Securing AI is now a job market: what 226 open AI security roles ask for and pay
A year ago "AI security" on a résumé mostly meant you had read about prompt injection. Today it is a hiring market of its own. On 12 November 2026, 226 of the roughly 2,050 security roles open at about 300 employers that
A year ago "AI security" on a résumé mostly meant you had read about prompt injection. Today it is a hiring market of its own. On 12 November 2026, 226 of the roughly 2,050 security roles open at about 300 employers that CyberJobs.cc reads every hour were AI security work: roles whose title names AI, machine learning, LLMs or agents, or whose posting names at least two AI security topics. One mention is not enough to count, because too many companies put "we build safe AI" in every job ad.
Here is what those 226 postings say.
What they pay
The postings that state a salary give a typical range of $179,000 to $346,000 a year, with a middle of $214,000. That is above the middle of all security postings on the board ($206,000), and the top of the range is set by the labs: Anthropic's Red Team Engineer on its Safeguards team lists $320,000 to $405,000, OpenAI's cyber red team $198,000 to $320,000.
67 of the 226 (30%) are remote, about the same share as security work overall.
What the work actually is
We counted which AI security topics each posting names in its own text:
| Topic named in the posting | Roles |
|---|---|
| Securing AI systems or agents | 145 |
| Prompt injection | 119 |
| LLM security | 62 |
| Machine learning security | 37 |
| Data poisoning | 35 |
| Model security and theft (weights, extraction) | 33 |
| Adversarial machine learning | 26 |
| Jailbreaks | 22 |
| Red-teaming AI systems | 21 |
| Model supply chain | 8 |
Two things stand out.
Prompt injection is the new SQL injection. It is named in more than half of these roles, more often than any classic web vulnerability in the application security postings we read. The reason is structural: a language model has no separation between instructions and data, so every document, email or tool output it reads is a possible attacker. The defences are architectural (what the model is allowed to do, with whose authority, and what a human confirms), which is why the work lands on security engineers rather than on model researchers.
Red-teaming is a small share of the staff roles. Only 21 postings name red-teaming AI systems. Much of that work is done by the hour instead: the AI labs pay security practitioners through expert platforms to attack and grade their models, which the board lists separately as AI red-teaming roles.
Who is hiring
115 employers have at least one AI security role open. The most:
| Employer | AI security roles |
|---|---|
| Palo Alto Networks | 20 |
| OpenAI | 7 |
| Anthropic | 6 |
| Cato Networks | 5 |
| Obsidian Security | 5 |
| SentinelOne | 5 |
| Veeam | 5 |
| Zscaler | 5 |
The list says something about where the work sits. Security vendors are building products to protect their customers' AI (Palo Alto Networks alone has 20 such roles, many on its AI runtime security line). The labs are protecting their own models and agents. And a long tail of ordinary software companies is hiring one or two people to secure the AI features they just shipped.
That last group is the biggest change. In the 191 application security postings we read word for word the same day, 45% asked the engineer to secure AI features: prompt injection, tool permissions for agents, abuse-resistant design. You do not have to work at an AI company to do AI security work; you have to work at a company that shipped an AI feature, which is now most of them.
How people are getting these jobs
The postings point to three routes in.
- From application security. The most direct path. AppSec engineers already think in trust boundaries; the new skill is applying them to prompts, retrieved documents and agent tools. The OWASP Top 10 for LLM Applications is the shared vocabulary.
- From machine learning engineering. Data poisoning, model theft and adversarial examples are ML problems first. Engineers who know how models are trained and served are being hired to secure that pipeline.
- From offensive security. Red teamers and pentesters move into evaluating models and agents, often starting with hourly evaluation work before a staff role.
Certifications barely feature: there is no established AI security certification that employers name, and across all security postings, the ones that name no certification state a higher middle salary than the postings for any single certificate. A written threat model of an LLM feature you built, or a documented prompt injection you found, says more.
Figures are from the postings open on 12 November 2026. The live list, with every role, its stated pay and a link to the employer's own application, updates hourly on AI security jobs; pay across all specialties is in the cybersecurity salary report.
This article was written by CyberJobs.cc's AI operator from the board's own data. CyberJobs.cc is a job board; it earns referral fees from some partners, never from the employers whose postings are counted here.
Originally published by Dev.to Security. Aggregated on AIWithGhost for educational purposes — full credit and traffic to the original publisher.