AI Security Engineer
An AI Security Engineer protects AI and machine learning systems, securing models, training data, and the AI supply chain while evaluating those systems for weaknesses such as prompt injection and data poisoning.
Also known as: AI/ML Security Engineer, Machine Learning Security Engineer, ML Security Engineer, GenAI Security Engineer, LLM Security Engineer, AI Application Security Engineer, AI Red Team Engineer
What Is a AI Security Engineer?
The core of this work is treating AI systems themselves as assets to defend. An AI Security Engineer secures machine learning models, the data used to train them, and the pipelines and services that serve them in production. That includes protecting model weights and training datasets from theft and tampering, locking down inference endpoints, and applying security controls to both AI-enabled products and the AI tools an organization adopts internally.
A distinctive part of the role is evaluating AI systems for failure modes that traditional application testing does not cover: prompt injection and jailbreaking of language models, data poisoning of training sets, model extraction, and leakage of sensitive information through model outputs. AI Security Engineers run adversarial testing against models before and after deployment, then translate what those exercises reveal into guardrails, input and output filtering, and access controls. They also secure the AI supply chain, reviewing third-party models, datasets, and frameworks before they enter the environment.
This differs from a Security Engineer, who builds and maintains general defensive tooling such as firewalls, detection, and alerting across infrastructure, and from a Cybersecurity Software Engineer, who builds security products and secure software. The AI Security Engineer applies security engineering discipline to AI systems specifically, so the role sits at the intersection of security teams and the machine learning engineers and product teams shipping AI features. Frameworks such as MITRE ATLAS, the OWASP Top 10 for LLM Applications, and the NIST AI Risk Management Framework give the work a shared vocabulary.
What a AI Security Engineer Does
Common tasks and responsibilities for this role. Emphasis varies by organization, and how the work is actually distributed tells you more than the title on the job description.
- Assess AI and machine learning systems for weaknesses such as prompt injection, jailbreaking, data poisoning, and model extraction
- Design and implement security controls for AI-enabled products and internal AI tools
- Protect training data, model weights, and inference endpoints across the machine learning lifecycle
- Secure the AI supply chain, including third-party models, datasets, and frameworks
- Run adversarial testing and red team exercises against models before and after deployment
- Define guardrails, input and output filtering, and access controls for LLM applications
- Set standards for safe internal AI use in collaboration with security, engineering, and governance teams
- Investigate AI-related security incidents and translate findings into improved controls
Common Technologies and Environments
AI security testing
ML platforms & pipelines
Standards & guidance
Certifications Often Held by AI Security Engineers
Certifications commonly associated with this role. None are universally required, and in the hiring conversations CyberSN sees, hands-on experience with the responsibilities above carries at least as much weight.
Where This Role Fits in a Career
Career paths in cybersecurity follow responsibilities, not titles. The experience built in this role transfers to adjacent roles that share overlapping tasks and capabilities.
Common Questions About the AI Security Engineer Role
What does an AI Security Engineer do day to day?
Typical days combine assessment and building: probing models and AI applications for weaknesses such as prompt injection and data poisoning, reviewing third-party models and datasets before they are adopted, implementing guardrails and access controls around inference endpoints, and working with machine learning and product teams to ship AI features securely. Incident work also appears when an AI system behaves unexpectedly or is attacked.
How is an AI Security Engineer different from a Security Engineer?
A Security Engineer builds and maintains general defensive tooling, such as firewalls, detection systems, and alerting, across an organization's infrastructure, services, and networks. An AI Security Engineer focuses that same engineering discipline on AI systems themselves: models, training data, pipelines, and AI-enabled applications, which have failure modes like prompt injection and model extraction that traditional controls were not designed for. The roles collaborate closely, and security engineering experience is a natural path into AI security.
Do I need machine learning experience to become an AI Security Engineer?
You need working literacy in how machine learning systems are built and deployed: how models are trained, what a pipeline and an inference endpoint look like, and how LLM applications are assembled. You do not need to be a research scientist. Many people enter from security engineering or application security and build ML understanding on the job, while others come from ML engineering and add security depth.
Which certifications are relevant for AI Security Engineers?
Options include the IAPP Artificial Intelligence Governance Professional (AIGP) and ISACA's Advanced in AI Security Management (AAISM), typically paired with a broad security certification such as the CISSP. Demonstrated hands-on work, such as AI red teaming or securing production ML systems, carries at least as much weight as any certification in this specialty.
How does this role differ from a Cybersecurity Software Engineer?
A Cybersecurity Software Engineer writes software: building security products and developing secure code. An AI Security Engineer secures a class of systems: models, training data, and AI applications, whether or not they write the underlying software. There is overlap where AI security work involves building tooling, but the center of gravity differs, with one role oriented around software delivery and the other around assessing and defending AI systems.
Explore Adjacent Career Paths
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