AI risk and security
Hire AI Security Engineers who reduce the new attack surface of AI products.
Get a dedicated AI Security Engineer to threat-model LLM apps, test prompt injection, protect sensitive data, review tool permissions, and design practical AI safety controls. Shortlist in 48 hours. Two-week paid trial in your codebase. Starts at $2,500/mo.
Direct answer
What does AI Security Engineer own?
An AI Security Engineer is the right hire when AI features introduce risks that normal application security does not fully cover. This role owns AI threat modeling, prompt injection testing, data exposure review, RAG/agent security checks, tool permission audits, guardrail design, logging requirements, and security backlog creation.
Hiring problem
Hire this role when AI introduces risks your normal appsec process does not fully cover.
Traditional application security does not fully cover prompt injection, indirect prompt attacks, tool misuse, sensitive context leakage, unsafe model outputs, and AI-specific logging/monitoring gaps.
- AI threat modeling
- Prompt injection testing
- Data leakage review
- Tool permission audit
- RAG security review
- Agent safety controls
- Sensitive output handling
- Logging/monitoring requirements
- Human approval controls
- Security documentation
- Generic appsec only
- Compliance paperwork without engineering review
- Model quality tuning
- Product UI build
First 14-day proof
The trial should create evidence, not just activity.
AI threat model
Maps users, model, prompts, tools, context, logs, and data stores into a clear picture of where abuse can enter. It is the foundation every other control is ranked against.
Attack scenario list
Documents prompt injection, indirect injection, exfiltration, unsafe action, jailbreak, and tool-misuse scenarios specific to your system — not a generic checklist.
Prompt injection test cases
Reproducible tests against prompts, documents, tools, and retrieved context. A weak version is a one-time poke; a strong version is a suite you can re-run on every release.
Tool permission review
Shows which tools can act, what they can change, and what approval gates are needed. It bounds what a compromised prompt could actually do.
Data exposure findings
Identifies sensitive data paths, logging risks, context leakage, and permission gaps — the issues most likely to block an enterprise launch.
Guardrail plan
Defines practical controls: validation, allowlists, approvals, monitoring, refusal, and incident review. It translates risk into engineering work.
Security backlog
Ranks findings by release risk, severity, effort, and owner so the team fixes what matters before ship date, not everything at once.
Default stack
Stack fluency for AI Security Engineer work.
The exact tools follow your environment. These are the common surfaces we vet against for this role.
Use cases
Where this hire creates leverage.
The best use case is one where the role can own a clear first proof during the paid trial.
LLM app pre-release review
A security review before a customer-facing AI feature ships, ending in a ranked backlog the team can clear before launch.
RAG permission audit
Check whether retrieval exposes or hides documents incorrectly. Pair with a RAG & Context Engineer if the fix is in the retrieval layer itself.
Agent tool safety
Review tool authority, approvals, rollback, and incident surfaces. Pair with an Agentic Workflow Engineer if the architecture needs to change.
Sensitive-data assistant
Design safer AI workflows around private, regulated, or customer-sensitive information, with controls a security team can operate.
AI governance controls
Translate AI policy into concrete engineering controls and review gates, instead of a document nobody can enforce.
Enterprise security approval
Prepare the threat model, tests, and findings that a security team needs before they will sign off on release.
Transparent pricing
Pick seniority by ownership, not mystery quotes.
Supervised delivery for clear implementation work.
Independent feature ownership for production AI work.
High-judgment ownership for ambiguous or risky AI delivery.
Outcome clarity
What should change after you hire this role?
The AI attack surface is mapped.
High-risk prompts, tools, and data paths have tests.
Security backlog is ranked by practical release risk.
Adjacent-role comparison
When another AI role is the better hire.
AI Platform Engineer
Choose AI Platform Engineer if controls need shared platform enforcement.
RAGRAG & Context Engineer
Choose RAG Engineer if retrieval permissioning is the main issue.
AGTAgentic Workflow Engineer
Choose Agentic Workflow Engineer if tool execution architecture is the main issue.
LLMLLM Engineer
Choose LLM Engineer if behavior quality/evals are the main issue.
Vetting criteria
Screened for this role’s failure modes.
AI threat modeling
Prompt injection reasoning
Tool permission audit
Sensitive data controls
Practical security backlog creation
Interview questions
Use the interview to test judgment.
- How would you test indirect prompt injection?
- Where can a RAG system leak data?
- How do you scope tool permissions?
- What logs are required for AI incidents?
Hiring flow
From scope to paid trial.
30-minute role scope
Map the AI workflow, current stack, first deliverable, security boundaries, seniority, and the role that should own the work.
2-3 vetted engineers
Receive a short list with matching rationale. The goal is fewer names with stronger fit, not resume volume.
Paid trial in your codebase
The selected engineer works inside your repo, rituals, issue tracker, and review process so fit is judged by real work.
Continue, replace, pause, or scale
Continue month-to-month, request a free replacement, pause without a long lock-in, or add adjacent roles.
Security, IP, governance
Repo access is scoped before the engineer starts.
NDA, IP assignment, repository access, communication channels, data boundaries, and AI tool rules are clarified before onboarding. Devlyn avoids unverified compliance claims and works within buyer-controlled systems.
FAQ
Questions before you hire AI Security Engineer.
Is Devlyn claiming compliance certifications?
No. We avoid unverified compliance claims. This role supports buyer security rules through scoped access, threat modeling, testing, and practical controls.
What is different about AI security?
AI systems accept natural-language instructions, retrieve sensitive context, and may call tools. That creates injection, exfiltration, misuse, and governance risks normal appsec may miss.
Can this role work with our security team?
Yes. The work translates AI-specific risks into engineering controls that your security team can review and operate.
How fast can I see AI Security Engineer candidates?
After the role scope, Devlyn targets two or three vetted profiles within 48 hours.
What does the two-week paid trial include?
The trial should produce role-specific proof for AI Security Engineer work inside your actual repo, data environment, or approved workflow.
Can the engineer work in our repository?
Yes. Repo access, communication channels, data boundaries, NDA, and IP assignment are scoped before onboarding.
What if fit is wrong?
You can request a free replacement instead of being forced through a long lock-in or conversion fee.
What does pricing include?
Pricing covers one dedicated AI-native engineer. Junior starts at $2,500/mo, mid at $3,500/mo, and senior at $4,500/mo.
Final CTA
Tell us the AI workflow. We’ll confirm whether AI Security Engineer is the right hire.
If another role is a better fit, the role scope should catch that before you interview anyone.