AI Security Awareness Training

Build everyday AI security judgment, where candidates practice recognizing risky AI situations, protecting sensitive information, limiting AI authority, verifying outputs and identities, and reporting suspected incidents early.

Artificial intelligence has moved into daily work faster than many organizations have built the judgment to use it safely. This course develops that judgment through brief scenarios, practical decision rules, and repeated knowledge checks. The goal is to instinct, understand and develop a security awareness culture when it comes to AI use.

This is an awareness course. It teaches risk recognition, safe behavior, and responsible AI use. Detailed secure engineering, red teaming, penetration testing, exploit development, model evaluation and legal interpretation require role-based specialist training.

The course is organized into nine modules, opened by a course introduction and closed by a wrap-up on the frameworks behind the practice. Each module ends with a Check on Learning that must be answered correctly before the next module unlocks.

• Module 1: AI Security Culture and Situational Awareness
• Module 2: AI Systems, Taxonomy and Lifecycle
• Module 3: Approved AI, Shadow AI, and Data Protection
• Module 4: Privacy, Legal Duties, Identity and Access
• Module 5: Prompt Injection, RAG, Memory, and Poisoned Context
• Module 6: Agentic AI, Automation and Autonomous Actions
• Module 7: Deepfakes, Social Engineering, Output Integrity, and Human Review
• Module 8: AI-Generated Work, Supply Chain, Vendors and Adversarial ML
• Module 9: Remote Use, Insider Risk, Incident Response and Security Hygiene

After completing every module, candidates sit the certification examination: 60 scenario-based multiple-choice questions, each with five options (A through E).

• Select the single best answer for each of the 60 questions. Every question provides five plausible options, A through E.
• Apply the training principles to the facts presented. Several questions combine more than one AI risk domain, so the safest or most defensible response may depend on data sensitivity, permissions, provenance, verification, and consequences together.
• Do not assume that an approved product, familiar identity, confident AI output, internal source, or existing software approval is sufficient by itself.