SecAIPlus.com @SecAIPlus
CompTIA SecurityAI+ certified. AI security, threat modeling, and cert prep. Free study resources below. 📺 https://t.co/2lj0H4sMg9 secaiplus.com Joined March 2026-
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Three ISO numbers, free exam points: 27001 — information security management 27701 — privacy management 42001 — AI management SecAI+ will happily offer all three as answer choices on one question. Don't donate the points.
Evasion ≠ poisoning. Poisoning: corrupt the model before deployment (training time). Evasion: fool the model after deployment (inference time). One breaks the tool. The other sneaks past it. Different timeline, different controls.
You wouldn't run an unsigned binary from a forum. A pretrained model off a public hub is an unsigned binary — with weights nobody can eyeball. Provenance, hashes, scanning before load. Same hygiene, new file type.
"Human in the loop" is doing a lot of work in AI governance docs. A human who reviews 400 AI decisions an hour isn't in the loop. They're in the blast radius. If the review is a formality, the control doesn't exist.
An analyst who trusts every AI verdict is a rubber stamp. An analyst who trusts none of them is doing two jobs. Domain 3 of SecAI+ is about the space between — when to let automation act, and when a human stays in the loop.
Sitting SecAI+ this fall? 20% off the full prep stack — study guide, 3 timed practice exams, 150-card Anki deck — through Labor Day. Code LABORDAY20 → secaiplus.gumroad.com/l/everything-b…
Your SOC lives in MITRE ATT&CK. ATLAS is its sibling for AI systems — same structure, different attack surface: poisoning, extraction, evasion, prompt injection. If you can read ATT&CK, you can read ATLAS. Most teams just haven't opened it.
Shadow AI is just shadow IT with better marketing. Proprietary code into a public chatbot. Customer data through an unapproved model. You don't firewall it away — you give people a sanctioned tool that's actually good.
CompTIA SecAI+ is now ANAB-accredited. Translation: "is this new cert even legit?" just got an official answer. I passed it in the February cohort. What it actually tests: youtu.be/tKiFVGtEZIk
Data poisoning ≠ model poisoning. Poison the data: corrupt the training set. Poison the model: tamper with the weights directly. Same goal, different entry point, different controls. Know it cold.
Healthcare diagnostic AI under the EU AI Act is High risk — not Unacceptable. High risk = heavily regulated. Unacceptable = banned (social scoring, mass biometric surveillance). Picking "unacceptable" for healthcare is the classic trap answer.
Stop losing these points: NIST CSF → Identify, Protect, Detect, Respond, Recover NIST AI RMF → Map, Measure, Manage, Govern The exam swaps them on purpose.
I passed the CompTIA SecurityAI+ in February — one of the first cohorts. Everyone asks the same two things: what's on it, and how to prep. Just answered both in my first video. YouTube: @SecAIPlus
Your RAG knowledge base is attack surface. Poison one retrieved document and the model follows the instructions hidden inside it — without the attacker ever touching your prompt. That's indirect prompt injection. Validate inputs. Filter retrieved content.
Prompt injection ≠ jailbreaking. Injection: crafted input overrides the system prompt (SQL injection, but for language). Jailbreak: you talk the model out of its own safety rules. Different attack. Different fix. SecAI+ tests whether you know which is which.
AI security isn't becoming a specialty. It's becoming part of every security job. The people preparing now will look like they saw it coming.
The people asking me about AI security certs aren't beginners. They're security pros who can see AI landing in their lap whether they're ready or not.
Who owns AI risk in your org? If the honest answer is three teams pointing at each other, that's the vulnerability. Not the model. The org chart.
Output validation is the control layer most teams skip. The model generates, the app ships it, nobody checked in between. That gap is the new perimeter.
Your IR plan assumes you can find what changed and when. A model poisoned during training, six months before it misbehaves, breaks that assumption completely.
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