FieldsAI Security Engineer
AI Security Engineer
Secure AI systems against adversarial attacks, injection, and misuse.
Career
What you learn
- Why AI systems break differently from conventional software
- Prompt injection defended in measured layers rather than declared solved
- Agents secured by what they can reach before what they might do
- Honest reporting of the risk that remains after every defence
What you will understand
- Instructions and data arriving through the same channel
- Direct and indirect injection, and why the indirect kind is harder
- Blast radius as a deterministic limit where behaviour is probabilistic
- Model and training-data attacks, from extraction to poisoning
How you will practice
- Attack an AI application you built, then measure attack success before and after hardening
- Secure an agent so a working injection still cannot escalate what it reaches
- Run a structured red-team engagement and report findings with honest reproduction rates
What you will build
- A hardened AI application with before and after attack-success measurements
- An agent security review with a blast-radius map and a survived injection attempt
The proof you build
Evidence that you can secure an AI system under adversarial pressure and state plainly what risk remains.
Your work is observed with your consent, scored for independence and assistance, and turned into proof that carries a confidence level. The career path can reach a high-assurance credential, anchored by a scored capstone.
What is not live yet
The desktop app, consent-based observation, scoring, and credentials are in development. Nothing here implies they are live yet.
Get early access