GMLE
GIAC Machine Learning Engineer
GIAC Machine Learning Engineer
› Quality score
Four-axis SecProve rubric, each 0–10. SecProve editorial assessment — each axis carries a written justification so you can push back on any single call without dismissing the whole score.
› Exam format
75 questions + CyberLive, 3 hours, open-book, proctored via Pearson VUE. Passing score: 73%.
30-day wait between attempts. SANS course bundles typically include 2 attempts.
› Recertification
Valid for 4 years. Renewal via 36 CPE credits or renewal exam (479 USD). Each GIAC cert separate.
› NICE Framework work roles
The NIST NICE work-role IDs this cert maps to. NICCS lookup.
› Core domains covered
The 3 domains this cert is centrally about. Passing the exam demonstrates working knowledge of each.
Evasion attacks, poisoning attacks, model extraction, membership inference, model inversion, gradient-based attacks.
AI system threat modeling, red teaming methodology for LLMs (OWASP Top 10 for LLMs), automated red teaming tools, evaluation frameworks.
ML-based anomaly detection, UEBA, network traffic analysis, deep learning for malware.
› Prerequisites
No formal prerequisites. Associated SANS course strongly recommended.
› Study materials
Curated starting points. Not exhaustive — vet each against your learning style and the current exam version.
- SANS SEC595 Course Materials — SANS
- GIAC Practice Tests (2 included with exam)
› Version & lifecycle
› Salary signal
ML engineer in security, US, 4-6 years. Newer role category.
Robert Half Salary Guide extrapolation · 2024 · US base only · p25–p75 range
› How it compares
GMLE is the data-science engineering cert; GASAE is AI-security automation.
↔ Compare side-by-side› Careers that commonly pursue this cert
Secure AI/ML systems from adversarial attacks, data poisoning, and model compromise. The fastest-growing specialization in cybersecurity.
Secures the platform that trains, stores, and serves ML models — multi-tenant GPU isolation, pipeline integrity, feature-store hygiene, secrets management in ML workflows.
› Common exam traps to study
Cybersecurity cert exams reuse the same 25 distractor patterns over and over — category confusion, RTO vs RPO, IDS vs IPS, MD5 vs SHA-256, and more. Once you can name the trap, you stop falling for it. Each archetype page covers what it is, the specific pairs candidates confuse, and how to avoid it.
See this cert’s domains highlighted on the interactive map, or compare it against the rest of the catalog.