Here's your study companion for the AAISM™ exam.
300 quiz questions, a full study guide, glossary, and a 90-question simulated exam — built for studying on your phone or tablet, and works offline after your first visit.
The AAISM™ exam tests governance, risk, and technical controls. Every question in the app maps to a specific domain — so you know where you stand.
Want the full breakdown? Read the AAISM Certification Study Guide.
Everything you need to prepare — no subscriptions, no accounts, no waiting for content to load.
Comprehensive domain-by-domain notes covering every testable concept. Search and jump to any topic instantly.
Searchable reference for key AI security terms. From adversarial ML to zero trust — every term you'll see on exam day.
Filter by domain and difficulty across 300 questions. Instant feedback with full explanations after every answer. Study the gaps, not the whole.
Full 90-question simulated exam, distributed across the three major domain areas. See your score and review every missed question.
Real questions from the app. Click Quiz Mode, Study Guide, or Glossary to explore — select an answer to see instant feedback and full explanations.
Explanation
300 questions across all 3 domains. Filter by difficulty. Get full explanations every time.
Get Full Access — $9.99The AAISM™ exam tests your understanding of fundamental AI and machine learning concepts. Master the terminology in this section — these definitions appear throughout all three domains.
Overfitting vs. Underfitting — Overfitting: model memorizes training data, fails on new data. Underfitting: model too simple, poor on both. Know these cold for the exam.
Covers stakeholder considerations, AI strategies, policies, asset/data lifecycle, security program development, and business continuity.
Covers AI risk frameworks, threat modeling, OWASP LLM Top 10, and vendor/supply chain management.
Carefully crafted inputs designed to cause a model to make a mistake. Subtle modifications to data (e.g., images, text) that are imperceptible to humans but cause the AI to misclassify or produce incorrect outputs.
A formal document defining the scope, objectives, stakeholders, governance structure, risk management plan, success metrics, and approval signatures for an AI program.
The senior oversight, processes, standards, and safeguards that ensure safe and ethical use of AI — addressing inherent flaws from human-created AI, including bias and discrimination.
An attack where malicious data is injected into training datasets to introduce backdoors, manipulate model behavior, or sabotage model performance. A primary supply chain risk for AI systems.
A safety control requiring human oversight and final decision authority over AI outputs. Essential for high-stakes decisions in healthcare, law enforcement, and financial lending.
The degradation of an ML model's performance over time as real-world data distributions change or the relationship between inputs and outputs shifts. Requires continuous monitoring and periodic retraining.
An attack that overrides or manipulates an LLM's system instructions by embedding malicious content in user inputs. Listed as the #1 risk in the OWASP Top 10 for LLMs.
A principle that AI decisions are NOT automatically assumed to be correct. Three areas: Ability (can the AI perform its task?), Integrity (will data be processed correctly?), Benevolence (does it adhere to do-no-harm principles?).
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