Generative AI initiatives moved from prototypes to production faster than most security teams anticipated. The result: a scramble to define roles, set compensation, and design assessments for AI security talent. This guide synthesizes Cyfer marketplace data with public research to help you build the right roles.
Map AI Security Responsibilities
Use the MITRE ATLAS knowledge base and OWASP Top 10 for LLMs as reference points. Responsibilities typically cluster into:
- Model and data protection: Guard against prompt injection, data leakage, and model theft.
- AI application security: Secure APIs, RAG pipelines, and integrations with SaaS or internal systems.
- AI governance: Align with EU AI Act, NIST AI Risk Management Framework, and internal compliance requirements.
Key Roles and Salary Benchmarks
| Role | Core Skills | Typical Base (US) |
|---|---|---|
| AI Security Engineer | Threat modeling for ML pipelines, GPU runtime hardening, observability | $190k–$265k |
| AI Red Team Lead | Adversarial ML, jailbreak chaining, exploit tooling | $210k–$280k |
| AI Governance Lead | Policy, privacy, regulatory expertise | $180k–$230k |
Remote-friendly companies often add premiums for candidates with both ML and traditional security pedigrees.
Competency-Based Interviewing
Design interview loops that test:
- Scenario design: Ask candidates to secure a multi-tenant inference API, referencing NVIDIA NeMo Guardrails or similar frameworks.
- Red-team workshop: Provide transcripts of successful jailbreaks and evaluate mitigation strategies.
- Policy negotiation: Simulate debates with legal/product leaders about AI transparency obligations.
Share pre-reading such as Google's Secure AI Framework so candidates can prepare thoughtful responses.
Sourcing Strategies
- Tap into ML security communities like AI Village and MLSecOps.
- Partner with academic labs researching adversarial ML.
- Reengage alumni from your existing security org who upskilled via AI fellowships or bootcamps.
Retention Considerations
AI security talent seeks ongoing experimentation. Budget for:
- Dedicated GPU sandboxes for testing.
- Conference travel to DEF CON AI Village, Black Hat AI track, and NeurIPS security workshops.
- Rotations with research labs or product squads to avoid siloing.
Org Design and Growth
Decide where AI security sits:
- Embedded with AI platform engineering for velocity.
- Within the broader security engineering org for governance alignment.
- Matrixed across product lines when multiple business units deploy AI.
Whichever model you choose, document escalation paths for incidents involving models so responsibilities stay clear.
Career Pathing and Upskilling
- Offer technical principal tracks specializing in adversarial ML.
- Provide management paths for leaders running pods across red/blue/gov.
- Fund certifications or nano-degrees such as the MIT AI strategy program to show commitment.
Case Study
A consumer healthcare company building AI scribes created a three-person AI security pod reporting to the CISO but dotted-line to the ML platform VP. They used this article’s framework to define roles: an engineer focused on inference hardening, a red teamer to continuously jailbreak models, and a governance lead to interface with legal. Within the first quarter, they cut successful prompt-injection incidents by 70% and passed a rigorous customer security assessment referencing the NIST AI RMF.
Action Checklist
- Map AI attack surfaces using ATLAS/OWASP guidance.
- Define role charters with salary benchmarks before opening reqs.
- Share interview prep materials rooted in real model deployments.
- Budget for experimentation (GPU labs, conference travel).
- Publish internal career paths to retain AI security specialists.
Action plan: define responsibilities tied to ATLAS/OWASP, set compensation using AI-specific benchmarks, and design interview loops that mirror real-world adversarial scenarios. This positions your organization to deploy AI safely and credibly.