How to get into AI red teaming?

Why AI Red Teaming Is the Security Career Nobody's Ready For

Every organization racing to deploy AI agents has quietly discovered the same problem: almost nobody can secure them. According to research cited by the World Economic Forum in 2025, only 14% of organizations believe they have the AI security talent they need — a gap that has turned AI red teaming into one of the fastest-rising security careers, with job postings growing more than 25% annually.

The field is young but no longer experimental. Microsoft formed the first dedicated AI Red Team in 2018, and the discipline exploded after 2023 as LLMs went mainstream and the White House's Executive Order on AI put adversarial testing on the national agenda. Since 2021, Microsoft's team alone has red-teamed more than 100 generative AI products, publishing its lessons in a January 2025 paper. Regulation is now cementing demand: the EU AI Act mandates testing for high-risk AI systems, making adversarial testing "a requirement, not just a best practice."

Here is what trips up most newcomers: AI red teaming is not penetration testing with a new label. As practitioners put it, it is "a response to systems that think and act" — agents that plan, call tools, browse, write to memory, and execute changes in the real world. Traditional red teams probe infrastructure; AI red teams probe behavior: hallucinations, prompt injection, data leakage, and misuse. The results are probabilistic, not deterministic, and the attacker mindset shifts accordingly.

What makes this career unusually welcoming is that hiring is skills-first. Demonstrated ability — CTF rankings, published research, open-source contributions — carries more weight than formal certifications, and experience requirements sit notably below traditional senior security roles. Entry-level positions start around $60K–$90K, with the ladder climbing to $180K–$280K+ for senior leads.

The interdisciplinary nature widens the door further. Microsoft's team includes a neuroscientist and a linguist alongside cybersecurity experts, and some employers explicitly seek psychology, acting, or writing backgrounds for unconventional adversarial thinking. If you can think creatively about how systems fail, you already have a foothold.

The risk surface keeps expanding, too. Published research has found that roughly 26% of agent skills carry at least one vulnerability — exactly the class of flaws AI red teamers probe. SkillzBench is building free security scanning and sandbox benchmarking for agent skills to help address this gap from the adoption side, and the red-teaming side needs people just as badly.

For anyone weighing the leap, the essentials are refreshingly concrete:

• Free foundations: Microsoft's AI Red Teaming 101, the OWASP LLM Top 10, and MITRE ATLAS
• Core tooling: PyRIT (Microsoft) and garak (NVIDIA), the two most-cited open-source frameworks
• Proof of skill: AI CTFs, open-source contributions, and published writeups
• Communication: translating findings into business impact, not just attack success rates

The talent gap is real, the entry bar is skills-based, and the field is still young enough that newcomers can shape it. Few security careers have ever opened this wide, this fast.

The Skills That Actually Get You Hired (Not Certifications)

Here's a surprise that should encourage anyone considering this career: AI red teaming hires on demonstrated ability, not paper credentials. According to career research, CTF rankings, published research, and open-source contributions carry more weight than formal certifications — and experience requirements run notably lower than traditional senior security roles.

The core competency stack starts with two free OWASP resources. The OWASP GenAI Red Teaming Guide (published January 2025) covers how to test across four areas: model evaluation, implementation testing, infrastructure assessment, and runtime behavior analysis. The OWASP Top 10 for LLMs defines what to test, and MITRE ATLAS maps adversarial techniques against AI systems. HiddenLayer explicitly recommends the OWASP guide as "a great starting point for new red teamers" in its best-practices guidance.

On the tooling side, learn Python and get hands-on with the two most-cited open-source frameworks:

• PyRIT — Microsoft's Python framework, used heavily in real operations across 100+ red-teamed generative AI products
• Garak — NVIDIA's model vulnerability scanner for probing LLM weaknesses
• Inspect (UK AI Security Institute) and DeepTeam — additional open-source starting points in a landscape with no dominant platform yet

One counterintuitive finding shapes the skill profile: Microsoft's whitepaper on 100 red teaming operations found that "real attackers don't compute gradients, they prompt engineer" — basic techniques often match or beat sophisticated gradient-based attacks. That means creative adversarial thinking matters as much as deep ML math.

This is why the field is deliberately interdisciplinary. Microsoft's AI Red Team includes cybersecurity experts alongside a neuroscientist and a linguist, and job listings cite psychology, acting, or writing backgrounds as assets for unconventional adversarial thinking. Palo Alto Networks similarly emphasizes that teams need "diversity of thought and adversarial creativity" across security, ML, and human factors.

Two often-overlooked skills round out the stack. Report writing separates good red teamers from great ones — translating "73% attack success rate" into "an attacker could extract customer PII from the chatbot" is what gets you hired and promoted. And every finding should tie to a remediation recommendation, since proving something is broken without a fix wastes everyone's time.

The same skills-first logic applies to the agent ecosystem itself: published research found roughly 26% of agent skills carry at least one vulnerability, and SkillzBench is building free security scanning and sandbox benchmarking to help teams evaluate them before deployment. If you're drawn to this work, that risk surface is exactly where AI red teamers earn their keep.

What the Job Really Looks Like: Lessons From 100+ Microsoft Operations

Most people imagine AI red teaming as elite hackers running sophisticated adversarial attacks. Microsoft's own practitioners found the opposite: the techniques that work are often surprisingly simple. Their paper, "Lessons From Red Teaming 100 Generative AI Products," distills what the team learned across 100+ operations since 2021 — and it doubles as a realistic job description for anyone entering the field.

The first lesson is a mindset shift: start from impact, not attack strategy. Rather than asking "what attack can I run?", effective red teamers ask "what could this system do that would actually harm a user or a business?" That requires understanding what the system can do and where it is applied before writing a single prompt. Microsoft's team also stresses that AI red teaming is not safety benchmarking — you must model both malicious adversaries and benign users who trigger failures unintentionally.

The second lesson may surprise aspiring hackers: "real attackers don't compute gradients, they prompt engineer." Microsoft found that basic techniques often work as well as, and sometimes better than, gradient-based attacks. Simple, creative prompt engineering is a core job skill, not a beginner's crutch.

Automation matters too, but in a supporting role. Across 100+ products, fully manual testing became impractical, so the team built PyRIT, Microsoft's open-source Python framework, to scale operations. The lesson for career-changers: automation augments human judgment, it never replaces it. Practitioners consistently note that the adaptive nature of AI threats "demands human ingenuity."

The third lesson is the one that separates careers. According to career research, "report quality is what separates good red teamers from great ones." That means translating findings into business terms — "an attacker could extract customer PII from the chatbot using publicly documented techniques" rather than "we achieved a 73% ASR on prompt injection." Great reports also tie every finding to a remediation recommendation; as one practitioner puts it, proving something can be broken without a method to fix it wastes everyone's time.

A strong operation typically includes:

• Impact-first scoping: understanding the system's capabilities and where it is deployed before attacking
• Simple, creative prompt engineering validated against more sophisticated techniques
• Automated baselines with tools like PyRIT, with human judgment driving the campaign
• Findings documented with sample prompts, responses, and reproducible tooling
• Business-impact framing plus concrete remediation recommendations

These same instincts apply if you're on the other side of the table — deploying AI agents in your business. Research found roughly 26% of third-party agent skills carry at least one vulnerability, exactly the risk class red teamers probe. SkillzBench is building free security scanning and sandbox benchmarking to help you verify skills before wiring them into an agent; the waitlist is open now.

Your Step-by-Step Entry Plan: Feeder Roles, Free Resources, and First Moves

The best part about breaking into AI red teaming? You don't need a $4,000 certification to start — the field is famously skills-first, where CTF rankings and open-source contributions carry more weight than credentials. Here's a practical roadmap that gets you there without wasting money.

Step 1: Learn free before you spend. Start with Microsoft's AI Red Teaming 101, the OWASP LLM Top 10, and the MITRE ATLAS framework — all free. HiddenLayer explicitly recommends the OWASP GenAI Red Teaming Guide as a great starting point for new red teamers. Only after exhausting these should you consider the full certification stack, which runs roughly $3,887–$5,646 over about 18 months according to career-path research.

Step 2: Pick your feeder role. Penetration testing is the most direct path, with a 6–12 month transition once you add MITRE ATLAS, PyRIT, and Garak to your toolkit. AI Security Specialist is the shortest route at roughly 6 months. Security Engineer, ML Engineer, and Trust & Safety roles take longer at 1–2 years.

Step 3: Build a public portfolio. Compete in the AI Village CTF at DEF CON and HackTheBox's AI Red Teaming CTF, contribute to open-source red-teaming tools, and publish writeups. Remember that demonstrated skills beat formal certifications in this hiring market — and Microsoft's own team, which has red-teamed over 100 generative AI products, found that simple prompt-engineering techniques often outperform sophisticated gradient-based attacks, so you can start experimenting immediately.

Step 4: Commit to a daily habit. Budget 1–2 hours per day reading papers (arXiv, NeurIPS) and testing attacks hands-on. Microsoft's lessons from 100+ red team operations are essential reading, especially the principle of starting from impact rather than attack strategy.

Along the way, sharpen these core competencies:

• Adversarial ML fundamentals and the OWASP LLM Top 10
• Tooling: PyRIT (Microsoft) and Garak (NVIDIA)
• Python scripting for automated attack testing
• Report writing — because report quality separates good red teamers from great ones, translating "73% ASR on prompt injection" into "an attacker could extract customer PII"

If you work with AI agents rather than just attacking them, the same adversarial mindset applies to evaluating third-party skills — published research found roughly 26% carry at least one vulnerability. SkillzBench is building free security scanning and sandbox benchmarking for agent skills, so you can join the waitlist to practice that verification discipline while you train.

Practice on Real Targets: Why Agent Skills Are the Perfect Training Ground

The best red teamers don't wait for permission to practice — they attack the AI systems already sitting in front of them. If you're building or working with AI agents, you have a live training range most candidates never think to use.

Here's why that matters. Published research found that roughly 26% of agent skills carry at least one vulnerability — the exact risk class AI red teamers probe every day. Agent skills, the instruction packages that extend tools like Claude Code, Codex, Cursor, and Gemini CLI, are increasingly woven into production workflows. That makes them a realistic, low-stakes place to practice adversarial evaluation.

Microsoft's AI Red Team, which has red-teamed more than 100 generative AI products since 2021, emphasizes starting from impact rather than attack strategy. Your own agent stack gives you that context for free: you know what the skills do, what data they touch, and what a failure would actually cost. Practicing on targets you understand teaches the skill hiring managers actually screen for — demonstrated work over formal certifications.

A practical practice loop looks like this:

• Pick a skill you already use and map its attack surface: what tools it can call, what content it ingests, what it writes to memory.
• Test against the OWASP LLM Top 10 — prompt injection, data leakage, and misuse are the behavior-focused failures that separate AI red teaming from traditional pentesting.
• Verify impact at the application layer, not just the model's output — did the system actually change a record or expose data?
• Write it up in business terms, tying every finding to a remediation recommendation, the way experienced practitioners insist on.

To make this repeatable, SkillzBench is building a free platform to security-scan agent skills, benchmark them in a sandbox with and without the skill attached, and connect vetted skills to your agent through a free MCP server. It's currently in pre-launch with a waitlist open, and every component — the directory, scanning, benchmarks, and alerts — will be free.

Joining the waitlist gets you early access to the scanning and benchmarking pipeline, plus a stream of real, versioned targets to test against. That's the kind of portfolio material that turns curiosity into a career.