CompTIA SecAI+ CY0-001 | Practice Exams 2026 | 900 Questions

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Cybersecurity analysts and engineers who need to understand AI-specific threats and controls

Security architects designing or evaluating AI deployments

GRC professionals responsible for AI governance, risk assessment, and compliance

Career changers entering cybersecurity through the AI security pathway

Certification candidates who have completed their study material and want rigorous exam-level practice before test day

SOC analysts, learn recon for bug bounty penetration testers ethical hackers, and incident responders who encounter AI tools and AI-generated threats in their daily work

Applying prompt engineering principles and recognising the security implications of system prompts, user prompts, and prompt templates

Protecting training data through proper data lineage, provenance, cleansing, verification, augmentation, and balancing

Implementing retrieval-augmented generation (RAG) securely, including vector storage and embedding protection

Securing every phase of the AI lifecycle — from business use case alignment through deployment, monitoring, and iteration

Using AI threat-modelling resources including the OWASP LLM Top 10, OWASP ML Security Top 10, MITRE ATLAS, MIT AI Risk Repository, and CVE AI Working Group

Implementing security controls such as model guardrails, prompt firewalls, rate limits, token limits, input quotas, modality limits, and endpoint access controls

Enforcing access controls across model, data, agent, and network/API layers

Applying data security controls — encryption in transit, at rest, and in use; data anonymisation; classification labels; redaction; masking; and minimisation

Configuring monitoring and auditing for AI systems — prompt monitoring, log sanitisation, log protection, confidence scoring, bias auditing, hallucination detection, and AI cost monitoring

Analysing evidence of AI-specific attacks — prompt injection, model poisoning, data poisoning, jailbreaking, input manipulation, model inversion, model theft, membership inference, AI supply chain attacks, transfer learning attacks, model skewing, output integrity attacks, backdoor attacks, Trojan attacks, insecure output handling, model denial of service, excessive agency, and overreliance

Recommending compensating controls appropriate to each attack type

Using AI-enabled security tools — IDE plug-ins, browser plug-ins, CLI plug-ins, chatbots, personal assistants, and Model Context Protocol (MCP) servers — for tasks including vulnerability analysis, anomaly detection, automated penetration testing, free iso iec 27035 security incident management for beginners course, and threat modelling

Understanding how AI enables and enhances attack vectors — deepfakes, impersonation, social engineering, reconnaissance, obfuscation, automated malware generation, and DDoS

Automating security tasks with AI agents, scripting tools (low-code/no-code), CI/CD integration, software composition analysis, and automated deployment/rollback

Explaining organisational governance structures for AI — AI Centre of Excellence models, AI-related roles (data scientist, AI architect, ML engineer, AI security architect, AI governance engineer, AI auditor, and others), and AI policies and procedures

Evaluating risks associated with AI — fairness, reliability, transparency, differential privacy, explainability, inclusiveness, accountability, intellectual property risks, autonomous system risks, and shadow AI

Navigating compliance frameworks — EU AI Act, OECD standards, ISO AI standards, NIST AI Risk Management Framework (AIRMF), corporate policies (sanctioned versus unsanctioned AI, private versus public models, sensitive data governance), third-party compliance evaluations, and data sovereignty

2.0 Securing AI Systems — 40%

3.0 AI-assisted Security — 24%

4.0 AI Governance, Risk, and Compliance — 19%

Model training techniques — supervised learning, unsupervised learning, reinforcement learning, federated learning, fine-tuning, epochs, pruning, and quantisation

Prompt engineering — system prompts, user prompts, zero-shot, one-shot, multi-shot prompting, system roles, and templates

Data security for AI — data cleansing, verification, lineage, integrity, provenance, augmentation, balancing, watermarking, RAG, vector storage, and embeddings

AI lifecycle security — business use case alignment, data collection, preparation, model development, evaluation, deployment, validation, monitoring, feedback, and human-centric design principles

AI threat modelling — OWASP LLM Top 10, OWASP ML Security Top 10, MITRE ATLAS, MIT AI Risk Repository, CVE AI Working Group, and threat-modelling frameworks

Security controls for AI — model evaluation, model guardrails, prompt templates, prompt firewalls, rate limits, token limits, input quotas, modality limits, endpoint access controls, and guardrail testing

Access controls — model access, data access, agent access, and API access

Data security controls — encryption (in transit, at rest, in use), anonymisation, classification labels, redaction, masking, and minimisation

AI monitoring and auditing — prompt monitoring, log monitoring, log sanitisation, log protection, response confidence levels, rate monitoring, AI cost monitoring, hallucination detection, accuracy auditing, bias and fairness assessment, and access auditing

AI attack analysis — prompt injection, model poisoning, data poisoning, jailbreaking, input manipulation, backdoor attacks, Trojan attacks, model inversion, model theft, membership inference, AI supply chain attacks, transfer learning attacks, model skewing, output integrity attacks, insecure output handling, model DoS, sensitive information disclosure, insecure plug-in design, excessive agency, overreliance, and circumventing AI guardrails

Compensating controls — prompt firewalls, model guardrails, access controls, data integrity controls, encryption, prompt templates, rate limiting, and least privilege

AI-enabled security tools — IDE plug-ins, browser plug-ins, CLI plug-ins, chatbots, personal assistants, and MCP servers for signature matching, code quality, vulnerability analysis, automated pen testing, anomaly detection, pattern recognition, incident management, threat modelling, fraud detection, translation, and summarisation

AI-enhanced attack vectors — deepfakes, impersonation, misinformation, disinformation, adversarial networks, reconnaissance, social engineering, obfuscation, automated data correlation, and automated attack generation

Security automation with AI — scripting tools (low-code/no-code), document synthesis, incident response ticket management, change management, AI agents, and CI/CD integration (code scanning, SCA, unit testing, regression testing, model testing, automated deployment/rollback)

AI governance — AI Centre of Excellence, AI policies and procedures, and AI-related roles

AI risk — responsible AI principles (fairness, reliability, safety, transparency, privacy, security, differential privacy, explainability, inclusiveness, accountability, consistency, awareness training), bias, data leakage, reputational loss, model performance risks, IP risks, autonomous system risks, and shadow AI

AI compliance — EU AI Act, OECD standards, ISO AI standards, NIST AIRMF, corporate policies, third-party compliance evaluations, and data sovereignty