit from repeated review.

720Practice Questions And Activities

Work with scenarios and explanations as you connect ideas to a situation.

Preparation With The Work In View.

An AI output still needs context, verification, and accountable human decisions. Useful security begins with understanding where trust is being placed. The complete outline helps you find the course material behind a question, revisit a difficult area, and build a more connected understanding.

CompTIA SecAI+ certification logo
CompTIA SecAI+A Clear Preparation Goal.

Build the knowledge behind the credential, with the separate certification process in view.

Keep The Credential In Context

Prepare For The Exam.
Keep The Knowledge With You.

The 4 course areas give your SecAI+ review a broad structure. Work through unfamiliar subjects as well as the topics you enjoy, and use practice explanations to identify the distinctions that need a clearer second look.

AI adds new dependencies to familiar security responsibilities. Understanding the data, the system, the output, and the people using it helps you ask more useful questions about risk. Bringing those connections into your study routine makes the material more useful than a list of terms.

CompTIA sets the separate exam, eligibility, and credential requirements. Course completion provides preparation materials; it does not award certification or guarantee an exam result.

12 Your Access Options

Choose The Scope
That Fits Your Learning.

Focus on AI Risk & Security, or choose All Access if you want room to explore other professional subjects. The learning goal comes first; the access option should support it.

Individual Course

AI Risk & Security

$179One Payment
120 Days Of Access

A focused SecAI+ preparation path with the complete course and connected study tools.

  • 15 complete lessons
  • 810 flashcards
  • 720 practice questions and activities
  • Connected Student Center access
View Course Access At Checkout

Review the final offer and access terms at checkout. Certification exam fees are separate.

All Access Membership

Room To Explore
Your Next Subject.

$349Per Year

Bring your SecAI+ learning into a wider professional catalog, with lessons, review, practice, and your Student Center in one membership.

IT CoreNetwork CoreCybersecurity FoundationsSecurity Operations AnalystAI Risk & SecurityRisk Management Professional · Confirm AvailabilityCybersecurity LeadershipProject Management FoundationsProject Management CoreHuman Resources Professional
Choose Annual All Access

Billed annually. Membership renews until canceled.

A Way To Explore The Learning Environment

30 Days Of All Access To Find Your Fit.

$0 today, then $49 per month unless canceled. Payment method required.

Start The 30 Day Trial

Certification exams, vouchers, optional labs, and third party credential requirements are separate unless a specific checkout offer states otherwise.

13 Clear Answers Before You Begin

Know What
You Are Choosing.

Review the course, access options, and study format. If your question involves a team or a particular learning goal, contact the academy for guidance.

Talk With Certify Academy
What Does AI Risk & Security Cover?

The 15 lessons cover AI Concepts, Data And Lifecycle, Protecting AI Systems, AI In Security Operations, AI Governance And Risk. Open the full curriculum above to inspect every lesson’s learning goals and detailed topics.

What Is Included With Individual Course Access?

Individual AI Risk & Security access is $179 for 120 days, including lessons, flashcards, practice, and the connected Student Center. Review the final offer and access terms at checkout.

How Does The 30 Day Trial Work?

The trial opens All Access for 30 days. It is $0 today and continues at $49 per month unless canceled before the trial ends. A payment method is required. The separate annual membership option is $349 per year.

Can I Study On My Own Schedule?

Yes. Use the online lessons, flashcards, and practice around the time you have. The planner divides the 15 lesson sequence across your selected window. Leave time for review and check that the chosen access period covers your plan.

How Should I Use The Three Learning Tools?

Start with the lesson for context. Try flashcards to check what you can recall and explain. Use practice to apply the topic, then read the explanation and return to the part of the lesson that needs a clearer second look.

How Does The Course Connect To SecAI+?

The course organizes preparation around 4 connected areas and a complete lesson sequence. CompTIA maintains the credential requirements separately. Review the full curriculum to understand the course scope and use the preview to explore its learning tools.

Does Course Completion Award SecAI+ Certification?

The course provides preparation materials. CompTIA awards the credential through its separate certification process. Exams, exam vouchers, eligibility requirements, and any required experience are separate unless the specific offer explicitly states otherwise.

Can A Team Use The Course?

Contact Certify Academy to discuss your learners, training goals, and enrollment needs. The public curriculum gives your organization a detailed starting point for that conversation.

14 A Considered Next Step

Start With A Topic.
See Where It Takes You.

Explore the curriculum, try the learning preview, or look at the Student Center. Give yourself a clear picture of the course before choosing the access option that fits your plans.

AI Risk & Security brings a broad subject into an organized path, with enough detail to return to the topics that matter to you.

Return To The Learning PreviewReview Access Options
Make Space For
Your Next Step.