AI Security Specialist
Identifies, tests, and mitigates security vulnerabilities specific to AI systems, including prompt injection, adversarial attacks, data poisoning, and model extraction.
Overview
An AI security specialist identifies and defends against the unique threats that arise when machine learning systems are deployed in the real world, from prompt injection and jailbreaking to adversarial examples that fool vision models and poisoning attacks that corrupt training data. They work across the full lifecycle of AI systems, conducting threat modeling during design, penetration testing during development, and ongoing monitoring in production. The role is at the frontier of both security and AI research, and it requires genuine curiosity about how systems can be broken.
Beginner roadmap
Phase 1: Security and AI FundamentalsWeeks 1-6
Build foundational knowledge in both cybersecurity principles and machine learning concepts, understanding how each domain thinks about trust, attack surfaces, and failure modes.
Phase 2: AI Attack TaxonomyWeeks 7-14
Study and implement the major categories of AI attacks including prompt injection, adversarial examples, model extraction, and data poisoning, learning how each exploits model properties.
Phase 3: Red Teaming and EvaluationWeeks 15-20
Conduct structured red team exercises against AI applications, develop evaluation frameworks for safety and security, and practice writing clear vulnerability reports.
Phase 4: Defenses and Production SecurityWeeks 21-26
Design and implement defensive measures, build monitoring for adversarial behavior in production systems, and document a complete security assessment for your portfolio.
Portfolio ideas
- A documented red team assessment of a publicly available AI application, with findings and recommended mitigations.
- A technical write-up demonstrating a prompt injection or adversarial example attack with clear explanation of the technique.
- A threat model for a realistic AI system deployment, including attack paths and defensive controls.
- A comparison of defensive techniques against a specific AI attack class, with measured effectiveness.
- A responsible disclosure write-up for a vulnerability you discovered, following standard disclosure practices.
Salary & sources
Salary ranges vary widely by region, seniority, industry, and company. Check current data on reputable salary aggregators (placeholder - verify before publishing).
Ready to put this into action?
Explore verified openings when they are available, or keep building practical skills through our guides.
Frequently asked questions
Traditional security focuses on protecting software, networks, and data from exploitation. AI security adds an entirely new attack surface: the models themselves. Prompt injection, adversarial examples, model inversion, and training data poisoning are threats that do not exist in conventional systems.
A strong foundation in either cybersecurity or machine learning provides a useful starting point. The ideal is deep knowledge of both, since understanding how models work is essential to finding and exploiting their weaknesses.
AI red teaming involves adversarially probing AI systems to find ways to make them behave unsafely, unfairly, or unreliably, before real users or malicious actors do. It combines security testing mindset with deep understanding of model behavior.
Yes, rapidly. As AI becomes embedded in critical systems, the attack surface it creates has attracted serious attention from regulators, enterprises, and attackers alike. Organizations are actively hiring for roles that can both find AI vulnerabilities and build defenses against them.
Related career guides
AI Ethics Specialist
Ensures AI systems are developed and deployed responsibly by identifying bias, advancing fairness, and shaping governance policies that protect people and organizations.
AI Researcher
Advances the frontier of artificial intelligence by designing experiments, publishing findings, and developing novel techniques in academic or industrial research settings.
AI Solutions Architect
Designs end-to-end AI system architectures for enterprise clients, translating complex business requirements into scalable, secure, and maintainable technical blueprints.
Ready to build AI career skills?
Start with the practical guides, glossary, and comparisons that give the job market context.