Your security team is about to add a new specialization. Not because of a compliance mandate or a framework update—because AI systems break every assumption traditional security roles are built on.
At Snyk's inaugural AI Security Summit, speakers predicted that within three years, every Fortune 500 company will have AI Security Engineers on staff. The timeline matters less than the reason: AI models are now "so good at computer security they are beginning to find critical vulnerabilities," which means attackers are using them too.
This guide defines the AI Security Engineer role, its scope, and what your organization needs to implement it.
Scope - What This Guide Covers
This guide addresses:
- The security responsibilities unique to AI systems that traditional roles don't cover
- Core competencies and technical skills required
- How to structure the role within existing security teams
- Compliance touchpoints with emerging AI regulations
- Common mistakes when staffing this function
What this guide does NOT cover:
- General machine learning operations (MLOps)
- Data science team management
- AI ethics and bias mitigation (except where they intersect with security)
Key Concepts and Definitions
AI Security Engineer: A security practitioner who secures AI systems against attacks that exploit model behavior, training data, or inference processes. This is distinct from using AI tools for security work.
Non-deterministic systems: Unlike traditional software that produces the same output for a given input, AI models can produce variable outputs. You cannot write a test case that says "input X must always produce output Y." This breaks conventional security validation methods.
Adversarial inputs: Crafted inputs designed to manipulate model behavior—like adding specific pixel patterns to an image that cause a classifier to misidentify it, or prompt injections that bypass safety guardrails.
Model poisoning: Attacks that corrupt training data or fine-tuning processes to embed backdoors or bias model behavior.
Inference-time attacks: Exploits that occur when a model is actively processing requests—prompt injection, data exfiltration through model responses, or resource exhaustion attacks.
Requirements Breakdown
The AI Security Engineer role addresses gaps in three areas:
1. Pre-deployment Security
What traditional AppSec misses: Your application security team validates code logic, dependency vulnerabilities, and authentication flows. They don't evaluate whether a model leaks training data through its outputs or whether the fine-tuning pipeline can be poisoned.
AI Security Engineer responsibilities:
- Validate model provenance and supply chain (where did this model come from, who trained it, what data was used)
- Test for training data leakage
- Assess model robustness against adversarial examples
- Review fine-tuning and RLHF processes for manipulation vectors
- Evaluate prompt injection defenses before production
2. Runtime Monitoring and Response
What SOC teams miss: Security operations centers monitor network traffic, authentication events, and endpoint behavior. They don't flag when a model starts producing anomalous outputs or when inference patterns suggest data exfiltration.
AI Security Engineer responsibilities:
- Monitor model behavior drift
- Detect inference-time attacks (unusual prompt patterns, output steering attempts)
- Identify data extraction through model queries
- Track model performance degradation that could indicate poisoning
- Respond to incidents involving model manipulation
3. Compliance and Risk Management
What compliance teams miss: Your compliance function maps controls to NIST CSF v2.0, ISO 27001, or SOC 2 requirements. These frameworks don't address AI-specific risks like model inversion or membership inference attacks.
AI Security Engineer responsibilities:
- Interpret emerging regulations (EU AI Act, state-level AI bills)
- Document model risk assessments
- Establish AI system inventory and classification
- Define acceptable use policies for AI systems
- Create incident response procedures for AI-specific attacks
Implementation Guidance
If You're Building This Role from Scratch
Start with a hybrid position: Your first AI Security Engineer should report to your security leadership but work embedded with ML/AI teams. This person needs access to model training pipelines, not just production systems.
Required technical foundation:
- Understanding of model architectures (transformers, CNNs, RNNs)
- Ability to read Python ML code (PyTorch, TensorFlow, JAX)
- Experience with traditional AppSec or infrastructure security
- Familiarity with ML frameworks and deployment patterns
Don't require a PhD: You need someone who can threat model an LLM-powered feature and write security tests for it. Deep ML research experience is less valuable than security engineering fundamentals plus willingness to learn model internals.
If You're Upskilling Existing Team Members
Best candidates:
- AppSec engineers who already review code that calls AI APIs
- Cloud security engineers who manage ML infrastructure
- Security researchers interested in novel attack surfaces
Training path (3-6 months):
- Hands-on model training and fine-tuning (build intuition for how models work)
- OWASP Top 10 for LLM Applications (covers common AI-specific vulnerabilities)
- Adversarial ML fundamentals (academic papers, CTF challenges)
- Compliance frameworks emerging for AI (EU AI Act, NIST AI Risk Management Framework)
Integration with Existing Teams
During threat modeling: AI Security Engineer joins when features use ML models, focuses on model-specific attack vectors while AppSec covers traditional threats.
During code review: AI Security Engineer reviews training scripts, inference code, prompt templates, and model configuration. AppSec reviews authentication, authorization, and business logic.
During incidents: AI Security Engineer determines if anomalous behavior stems from model manipulation. SOC handles containment and forensics.
Common Pitfalls
Treating this as a data science role: Data scientists optimize for model accuracy and performance. AI Security Engineers optimize for resilience against adversarial behavior. These goals often conflict. Don't expect your ML team to self-police security.
Waiting for perfect tooling: The AI security tool market is immature. Your AI Security Engineer will write custom tests, build monitoring dashboards from scratch, and create novel detection methods. If you need mature, vendor-supported tools for every task, you're not ready for this role.
Ignoring regulatory timelines: The EU AI Act enters into force in phases starting 2024. If you deploy AI systems in EU markets, you need someone tracking compliance requirements now, not when enforcement begins.
Assuming traditional security tools cover AI: Your SAST scanner won't detect prompt injection vulnerabilities. Your WAF won't block adversarial inputs designed to manipulate model behavior. Your SIEM won't alert on model behavior drift. You need new detection and prevention capabilities.
Hiring for research over engineering: You need someone who can secure the AI systems you're deploying this quarter, not someone who can publish papers about theoretical attacks. Prioritize practical security engineering skills.
Quick Reference Table
| Responsibility Area | Traditional Role | AI Security Engineer Addition |
|---|---|---|
| Pre-deployment testing | AppSec validates code, dependencies | Tests for training data leakage, adversarial robustness, prompt injection |
| Runtime monitoring | SOC monitors network, endpoints | Monitors model behavior drift, inference patterns, output anomalies |
| Compliance mapping | GRC maps to ISO 27001, SOC 2 | Interprets EU AI Act, NIST AI RMF, documents model risk assessments |
| Incident response | Security team contains and investigates | Determines if incident involves model manipulation, assesses model integrity |
| Threat modeling | AppSec identifies attack vectors | Adds model inversion, poisoning, adversarial inputs to threat model |
| Vendor assessment | Security reviews SaaS vendors | Evaluates model provenance, training data sources, fine-tuning risks |
The AI Security Engineer role exists because AI systems have attack surfaces that didn't exist five years ago. Your application security team can't secure what they don't understand, and your ML team can't defend against attacks they've never considered. This role bridges that gap.
Start by identifying one person who can learn both domains. Give them time to experiment with attacks against your own models. Let them fail fast and document what they learn. The role will evolve as AI systems evolve—but you need someone in the position now, learning alongside your AI deployments.
EU AI Act
