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Founded by Julian Calvo, Ed.D. · Cybersecurity career intelligence · Est. 2024
AI security is the fastest-growing cybersecurity specialty. Roles pay 30–50% more than equivalent traditional cybersecurity positions. The transition window is open: existing cybersecurity practitioners are well-positioned because the threat-model rigor transfers cleanly. Below: the 14 AI security roles DecipherU tracks today, sorted by median compensation.
AI security job postings are growing roughly 4× the pace of the broader cybersecurity market based on aggregated industry reports through 2026.
AI security roles pay 30–50% above equivalent traditional cybersecurity roles at the same seniority level.
The window for traditional cybersecurity practitioners to add AI security depth before the talent supply catches up to demand. Acting now compounds.
An Adversarial ML Researcher discovers novel attacks against ML systems and publishes the defenses. The role lives at AI labs, AI-native security companies, and a small number of mature enterprise security teams. You read papers, you reprod…
An AI Application Security Engineer secures the application layer that wraps LLMs, agents, and ML inference: input handling, output filtering, tool-call governance, retrieval pipelines, and the user-facing surface where prompt injection and…
An MLOps Security Engineer secures the ML pipeline end-to-end: training data ingestion, feature stores, training infrastructure, model registry, deployment, inference endpoints, and the supply chain of every dependency along the way. The ro…
An LLM Security Specialist focuses on the model-interaction layer specifically: prompt injection, jailbreak techniques, system-prompt leakage, output safety, and the moderation layers that wrap commercial and open-source LLMs. The role is n…
An AI/ML Security Engineer secures the machine-learning systems a company builds or integrates: model training pipelines, inference endpoints, prompt surfaces, and the data stores that feed and audit them. The role is new and drifting fast.…
An AI Threat Detection Engineer builds detection logic for AI-specific abuse and attack patterns in production: prompt-injection signatures, agent-tool-call anomalies, scraping behavior, model-extraction attempts, and the broader category o…
An AI red team operator attacks machine learning systems the way a traditional pentester attacks a network. You probe LLM applications for prompt injection, jailbreaks, and tool-call abuse. You evaluate guardrails for the gaps a real advers…
An AI Privacy Engineer designs and ships the privacy-preserving controls in AI systems: differential privacy in training, federated learning architectures, PII minimization in prompts and outputs, training data lineage, and the data-subject…
A Security Architect designs the cybersecurity systems other teams operate. You pick the identity model, the network segmentation strategy, the logging topology, and the zero-trust reference architecture. The role sits upstream of engineeri…
An AI Governance Specialist runs the program that gets AI deployed responsibly: NIST AI RMF implementation, EU AI Act compliance, internal AI policy, model-risk inventories, and the cross-functional review process that gates new AI features…
An AI Compliance Auditor runs the audit function for AI deployments: model evidence collection, fairness and bias testing, regulatory mapping, third-party AI vendor assessments, and the documentation trail regulators and customers require. …
A Financial Services Security Engineer runs the cybersecurity controls for banks, broker-dealers, insurers, and fintech startups. The role blends the usual defensive work with industry-specific regulation: PCI DSS for card data, SOX for con…
A Mobile Security Engineer secures iOS and Android applications, the devices they run on, and the backend APIs they talk to. The role sits between AppSec and embedded: you review mobile code, reverse-engineer competitor or suspicious apps, …
A Compliance Auditor evaluates whether an organization's cybersecurity program matches the controls its regulators, contracts, and frameworks require. You test evidence, document findings, and write the reports auditors and management rely …
DecipherU's AI security career data is rebuilt from primary sources every quarter: BLS occupational projections, public salary postings on AI lab career pages, NIST AI RMF role mappings, OWASP LLM Top 10 working group materials, and our own placement data from the hiring marketplace.
We do not include adjacent fields (data scientist, ML engineer, MLOps) on this hub even though they intersect — those are different career tracks. This hub focuses on roles where the security function is primary and AI is the system being secured.
Salary numbers reflect total compensation midpoint for senior individual contributors at companies actively hiring. Entry-level and management ranges are documented on each individual role page.
Career transition timelines and outcomes vary by individual. This guide is for educational purposes and does not guarantee employment outcomes.
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