AI Security Engineer
AI Security Engineers secure AI systems against attacks and misuse. They work on adversarial robustness, model security, and responsible AI.
Median Salary
$180,000
Job Growth
Emerging — AI security is critical emerging challenge
Experience Level
Entry to Leadership
Salary Progression
| Experience Level | Annual Salary |
|---|---|
| Entry Level | $120,000 |
| Mid-Level (5-8 years) | $180,000 |
| Senior (8-12 years) | $215,000 |
| Leadership / Principal | $250,000+ |
What Does a AI Security Engineer Do?
AI Security Engineers secure AI systems against attacks and misuse. They conduct threat modeling identifying vulnerabilities. They perform adversarial testing attempting to break models. They implement defenses against attacks. They work on privacy—protecting training data. They handle model extraction and theft prevention. They ensure responsible AI deployment.
A Typical Day
Threat modeling: Identify security threats in AI system.
Testing: Conduct adversarial testing attacking model.
Analysis: Analyze attack results. Understand vulnerabilities.
Defense: Implement defenses against identified threats.
Evaluation: Evaluate effectiveness of defenses.
Documentation: Document security findings.
Recommendation: Recommend security improvements.
Key Skills
Career Progression
AI security engineers often progress to security lead or chief security architect roles.
How to Get Started
Security: Strong security fundamentals.
Machine learning: Understanding of how ML works and vulnerabilities.
Adversarial: Study adversarial machine learning and attacks.
Python: Python for implementing attacks and defenses.
Red-teaming: Experience with red-teaming and penetration testing.
Research: Follow AI security research. It's an active area.
Real systems: Work on real AI system security.
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Explore Track →Frequently Asked Questions
What security threats exist for AI systems?▼
Adversarial examples, model theft, data poisoning, privacy attacks, prompt injection, jailbreaking.
What's adversarial robustness?▼
Building models that maintain performance even when attacked. Adversarial examples are crafted inputs that fool models.
How do you test AI security?▼
Adversarial testing, penetration testing, fuzzing, red-teaming.
What's the biggest AI security challenge?▼
Adversarial robustness is hard. Can't guarantee safety. Trade-off with performance.
Is AI security a growing field?▼
Yes. Critical as AI deployment increases. High demand, growing salaries.
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Last updated: 2026-03-07