CompTIA SecAI+ (CY0-001) Course + EBook + Exams (ALL IN ONE)

Training, validation, fine-tuning, epochs, pruning, quantization, transfer learning, model evaluation metrics.

This section is the most exam-dense material in the entire certification. It covers security controls for AI systems under Domain 2, Objective 2.2, which carries 40 percent of the total exam weight. Everything we have learned so far, the threat landscape from Section 3 and the attack techniques from Section 4, all leads to this question: what controls do we put in place to defend AI systems? In this section, we will answer that question comprehensively across six lectures. We start with model-level evaluation and guardrails, the layered defense architecture that wraps around every AI system. Then we move to prompt templates and prompt firewalls, the instruction-layer controls that shape how models receive and process requests. After that, we cover gateway controls including rate limiting and token management, which regulate how clients interact with AI APIs. We then learn how to test and validate those guardrails through AI red-teaming and automated testing frameworks.. And finally, a practice scenario session puts it all together with exam-style questions that test your ability to match the right control to the right attack. By the end of this section, you will have a complete defensive toolkit and the ability to apply it in any scenario the exam throws at you. Let us get started.

Welcome to Section 6 of the CompTIA SecAI Plus course. This is one of the most important sections in the entire certification because it covers two critical exam objectives from Domain 2, which carries a massive 40 percent weight on the CY0-001 exam. In this section, we are going to cover six interconnected topics. We will start with access control models, specifically RBAC and ABAC, and how they apply to AI-specific principals like agents and pipelines. Then we will move to securing AI agents and API networks, which is increasingly relevant as agentic AI becomes mainstream. After that, we will dive deep into data encryption across all three states: at rest, in transit, and in use. We will then cover data anonymization, masking, and minimization, which bridges both security and compliance. Next, we will explore how to secure AI deployment environments, from containers to model registries. And finally, we will bring it all together in a hands-on lab where you will implement RBAC for an AI platform. Each lecture builds on the previous one, so by the end of this section, you will have a complete picture of how access controls and data security work together to protect AI systems. Let us get started.

Real-time monitoring strategies, logging frameworks, prompt/response monitoring, anomaly detection in AI behavior.

Securing AI logs, preventing sensitive data leakage in logs, log integrity, retention policies

Tracking prompt costs, storage costs, response/processing costs, rate monitoring, budget alerts, capacity planning.

Detecting hallucinations, measuring response confidence, fairness audits, bias detection frameworks, accuracy benchmarking.

Prompt injection (direct/indirect), model poisoning, data poisoning, training data manipulation, backdoor attacks.

Jailbreaking, model inversion, model theft, membership inference, supply chain attacks, transfer learning attacks, model DoS, excessive agency.

Implementing layered defenses: prompt firewalls, guardrails, templates, access controls, data integrity, encryption, rate limiting, least privilege.

Simulate prompt injection, jailbreak attempts, and implement compensating controls. Analyze logs for indicators of attack.

Comprehensive practice quiz covering all Domain 2 objectives with scenario-based questions.

Welcome to Section 7, the final and largest section of Domain 2. This section brings together everything from Sections 5 and 6 into a complete operational security picture. We have nine lectures covering three major themes. First, monitoring and observability: how to watch your AI systems in real time, manage logs securely, track costs as a security metric, and audit for hallucinations and bias. Second, the attack deep dive: a comprehensive two-part walkthrough of every major attack category, from prompt injection and data poisoning to jailbreaking, model theft, and supply chain attacks. And third, defense and response: how to build a layered defense stack with compensating controls, a hands-on lab where you play attacker and defender, and a full Domain 2 practice quiz with 50 questions. Section 7 carries enormous exam weight because Objectives 2.5 and 2.6, monitoring AI systems and implementing compensating controls, are scenario-heavy on the CY0-001 exam. By the end of this section, you will have the complete Domain 2 toolkit: you can identify attacks, select the right controls, implement monitoring, and respond to incidents. Let us begin with AI monitoring.

IDE plug-ins, browser extensions, CLI tools, chatbots, personal assistants, MCP servers for security operations.

Signature matching, pattern recognition, behavioral analytics, UEBA, anomaly detection with ML models.

Automated vulnerability scanning, AI-assisted code review, code quality/linting, automated penetration testing tools.

AI-powered incident triage, alert correlation, automated incident response playbooks, threat intelligence enrichment.

Deepfakes (impersonation, misinformation), adversarial networks, AI-powered reconnaissance, social engineering, automated attack generation.

Detecting deepfakes, countering automated attacks, defending against AI-powered social engineering, DDoS mitigation.

Use an LLM to analyze security logs, detect anomalies, and generate incident summaries from raw log data.

Using AI to generate scripts, document synthesis, automating repetitive security tasks without deep coding.

Designing AI agents, agent orchestration, autonomous vs semi-autonomous agents, safety guardrails for agents.

Code scanning, software composition analysis (SCA), unit/regression/model testing, automated deployment and rollback with AI.

AI-assisted ticket triage, change-management approvals, automated deployment/rollback procedures.

Integrate AI-powered code scanning and automated testing into a CI/CD pipeline using GitHub Actions.

Practice questions covering AI tools, attack vectors, and automation scenarios.

AI Center of Excellence (CoE), AI governance boards, policies and procedures, organizational alignment.

Data scientist, AI/ML engineer, MLOps engineer, platform engineer, security architect, AI governance officer, AI risk manager, AI auditor, data engineer.

Fairness, reliability/safety, transparency, privacy/security, explainability, inclusiveness, accountability, consistency, awareness.

Bias introduction, accidental data leakage, reputational damage, performance/IP risks, autonomous system risks, shadow AI detection and management.

What is shadow AI, risks of unsanctioned AI use, detection methods, governance policies, sanctioned vs unsanctioned tools.

Step-by-step walkthrough of establishing an AI governance program in a mid-size enterprise.