CompTIA SecAI+ CY0-001 (V1) Exam Objectives
This page translates the official CompTIA SecAI+ CY0-001 (V1) exam objectives into clear, practical language. You’ll see what each domain covers, realistic task themes, and a focused study plan that turns objectives into repeatable labs, PBQs, and job-ready security skills around AI systems.
The exam uses multiple-choice and performance-based items, and CompTIA recommends 3–4 years of IT experience and ~2 years of hands-on cybersecurity experience. Always verify details with the current blueprint before you schedule your exam.
Last updated: November 12, 2025. Always verify against the official CompTIA blueprint before your exam.
TL;DR: SecAI+ validates the ability to understand core AI concepts, secure AI systems, leverage AI to enhance security operations, and apply AI-specific governance, risk, and compliance (GRC). Domain weights: 1) Basic AI Concepts Related to Cybersecurity (17%), 2) Securing AI Systems (40%), 3) AI-assisted Security (24%), 4) AI Governance, Risk, and Compliance (19%).
Table of Contents
- What Is Covered on CompTIA SecAI+ CY0-001 (V1)?
- Objective-By-Objective Breakdown (with examples)
- Why SecAI+ for Modern Security Teams?
- Study Plan: How to Master the SecAI+ Objectives
- Free Checklist & Next Steps
- Frequently Asked Questions
What Is Covered on CompTIA SecAI+ CY0-001 (V1)?
SecAI+ certifies that you can apply AI literacy to security work, harden AI systems and data, use AI tools responsibly to automate security tasks, and navigate AI-specific governance and compliance. Expect scenario-driven items and PBQs across architecture, controls, monitoring, evidence analysis, and policy.
Objective-By-Objective Breakdown (with examples)
1) Basic AI Concepts Related to Cybersecurity — 17%
Know the AI landscape and where security fits.
- Types & techniques: generative AI, ML/statistical learning, transformers, deep learning, NLP with LLMs/SLMs, and GANs; supervised/unsupervised/reinforcement learning; validation, fine-tuning (epoch, pruning, quantization).
- Prompt engineering: roles, system/user prompts, zero/one/multi-shot, and prompt templates.
- Data security for AI: cleansing, verification, lineage, integrity, provenance, augmentation/balancing; structured/semi/unstructured data; watermarking; RAG with embeddings and vector storage.
- Secure AI life cycle: align to use case and corporate objectives; secure collection/prep, model selection/evaluation, deployment/validation, monitoring/maintenance, feedback/iteration; human-in-the-loop, oversight, and validation.
2) Securing AI Systems — 40%
Design and enforce controls that protect models, data, agents, and integrations.
- Threat-modeling resources: OWASP LLM Top 10 and ML Security Top 10, MIT AI Risk Repository, MITRE ATLAS, CVE AI Working Group, and general threat-modeling frameworks.
- Security controls: model controls (evaluation, guardrails, prompt templates); gateway controls (prompt firewalls, rate/token limits, input quotas by size/quantity, modality limits, endpoint access); guardrail testing/validation.
- Access controls: for models, data, agents, and APIs/networks; enforce least privilege.
- Data security: encryption in transit/at rest/in use; anonymization; labels/classification; redaction/masking; minimization.
- Monitoring & auditing: prompt/query/response monitoring, log monitoring/sanitization/protection, response confidence, rate and cost monitoring (prompts, storage, response, processing), and auditing for hallucinations, accuracy, bias/fairness, access.
- Attacks & compensating controls: prompt injection, model/data poisoning, jailbreaking, input manipulation, bias introduction, guardrail circumvention, integration abuse, model inversion/theft, supply chain/transfer learning attacks, model skewing, output integrity attacks, membership inference, insecure output handling, model DoS, sensitive data disclosure, insecure plug-ins, excessive agency/overreliance. Counter with prompt firewalls, guardrails, access controls, data integrity and encryption, templates, rate limiting, and least privilege.
3) AI-assisted Security — 24%
Use AI to responsibly accelerate security work.
- Tools & use cases: IDE/browser/CLI plug-ins, chatbots, personal assistants, MCP servers; signature matching, code quality/linting, vuln analysis, automated pentesting, anomaly detection, pattern recognition, incident management, threat modeling, fraud detection, translation, summarization.
- AI-enabled attacks: deepfakes (impersonation, mis/disinformation), adversarial networks, reconnaissance, social engineering, obfuscation, automated data correlation, automated attack generation (discovery, payloads, malware, honeypots, DDoS).
- Automation scenarios: low-/no-code scripting, document synthesis, IR ticket management, change-management approvals and automated deployment/rollback, AI agents, and CI/CD hooks (code scanning, SCA, unit/regression/model testing, automated deploy/rollback).
4) AI Governance, Risk, and Compliance — 19%
Build structures and policies that enable safe AI adoption.
- Governance structures & roles: AI CoE, policies/procedures; roles like data scientist, AI/ML/MLOps/platform/security architects & engineers, AI governance/risk/audit, and data engineer.
- Risks & responsible AI: fairness, reliability/safety, transparency, privacy/security, explainability, inclusiveness, accountability, consistency, awareness; bias introduction, accidental leakage, reputational loss, performance/IP risks, autonomous systems; shadow IT and “shadow AI.”
- Compliance impacts: EU AI Act, OECD/ISO standards, NIST AI RMF, corporate policies (sanctioned vs unsanctioned, private vs public models, sensitive-data governance), third-party compliance evaluations, data sovereignty.
Why SecAI+ for Modern Security Teams?
SecAI+ proves you can bridge AI and cybersecurity: protect AI pipelines, apply data controls, operationalize monitoring/auditing and cost/rate governance, and use AI to responsibly automate security tasks while meeting regulatory expectations. That maps directly to security engineer, blue-team, AppSec, SecOps, and governance roles.
Study Plan: How to Master the SecAI+ Objectives
- Build a secured RAG mini-stack Stand up a small RAG app with embeddings + vector DB. Add prompt templates, a prompt firewall, token/rate limits, and endpoint ACLs. Validate with guardrail tests and log sanitization/protection.
- Instrument monitoring & auditing
Log prompts/responses with confidence scores; track cost (prompt/storage/response/processing). Add alerts for spikes, hallucination/bias audits, and access anomalies. - Run an attack-and-defend lab
Simulate prompt injection, jailbreaks, and basic poisoning. Capture evidence and implement compensating controls: stricter templates, rate limits, least privilege, data integrity checks, and model guardrails. - Automate useful tasks
Use an MCP-backed assistant or IDE/CLI plug-in for secure code reviews, SCA, and IR ticket summarization. Wire CI/CD checks (code scan, SCA, unit/regression/model tests) with auto-rollback. - Govern continuously
Draft a lightweight AI policy (sanctioned vs unsanctioned use, private vs public models, sensitive-data handling), map to NIST AI RMF and EU AI Act considerations, and document roles and approval paths.
