AI security fundamentals
Security and Resilience
What is AI security fundamentals?
Understands prompt injection, data poisoning, model theft, insecure integrations and excessive agent privileges, and the controls that reduce each.
Where the frameworks place it: OWASP Top 10 for LLM Applications; Google SAIF; NIST AI RMF MEASURE 2.
The interview question it draws
What are the security failure modes specific to AI systems, and which conventional control covers none of them?
A strong answer walks through the practice itself, with one real case, what you decided, and what the evidence showed afterwards.
Roles that ask for it
- Cybersecurity Risk Analystcore, working knowledge
- AI Governance Engineercore, depth expected
- AI Incident Response Leadcore, depth expected
- AI Security Architectcore, depth expected
- Chief Information Security Officer, AI security focuscore, depth expected
- Chief Technology Officer, AI engineering editioncore, depth expected
- Third-Party AI Risk Analystrequired, working knowledge
- AI Evaluation Specialistrequired, working knowledge
- AI Privacy Engineerrequired, working knowledge
- AI Vendor Risk Managerrequired, working knowledge
- Security Compliance Managerrequired, working knowledge
- Third-Party Cyber Risk Managerrequired, working knowledge
- Chief Information Officer, technology leadership editionrequired, working knowledge
Backgrounds that already carry it
- Cybersecurity and IT (described, not yet shown)
Threat modeling and control design transfer; the AI-specific threats are new.
- A computer science or software engineering degree (described, not yet shown)
Security fundamentals are a course you took, not yet a control you ran.
- A cybersecurity or information assurance degree (shown by a work product)
Security fundamentals are your coursework and your labs.
Where it is taught and graded
17 graded topics, each passed by explaining it back. The first module of every program is free with a free account.
- Module 1: Digital Foundations (1)
- Module 8: Assessment and Continuous Learning (1)
- Module 10: AI Security Fundamentals (6)
- Module 5: Technical Controls and Threat Modeling (2)
- Module 6: Testing and Red-Teaming (2)
- Module 9: Risk, Resilience and Frontier AI (2)
- Module 23: Technical Credibility Deep Dive (2)
- Module 5: Poison, Leaks, and the Adversary (1)
Questions
- What is AI security fundamentals?
- Understands prompt injection, data poisoning, model theft, insecure integrations and excessive agent privileges, and the controls that reduce each.
- Which AI governance roles ask for AI security fundamentals?
- 13 roles on the map name it, and it is core to Cybersecurity Risk Analyst, AI Governance Engineer, AI Incident Response Lead, AI Security Architect, Chief Information Security Officer, AI security focus, Chief Technology Officer, AI engineering edition.
- How do I learn and prove AI security fundamentals?
- 17 graded topics teach it across 5 programs. Each topic is graded by explaining it back against its own transcript, so a pass is evidence, not attendance. The first module of every program is free with a free account.
- What interview question tests AI security fundamentals?
- What are the security failure modes specific to AI systems, and which conventional control covers none of them? A strong answer shows the practice itself: Understands prompt injection, data poisoning, model theft, insecure integrations and excessive agent privileges, and the controls that reduce each.