Stage 1 · The AI threat surface
2-3 weeks
OWASP LLM Top 10, MITRE ATLAS, NIST AI 600-1. The catalog of AI-specific attack patterns and the controls that map to each.
View AI Security Engineering →Cybersecurity and Applied AI career intelligence
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Founded by Julian Calvo, Ed.D., M.S.
Cybersecurity × Applied AI · Convergence
AI Security Engineering is the convergence persona where cybersecurity engineers move into defending AI systems. The 2026 market pays a measurable premium for engineers who can credibly threat-model an AI agent, audit a RAG pipeline, and run an AI red-team engagement.
The highest-paying convergence persona. Prompt injection, agent abuse, supply chain, model integrity, AI red teaming.
What this path pays
$150K → $310K-$520K
Senior security engineer base bands sit at $150-210K (Lightcast 2024). AI security engineer total comp at frontier labs and regulated banks in 2026 clusters at $310-520K, including equity premium.
Source: BLS + Lightcast 2024 + ISC2 2025 AI security workforce data
Why this path
Less than 5,000 practitioners globally meet the bar that a frontier lab or a regulated bank's AI Risk team would hire at senior level (ISC2 2025 estimate). Demand from frontier labs, regulated industries, hyperscalers, and pure-play AI security vendors is at least an order of magnitude larger. The track teaches OWASP LLM Top 10 controls in production, MITRE ATLAS adversarial techniques, prompt injection defense, and the AI red-team engagement playbook.
Stage 1 · The AI threat surface
2-3 weeks
OWASP LLM Top 10, MITRE ATLAS, NIST AI 600-1. The catalog of AI-specific attack patterns and the controls that map to each.
View AI Security Engineering →Stage 2 · Prompt injection defense
2 weeks
Direct + indirect injection, encoding tricks, output manipulation, image-markdown exfiltration. Defenses at the prompt, gateway, and renderer layers.
View AI Security Engineering →Stage 3 · Agent security + supply chain
3-4 weeks
Excessive agency, tool-call abuse, RAG poisoning, model supply-chain integrity. The control set production agentic systems require.
View AI Security Engineering →Stage 4 · AI red team engagement
3-4 weeks
Run a complete AI red-team engagement against a target system. Documented threat model, attack chain, remediation guidance.
View AI Security Engineering →Yes at frontier labs (Anthropic, OpenAI, Google DeepMind security teams), regulated banks' AI Risk teams, and pure-play AI security vendors. Equity is a meaningful share of the total. Median across the broader AI security engineer market in 2026 is closer to $245-360K. The premium reflects scarce supply: ISC2 2025 estimates fewer than 5,000 globally meet the senior bar.
Senior security engineering background or strong AI engineering background — both work. Pure security engineers add ML fundamentals + AI threat surface knowledge; pure AI engineers add adversarial thinking + security engineering discipline. Without one of those bases, this is a 12-18 month path, not a 4-month path.
Traditional AppSec assumes deterministic code. AI systems introduce probabilistic outputs, natural-language attack surfaces, training data as an attack vector, and model behavior that shifts under adversarial input. Prompt injection has no analog in SAST/DAST tooling; adversarial examples exploit model geometry. The threat model is fundamentally different.
No. Hiring evidence works: a documented AI red-team engagement, a public detection write-up, an OSS contribution to a guardrail framework, a conference talk. The bar is 'can you reason about adversarial AI behavior at depth' — research publications are one signal, not the only one.