CompTIA SecAI+ study guide
CompTIA SecAI+ · Work through your exam, one topic at a time.
CompTIA SecAI+ (also written SecurityAI+) is a vendor-neutral certification aimed at security professionals who must secure artificial intelligence systems and, increasingly, use AI to defend everything else. It suits security analysts, engineers, architects, GRC staff and AI/ML practitioners whose work touches model deployment, data protection or AI risk. The CompTIA SecAI+ study guide has 5 sections and about 1,478 words.
01AI Governance, Risk, and Compliance
AI governance is the set of policies, roles, and controls that keep an organization's AI use lawful, ethical, and aligned with business risk appetite. Candidates should be fluent in the distinction between governance (decision rights, accountability, policy) and compliance (evidence that external or internal obligations are met).
301 words, about 2 min02AI Threat Landscape and Attack Surface
AI systems expand the attack surface in two directions: attacks on AI (integrity, confidentiality, availability of models and data) and attacks using AI (automation, scale, and social engineering). Candidates must be able to match a described attack to its category.
306 words, about 2 min03Securing AI Systems and Data
This domain covers the technical controls that protect models, training data, prompts, and inference infrastructure. Expect questions on data protection across the lifecycle : classification, minimization, de-identification, encryption in transit and at rest, and preventing sensitive data from entering prompts or training corpora.
284 words, about 1 min04AI-Assisted Security Operations and Defense
This domain is about using AI inside the SOC and defending AI-enabled operations. Candidates should understand where AI genuinely helps: alert triage and prioritization , anomaly and behavioral detection , log correlation , malware and phishing classification , vulnerability prioritization , natural-language investigation assistance , and automated response playbooks .
304 words, about 2 min05Secure AI Development and Deployment Lifecycle
Security must be woven through the whole AI lifecycle: problem definition, data acquisition, training/fine-tuning, evaluation, deployment, monitoring, and retirement. Expect questions on secure SDLC adapted to ML : threat modeling before build, version control for code and data and model artifacts, reproducible pipelines, and segregation of duties between training and deployment.
283 words, about 1 min