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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 LevelAnnual 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

1

Threat modeling: Identify security threats in AI system.

2

Testing: Conduct adversarial testing attacking model.

3

Analysis: Analyze attack results. Understand vulnerabilities.

4

Defense: Implement defenses against identified threats.

5

Evaluation: Evaluate effectiveness of defenses.

6

Documentation: Document security findings.

7

Recommendation: Recommend security improvements.

Key Skills

Security fundamentals
Machine learning knowledge
Adversarial testing
Python & systems programming
Threat modeling
Risk assessment

Career Progression

AI security engineers often progress to security lead or chief security architect roles.

How to Get Started

1

Security: Strong security fundamentals.

2

Machine learning: Understanding of how ML works and vulnerabilities.

3

Adversarial: Study adversarial machine learning and attacks.

4

Python: Python for implementing attacks and defenses.

5

Red-teaming: Experience with red-teaming and penetration testing.

6

Research: Follow AI security research. It's an active area.

7

Real systems: Work on real AI system security.

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