Team Composition & Skills Matrix
Ideal AI red team composition: ML engineers, security researchers, domain experts. Skills matrix template and guidance for building complementary skill sets.
Team Composition & Skills Matrix
An effective AI red team requires a blend of security expertise, ML knowledge, and domain understanding that rarely exists in a single person. This page provides frameworks for building teams with complementary skills and assessing coverage gaps.
Core Role Profiles
| Role | Primary Skills | Brings to the Team | Typical Background |
|---|---|---|---|
| Security Researcher | Offensive security, vulnerability analysis, exploit development | Attack methodology, adversarial thinking, reporting | Pen testing, bug bounty, security consulting |
| ML Engineer | Model architecture, training pipelines, inference optimization | Understanding of model internals, can build custom attacks | ML research, data science, MLOps |
| Prompt Engineer | Prompt design, instruction tuning, LLM behavior analysis | Deep understanding of model steering and guardrail mechanics | NLP research, LLM application development |
| Domain Expert | Industry knowledge (healthcare, finance, legal) | Understands what "harm" means in specific contexts | Industry practitioner, compliance, risk management |
| Tooling Engineer | DevOps, automation, pipeline design | Lab infrastructure, CART, evidence automation | SRE, DevOps, platform engineering |
Skills Matrix
Use this matrix to assess your team's coverage. Rate each member 0-3 (0 = no experience, 1 = basic, 2 = proficient, 3 = expert).
Template
| Skill Area | Analyst A | Analyst B | Analyst C | Coverage |
|---|---|---|---|---|
| Prompt injection techniques | 3 | 2 | 1 | Strong |
| Jailbreak / safety bypass | 2 | 3 | 1 | Strong |
| Model architecture knowledge | 1 | 1 | 3 | Adequate |
| Training pipeline attacks | 0 | 1 | 3 | Adequate |
| Agent/tool exploitation | 2 | 2 | 1 | Adequate |
| RAG/retrieval attacks | 2 | 1 | 2 | Adequate |
| Multimodal attacks | 1 | 0 | 2 | Weak |
| Infrastructure security | 3 | 1 | 0 | Weak |
| Report writing | 2 | 3 | 1 | Strong |
| Client communication | 1 | 3 | 1 | Adequate |
| Automation/tooling | 2 | 1 | 2 | Adequate |
| Compliance/governance | 0 | 1 | 1 | Weak |
Coverage Assessment
| Rating | Definition | Action |
|---|---|---|
| Strong (team max 3, multiple 2+) | Team can handle advanced work independently | Maintain through practice |
| Adequate (at least one 2+) | Team can handle standard work | Cross-train to build depth |
| Weak (no member above 1) | Significant gap -- findings may be missed | Hire, train, or partner |
| None (all 0) | Blind spot -- entire attack surface uncovered | Immediate action required |
Hiring for AI Red Teams
What to Look For
| Quality | Why It Matters | How to Assess |
|---|---|---|
| Adversarial mindset | Can think about how systems fail, not just how they work | Give a system description, ask "how would you break this?" |
| Learning velocity | AI security evolves weekly; must keep pace | Ask about recent techniques learned, self-study habits |
| Communication skills | Findings are worthless if they cannot be explained | Review writing samples, ask them to explain a complex attack to a non-technical audience |
| Intellectual curiosity | Best researchers are driven by curiosity | Ask about side projects, research they read for fun |
| Ethical judgment | Working with powerful attack techniques requires maturity | Scenario-based questions about responsible disclosure |
Interview Process
Resume Screen (10 min)
Look for: security background OR ML background, evidence of self-directed learning, writing samples (blog posts, CTF write-ups, papers).
Technical Phone Screen (45 min)
Questions covering both security fundamentals and ML basics. The candidate does not need to ace both -- you are looking for strength in one and willingness to learn the other.
Example questions:
- "Explain how prompt injection differs from SQL injection at a fundamental level."
- "What happens during the attention mechanism in a transformer?"
- "How would you test whether a chatbot's system prompt is extractable?"
Practical Exercise (2-3 hours, take-home)
Provide access to a deliberately vulnerable LLM application. Ask the candidate to:
- Identify at least 3 vulnerabilities
- Document one finding with reproduction steps
- Write a brief executive summary
Evaluate: technique variety, evidence quality, writing clarity.
Team Interview (60 min)
Panel with 2-3 team members. Focus on collaboration style, communication, and cultural fit. Include a live problem-solving exercise where the candidate talks through their approach to a novel attack scenario.
Sample Job Description Snippet
AI Red Team Analyst
We're looking for someone who breaks AI systems for a living. You'll probe
LLMs, agents, and AI-powered applications for security vulnerabilities --
from prompt injection to training data poisoning to tool abuse.
You bring:
- 2+ years in offensive security, ML engineering, or AI safety research
- Demonstrated ability to find and document vulnerabilities
- Strong written communication (you'll write reports, not just find bugs)
- Comfort with Python and API-based testing
Nice to have:
- Experience with transformer architectures or fine-tuning
- CTF competition experience
- Published security research or blog posts
- Familiarity with AI safety evaluation frameworksCross-Training Plan
Build team resilience by systematically cross-training members:
| Quarter | Security Researcher Learns | ML Engineer Learns |
|---|---|---|
| Q1 | Transformer architecture, tokenization | Offensive security methodology, evidence standards |
| Q2 | Fine-tuning, RLHF, alignment techniques | Prompt injection techniques, jailbreak patterns |
| Q3 | RAG pipelines, embedding spaces | Agent exploitation, tool abuse |
| Q4 | Training data security, model extraction | Report writing, client communication |
Cross-Training Methods
| Method | Time Investment | Effectiveness |
|---|---|---|
| Pair on engagements | 0 extra (built into work) | High -- learning by doing |
| Internal tech talks (30 min/week) | 2 hrs/month | Medium -- builds shared vocabulary |
| CTF participation (team) | 1 day/quarter | High -- competitive, fun, practical |
| Paper reading group | 2 hrs/month | Medium -- keeps team current on research |
| External training budget | 3-5 days/year per person | High -- structured learning |
Related Topics
- Scaling Red Team Programs -- growing the program as a whole
- Building an AI Red Team Program -- organizational placement and mandate
- Specialization Paths -- individual career growth within a team
References
- "Building Effective AI Red Teams" - Microsoft (2024) - Organizational guidance on team composition for AI adversarial testing programs
- "The AI Security Skills Gap" - World Economic Forum (2024) - Analysis of the intersection between cybersecurity and ML expertise in the workforce
- "CREST Certified Simulated Attack Specialist Competency Framework" - CREST International (2024) - Skills taxonomy for offensive security professionals adaptable to AI red teaming
What is the most important quality to hire for in an AI red team candidate?