A deep analysis of 100 real Indeed job postings reveals what skills, tools, and credentials are shaping the next generation of cybersecurity careers.
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| I Analyzed 100 AI Cybersecurity Job Postings So You Don’t Have To |
Let me be direct with you. I spent days going through 100 different cybersecurity AI job postings manually, by hand because I wanted to actually know what employers are asking for. Not what career blogs say they're asking for. Not guesswork. Real job descriptions from real companies like Walmart, Google, Uber, CrowdStrike, Crowe, Boeing, NVIDIA, and more.
Not every job has "AI" in the title. Some of them are just regular security engineer roles where AI is so deeply woven into the job description that it may as well be the entire job. If the word appeared once, I didn't count it. I'm talking about jobs where AI was central in the responsibilities, the qualifications, the tools, the architecture they expect you to understand.
I organized all 100 into a spreadsheet with the job description, posted salary, estimated salary, and the direct URL. You can go through the whole thing yourself:
View the Full 100-Job Spreadsheet
What follows is my deep analysis of everything inside those listings the patterns, the surprises, the gaps, and the roadmap I'd give to anyone trying to stay employable in this field over the next five years.
About the Data:
Salary figures are drawn directly from the Indeed postings. Where no salary was listed, an estimate was generated based on job description context. The average $172,000 reflects the mean across all roles with available compensation data. The lowest posted salary was approximately $56,000 (for a teaching role). The highest was $390,000 for a Senior Director of Engineering position.
Salary Reality: This Is Not an Entry-Level Field Anymore
The average salary floor across these 100 jobs? $145,000. The ceiling? $215,000. The absolute top? A Senior Director of Engineering role at $390,000 per year.
That's not a fluke. That's the market telling you something.
AI professionals in the United States command a median salary of $160,000, with senior AI engineers earning between $200,000 and $225,000 annually. When you layer AI competency on top of an already-demanding security discipline, you get the salary numbers you're seeing in this dataset. Companies at Walmart, Uber, Box, CrowdStrike, and Goldman Sachs are not posting $200K+ roles because they have budget to burn. They're doing it because they cannot find enough people who can do this work.
Job postings seeking AI skills increased at 81% and cybersecurity skills at 33% in 2024–2025, while the talent shortage index in both areas remained extremely high. The supply side has not caught up. It won't catch up for years. That imbalance is why these salaries exist.
"The average salary across these 100 cybersecurity AI jobs is $172,000. The most I've ever personally made in a corporate security role was $180,000. The fact that $172K is the average here not the ceiling tells you exactly where this field is going."
— Josh, Cyber Range
Programming Languages: Python Is Not Optional
Python came in first. By a lot. Not second-place close it was roughly twice as mentioned as the number two language.
| Rank | Language | Relative Frequency | Key Context in Listings |
|---|---|---|---|
| 1 | Python | 🔥🔥🔥🔥🔥 | ML pipelines, LLM APIs, automation, scripting, security tooling |
| 2 | Java | 🔥🔥 | Backend services, distributed systems, enterprise platforms |
| 3 | Go (Golang) | 🔥🔥 | High-performance security services, cloud infrastructure |
| 4 | JavaScript / TypeScript | 🔥 | Full-stack AI application development |
| 5 | PowerShell / Bash | 🔥 | Security automation, endpoint management, scripting |
| 6 | C / C++ | 🔥 | Low-level security research, firmware, embedded systems |
If you don't code yet, start with Python. Not because it's the only language it isn't but because it appears in practically every AI security role from the entry-level analyst position all the way up to the Distinguished Engineer role at CVS Health that pays $334,750. Python is the connective tissue between security engineering and machine learning. You need it.
Cloud Platforms: Azure Won, But You Need All Three
This one surprised a lot of people. Including me, honestly.
Azure came in at number one. AWS was number two. GCP third. Microsoft's heavy enterprise presence especially through Microsoft Sentinel, Defender, Entra ID, and the Azure OpenAI Service has made Azure the default platform for security-first organizations. Many of the largest enterprise security stacks in this dataset (Boeing, Crowe, Deloitte, ServiceNow, Moody's) were explicitly Azure-centric.
| Cloud Platform | Rank | Why It Dominates These Listings |
|---|---|---|
| Microsoft Azure | #1 | Sentinel (SIEM), Defender Suite, Azure OpenAI, Entra ID, AZ-500/SC-200 certifications |
| AWS | #2 | Bedrock (LLM platform), Lambda, Security Hub, broad enterprise adoption |
| Google Cloud (GCP) | #3 | Chronicle (SecOps), Vertex AI, BigQuery, SecOps integration |
Don't pick one and ignore the others. The most competitive candidates know all three at least at a conceptual level. But if you're building your primary depth somewhere, Azure is where the security-specific tooling is most mature for enterprise environments right now.
AI and ML Skills: What Employers Are Actually Requiring
This is the core of the analysis. Let me give you the numbers, then I'll unpack what they actually mean.
| AI/ML Skill Area | % of Jobs Mentioning It | Trend |
|---|---|---|
| Machine Learning Fundamentals | 57% | Established |
| AI Governance / Ethics / Frameworks | 50% | Rising Fast |
| Agentic AI / AI Agents | 40% | Rising Fast |
| LLMs (Large Language Models) | 30% | Established |
| Generative AI | 28% | Established |
| RAG (Retrieval-Augmented Generation) | 23% | Rising Fast |
| Adversarial ML | ~20% | Emerging |
| LLM Security / Prompt Injection | 45% | Rising Fast |
Machine Learning at 57% The Baseline Has Shifted
Machine learning is no longer a specialty. It's a baseline expectation for cybersecurity AI roles. That doesn't mean you need to be a data scientist. But you need to understand how ML models work, what their failure modes look like, how they can be attacked, and how to use them to solve security problems.
The specific frameworks mentioned most often: PyTorch, TensorFlow, scikit-learn (sklearn), and the OpenAI API. If you want a quick win, learn sklearn. It's approachable, well-documented, and used across threat classification, anomaly detection, and risk scoring use cases.
AI Governance at 50% The Biggest Skill Gap Nobody Is Talking About
Half of these 100 jobs mentioned AI governance. Half.
That number should stop you in your tracks. AI governance the discipline of creating rules, policies, and oversight mechanisms that ensure AI systems operate ethically, fairly, and in compliance with regulations is not a soft skill. It's becoming a hard requirement for senior security roles at regulated organizations.
Frameworks you should know:
- NIST AI Risk Management Framework (NIST AI RMF): The US government standard. Widely referenced across banking, healthcare, and defense roles in this dataset.
- EU AI Act: Increasingly relevant for any role at a global company. Financial penalties for non-compliance are significant.
- ISO/IEC 42001: The international AI management system standard. Referenced explicitly in senior architect and CISO-adjacent roles.
- OWASP Top 10 for LLMs: The most practical, hands-on guide to the specific vulnerabilities that exist in large language model applications.
- MITRE ATLAS: The adversarial threat landscape for AI systems. The AI equivalent of MITRE ATT&CK.
Agentic AI at 40% This Overtook Generative AI
This was the biggest trend shift in the data. Agentic AI autonomous AI agents that can take actions, call tools, make decisions, and execute workflows with minimal human intervention appeared in 40% of listings. Generative AI? Only 28%.
The market has moved past the "chat with an AI" phase. Companies now want engineers who can build AI agents that do real work. Security operations centers are building agentic threat hunters. Vulnerability management teams are deploying AI agents that can triage, investigate, and even remediate findings. The Walmart "Distinguished Defense Engineer" role literally asks for experience architecting MCP (Model Context Protocol) servers to expose security telemetry to LLM-powered agents.
If you don't know what an AI agent is, here's the short version: it's an LLM that has been given tools and allowed to act autonomously to complete a goal. LangChain, LangGraph, AutoGen, and the Model Context Protocol (MCP) are the main frameworks being referenced in these job listings.
RAG at 23% And Why You Should Practice It Now
RAG stands for Retrieval-Augmented Generation. It's a technique where instead of relying purely on a model's training data, you give it access to a specific knowledge base your organization's threat intelligence, your internal runbooks, your vulnerability logs and it generates answers based on that context.
The clearest consumer-facing example is Google's NotebookLM. You dump your documents in, ask a question, and the system retrieves the relevant content and synthesizes an answer. Enterprise RAG is the same idea at a production scale with security, access controls, and integration into real workflows.
"Understanding RAG and knowing how to implement an enterprise RAG solution will make you genuinely competitive for roles at companies like Boeing, Uber, CVS Health, and CrowdStrike because those are the exact architectures these companies are building right now."
— Observed trend across 23 of the 100 job listings
Security-Specific Requirements: Every Domain Is Affected
One of the things I want to push back on is the idea that AI integration only matters for one or two security domains. The data says otherwise.
| Security Domain | AI Integration Level | Example Roles in Dataset |
|---|---|---|
| Identity & Access Management (IAM) | High (29%) | API Security IAM Engineer, IAM Program Manager |
| Incident Response / SOC | High | Cyber Incident Response Engineer, Cybersecurity Analyst |
| Vulnerability Management | High | Risk Manager – VM, Kubernetes & Container Security Engineer |
| Threat Intelligence | High | AI-Driven Threat Intelligence Analyst, GenAI Threat Intel Analyst |
| Cloud Security | Very High | Staff Security Engineer (GCP/OCI), Azure AI Security Manager |
| GRC / Governance | High | AI Security Principal, Director GRC, OCI GPU Cloud Engineer |
| AppSec / DevSecOps | High | Senior Application Security Architect, Principal Engineer |
| Red Team / Purple Team | Emerging | Senior Incident Response Engineer (Purple Team) |
IAM appeared most often as a named domain, but that's partly a keyword artifact. When you look at the actual content of the listings, incident response, SOC operations, vulnerability management, and threat intelligence all carry equivalent AI integration expectations. Pick any domain you're genuinely interested in. It's going to have AI woven into it. You don't have to love all of it you just have to be competent in the AI layer relevant to your chosen specialty.
Frameworks and Standards That Appear Most Often
- NIST (CSF, 800-53, AI RMF): The most mentioned framework across all 100 jobs, by a wide margin.
- OWASP (Top 10, LLM Top 10): Now appears in two flavors: classic web security and AI-specific.
- GDPR: Especially for roles with European exposure or data privacy responsibilities.
- Zero Trust: Referenced as an architectural principle, not just a buzzword, in roles at Uber, Box, Google, and AT&T.
- PCI-DSS: Finance-adjacent roles consistently require it.
- MITRE ATT&CK / MITRE ATLAS: ATT&CK for traditional threats, ATLAS specifically for AI threats.
Security Tools Most Referenced
| Tool / Platform | Category | Why It Keeps Appearing |
|---|---|---|
| Microsoft Sentinel | SIEM | Dominant enterprise SIEM, AI-enhanced with Copilot for Security |
| Splunk | SIEM | Legacy but still pervasive, especially in large enterprises |
| CrowdStrike Falcon | EDR / XDR | Market leader in endpoint detection and response |
| Microsoft Defender Suite | EDR / XDR | Deep Azure integration, referenced in almost every Azure-focused role |
| Tenable / Qualys / Nessus | Vulnerability Management | Standard VM tooling, frequently alongside AI-enhanced workflows |
| AWS Bedrock | LLM Platform | Primary platform for banking and financial sector AI experiments |
| LangChain / LangGraph | AI Agent Framework | Most referenced framework for building agentic security workflows |
| Google Chronicle | SecOps / SIEM | Growing adoption in large enterprises, especially for AI-driven detection |
Top Certifications: CISSP Is Still the Crown
No surprises here, but the distribution tells an important story.
| Certification | Frequency | What This Tells You |
|---|---|---|
| CISSP | 24% of jobs | Non-negotiable for senior roles. Get it eventually. |
| CISM | High | Management-track complement to CISSP |
| CCSP | High | Cloud security, especially relevant for cloud-heavy roles |
| GIAC (GCIH, GPEN, GEVA, etc.) | High | Hands-on technical credibility |
| Azure (AZ-500, SC-200, SC-300, AI-900) | High | Role-specific, fast to obtain, directly relevant |
| CompTIA Security+ | Present | Entry baseline necessary but not sufficient |
| CRISC / CISA | Present | GRC-track roles, auditors, risk managers |
| OSCP / CEH | Present | Offensive security, penetration testing roles |
If you want to work in cybersecurity with heavy AI integration just get CISSP. It appears in nearly a quarter of these jobs. Not because it teaches you AI, but because it signals a level of baseline security competency that these employers require before they'll even consider the AI layer. Treat it as the foundation, not the destination.
The Azure certifications (AZ-500 for security engineering, SC-200 for security operations, AI-900 for AI fundamentals) are increasingly valuable because they map directly to tools you'll use day one. They're faster to obtain than CISSP and pair with it well.
Seniority Distribution: Only 7% Entry-Level
This number deserves its own section.
Seven percent. Out of 100 jobs, only 7 could reasonably be called entry-level. And even some of those had qualifications that most people would consider mid-level (three to five years of specific experience, specific certifications, etc.).
Cybersecurity is already hard to break into. It requires computing fundamentals, then general IT knowledge, then security fundamentals, then domain specialization. AI is now a fifth layer on top of that stack. The barrier to entry is going up. That's the honest truth.
The good news? If you're reading this now and you start building toward it, you have time. The market hasn't fully flipped yet. The employers building these teams need people who are growing in this direction, not who already have five years of AI security experience. Use the time you have.
Education Requirements: The Bar Is Rising Here Too
| Education Requirement | % of Jobs |
|---|---|
| Bachelor's degree required | 59% |
| Master's degree preferred | 39% |
| Equivalent experience accepted | 34% |
More than a third of these jobs will accept equivalent experience in lieu of a degree. That's meaningful. It means certifications plus a real portfolio projects you've built, threat hunts you've done, AI agents you've deployed can substitute for a formal degree at many of these employers.
The 39% who prefer a Master's are mostly in the more senior, research-oriented, or finance/healthcare-regulated roles. If you're aiming for something like Distinguished Engineer at CVS Health or AI Security Researcher at Carnegie Mellon's SEI, a master's or PhD is probably worth considering. For the vast majority of roles in this dataset, a bachelor's plus strong certifications plus demonstrable hands-on experience is sufficient.
What the AI Security Stack Actually Looks Like
Looking across the 100 job descriptions, a coherent picture of what employers call the "AI security stack" emerged. These four combinations appear together in about 40% of the listings:
- Python: The programming layer for ML, automation, and LLM API integration
- Machine Learning Fundamentals: Understanding how models work, how they fail, and how to attack and defend them
- LLM Security: Specific knowledge of prompt injection, jailbreaks, context poisoning, data leakage, and model extraction
- Cloud Experience: At minimum, Azure or AWS proficiency with security-specific services
- AI Governance: Framework literacy (NIST AI RMF, OWASP LLM Top 10, MITRE ATLAS)
This combination not each element in isolation is what the market is actually pricing at $145K–$215K average. Any one of these alone doesn't get you there. The stack does.
Key Insight: Prompt Engineering Has Been Superseded
Eighteen months ago, "prompt engineering" was showing up everywhere as a hot skill. It's now noticeably absent as a standalone requirement in senior roles. What replaced it? Adversarial ML (attacking and defending AI models) and RAG architecture. The market has matured past "how do I write good prompts" and moved to "how do I attack and secure the systems that run on top of LLMs."
Prompt engineering still matters you need to understand it to do the more advanced work. But if you're marketing yourself as a "prompt engineer" without the security and architecture layers on top, the senior roles won't find you compelling.
Notable Jobs From the Dataset: What Real Employers Are Actually Saying
Crowe (AI Security Engineer I Senior Staff) | $74,100 – $147,800
This one stood out. Crowe wants someone who can do adversarial ML attacks, RAG manipulation assessments, and prompt injection simulations not as a researcher, but as a production security engineer. They want CISSP, Azure certifications (AZ-500, AI-102), and experience with zero-trust architecture for CI/CD pipelines for AI systems. It's a wide scope at a salary that's on the lower end of this dataset, which tells you they're hiring someone they plan to grow.
Uber (Staff Security Engineer) | $232,000 – $258,000
Uber's listing is almost a manifesto for where cloud security is going. They want to build "self-healing" Cloud Security Posture Management CSPM that uses GenAI and multi-agent orchestration to automatically analyze, prioritize, and remediate exploitable risks at scale. The explicit skills: RAG pipelines, LangChain, AutoGen, GCP and OCI cloud security expertise, and the ability to implement "LLM-as-a-Judge" frameworks. This is agentic security operations, and it's happening at production scale right now.
Boeing (Senior Cybersecurity Third-Party Risk Analyst) | $128,700 – $181,500
Boeing is doing something interesting: they're building agentic AI for third-party risk management. Automated evidence triage, document ingestion, risk-scoring agents. The job explicitly asks for experience designing, training, or integrating agentic AI components including LLM orchestration, RAG, and agent frameworks for what is traditionally a GRC role. This is the clearest example in the dataset of AI permeating a domain (TPRM) where most people wouldn't expect it.
CrowdStrike (AIDR SE Specialist) | $135,000 – $205,000
Following their acquisition of Pangea, CrowdStrike built an AI Detection and Response product. They want pre-sales engineers who deeply understand prompt injection, sensitive information disclosure, model tampering, supply chain risks in AI systems, and the OWASP Top 10 for LLMs. This is a sales engineering role requiring genuine AI security depth not a superficial familiarity.
OpenAI (Security Engineer, Application Security) | $260,000 – $385,000
The highest-paying role in the AppSec category. OpenAI wants someone who can perform security assessments, develop security tools, do threat modeling, and conduct penetration testing all with deep awareness of how LLMs introduce new attack vectors. This is the cutting edge of the field. If you want to work here in three years, start building now.
Emerging vs. Established: The Two Lists You Should Know
Rising / Emerging (Build These Now)
- AI Agents and Agentic AI: 40% frequency, LangGraph, AutoGen, MCP
- RAG Architecture: 23% frequency, vector databases, enterprise knowledge integration
- NIST AI RMF: Referenced across regulated industries, boardroom-level concern
- Adversarial ML: Attacking and defending AI models, MITRE ATLAS
- Model Context Protocol (MCP): Referenced in multiple cutting-edge roles at Walmart, Google, Amazon, and Moody's
Established / Stable (You Still Need These)
- Python: Non-negotiable, universally required
- CISSP: Still the senior credential benchmark
- NIST / OWASP: The baseline frameworks, including LLM-specific variants
- Splunk / Microsoft Sentinel: SIEM proficiency, even as they get AI-augmented
- Identity and Access Management: IAM fundamentals transcend every AI transition
- TensorFlow / PyTorch: ML framework literacy, especially for roles touching model security
The Career Roadmap: Four Phases From Zero to AI Security
Based on what the 100 job descriptions collectively require, here's the most logical progression for someone trying to enter or advance in cybersecurity AI. This isn't theoretical it's derived from the actual qualification requirements in these listings.
Phase 1: Foundations (3–6 Months)
Learn Python fundamentals, Linux basics, and earn CompTIA Security+. The Google Cybersecurity Professional Certificate covers Python and Linux and gets you a discount on Security+. This is your entry ticket the minimum required to be taken seriously by the technical screening filters most of these employers use.
Phase 2: Cloud and ML Basics (3–6 Months)
Pick Azure or AWS and go through the security fundamentals track. Learn introductory machine learning with scikit-learn (sklearn). Study the NIST Cybersecurity Framework and the NIST AI RMF. At this stage, you're building the conceptual foundation that will make everything in Phases 3 and 4 make sense.
Phase 3: LLM and AI Security (2–4 Months)
Study LLM concepts how they work, how they fail, and how attackers exploit them. Read the OWASP Top 10 for LLMs. Understand prompt injection, jailbreaking, context poisoning, and data leakage. Learn the basics of CI/CD and how AI systems get deployed. Explore one RAG implementation hands-on using a tool like LangChain or LlamaIndex.
Phase 4: Portfolio and Application (Ongoing)
Build something demonstrable. An AI-powered log analyzer. A simple threat intelligence RAG system. A prompt injection detection tool. Something you can show, explain, and talk through in an interview. Then start applying for mid-level roles not entry-level ones. Use the skills and the project work to make the case that you're ready to grow into what these companies are building.
Five Strategic Takeaways From the Full Dataset
- The Power Combination: Python + LLM security knowledge + cloud experience + AI governance appears in 40% of these listings. This isn't coincidence it's the market telling you exactly what to build toward.
- The Biggest Gap Is AI Governance: 50% of jobs require it. Very few people have it. If you invest six weeks in NIST AI RMF, OWASP LLM Top 10, and ISO 42001 comprehension, you will immediately differentiate yourself from most of the applicant pool for senior roles.
- Agentic AI Overtook Generative AI: Agents at 40%. GenAI at 28%. The market has moved. If you're still thinking about cybersecurity AI as "use ChatGPT to write security reports," you're behind where employers are today.
- Adversarial ML and RAG Replaced Prompt Engineering: The early "prompt engineering" wave has matured into deeper technical disciplines. Employers want people who can attack models, not just query them.
- AI Will Not Be a Separate Job Category Forever: Right now we see "AI Security Engineer" and "AI Threat Intelligence Analyst" in job titles. In five years, those will just be "Security Engineer" and "Threat Intelligence Analyst." AI will be assumed. The titles are a temporary artifact of a transition period. Don't wait for the transition to finish get positioned now, while the explicit skills still create competitive differentiation.
My Honest Final Thoughts
The field is harder to enter than it was five years ago. That's just true. Anyone telling you otherwise is selling something.
But here's what's also true: the average salary across these 100 jobs is $172,000. The companies doing the hiring are Walmart, Google, Uber, NVIDIA, OpenAI, Goldman Sachs, Boeing, and CrowdStrike. These are not marginal roles at marginal companies. This is where the world's most significant security work is being done.
The higher bar is also the higher reward. If you were already going to put in the work to get into cybersecurity and if you're reading a 35-minute analysis piece on job market data, I think you were then adding the AI layer on top is the same kind of discipline applied to a different set of skills.
The smartest career move is not chasing a job title because it sounds advanced. It is understanding which roles are gaining budget, which ones are becoming more strategic, and where salary growth is accelerating because employers cannot afford weak execution.
Start with Python. Get Security+. Pick a cloud. Build one thing with an LLM. Read the NIST AI RMF. The rest follows from there.
And go look at the actual data yourself browse the 100 jobs, read the descriptions, and see what resonates with the direction you want to go.
📊 Browse All 100 Jobs in the Spreadsheet Watch the Full Video Breakdown
Sources and References:
Job data sourced from Indeed.com, manually collected and analyzed. Spreadsheet available at: docs.google.com/spreadsheets/d/1bYqXaimIvGWi4URfZbYoUeZ96exherHXb8R7JCk5W_Q
Salary benchmarks supplemented by Rise AI Talent Salary Report 2026; ACSMI Cybersecurity Job Market Trends 2026–2027; Robert Walters US AI/Cybersecurity Talent Report 2025; Auxis IT Salary Trends 2026; Practical DevSecOps Emerging AI Security Roles 2026.
Framework references: NIST AI RMF (nvlpubs.nist.gov), OWASP Top 10 for LLMs (owasp.org), MITRE ATLAS (atlas.mitre.org), ISO/IEC 42001.
