Building an AI Red Team Program
Complete guide to establishing an internal AI red team program including organizational design, staffing, tooling, engagement models, stakeholder management, and maturity progression.
Building an AI red team program is fundamentally different from extending a traditional security red team to cover AI systems. AI systems introduce novel attack surfaces, require specialized skills, and demand new engagement models that account for the probabilistic nature of AI behavior.
Program Design
Organizational Models
| Model | Best For | Pros | Cons |
|---|---|---|---|
| Embedded in Security | Traditional security orgs adding AI capability | Leverages existing security infrastructure | AI may be deprioritized vs. traditional security |
| Embedded in AI/ML Team | AI-first companies | Deep model access, fast feedback loops | May lack adversarial mindset |
| Standalone Team | Large organizations with significant AI deployment | Full independence, dedicated resources | Higher overhead, potential isolation |
| Hybrid: Security + AI Liaison | Medium organizations | Combines security rigor with AI expertise | Requires strong cross-team coordination |
| Outsourced / Augmented | Early-stage AI deployment | Low commitment, external expertise | Less institutional knowledge |
Recommended Structure for a Standalone Team
AI Red Team Lead
├── Prompt Injection / Jailbreak Specialist
├── Agent & Pipeline Security Engineer
├── ML Security Researcher
├── AI Infrastructure Security Engineer
└── Red Team Operations Analyst (tooling, reporting, coordination)
Staffing Plan by Program Size
Minimum Viable Team (3 people)
| Role | Skills Required | Responsibilities |
|---|---|---|
| AI Red Team Lead | 5+ years security, 2+ years AI | Engagement planning, stakeholder management, reporting |
| AI Security Engineer | 3+ years security, ML fundamentals | Prompt injection, agent exploitation, hands-on testing |
| ML Security Researcher | 3+ years ML, security interest | Training pipeline attacks, model evaluation, tool development |
Growth Team (6-8 people)
Add: second AI Security Engineer, AI Infrastructure Security Engineer, Multimodal Specialist, Operations/Tooling Analyst.
Mature Team (10+ people)
Add: Specialization leads for each attack domain, dedicated tool developers, research track for novel attack discovery.
Engagement Models
When to use: Production AI systems with high risk exposure.
Automated and semi-automated testing runs continuously against deployed AI systems, with human review of flagged results.
| Component | Frequency | Automation Level |
|---|---|---|
| Prompt injection regression suite | Daily | Fully automated |
| Jailbreak canary testing | Daily | Automated with human review |
| Safety benchmark evaluation | Weekly | Automated |
| Manual exploration of new features | Per release | Manual |
| Full adversarial assessment | Quarterly | Manual |
When to use: AI systems with moderate risk, or pre-deployment testing.
Structured engagements at regular intervals (monthly, quarterly) covering the full attack taxonomy.
| Phase | Duration | Deliverable |
|---|---|---|
| Scoping & reconnaissance | 2-3 days | Engagement plan, threat model |
| Attack execution | 5-10 days | Raw findings log |
| Analysis & reporting | 3-5 days | Formal report with remediation |
| Remediation verification | 2-3 days | Verification report |
When to use: New model deployments, major feature launches, incident response.
Triggered by specific events rather than a calendar schedule.
| Trigger | Scope | Timeline |
|---|---|---|
| New model deployment | Full assessment of new model | 1-2 weeks before launch |
| New tool/function added to agent | Tool-specific attack testing | 3-5 days |
| Security incident involving AI | Targeted investigation | Immediate |
| Regulatory audit preparation | Compliance-focused assessment | 2-4 weeks before audit |
When to use: Mature programs testing detection and response capabilities.
Full-scope, unannounced adversarial operations simulating real-world attackers.
| Phase | Activity | Duration |
|---|---|---|
| Planning | Develop attack scenarios, set objectives, brief executives only | 1-2 weeks |
| Execution | Unannounced attacks against AI systems, test detection | 2-4 weeks |
| Analysis | Evaluate what was detected, what was missed | 1 week |
| Debrief | Full team debrief with lessons learned | 1-2 days |
Tooling Stack
| Category | Tools | Purpose |
|---|---|---|
| Attack Automation | Garak, PyRIT, promptfoo | Systematic vulnerability scanning |
| Custom Tooling | Internal scripts, custom harnesses | Organization-specific attack scenarios |
| Infrastructure | Burp Suite, mitmproxy, Wireshark | API-level inspection |
| Tracking | Jira, Linear, custom dashboard | Finding management and reporting |
| Evidence | Screenshot tools, logging frameworks | Reproducible evidence capture |
| Collaboration | Shared attack databases, technique libraries | Team knowledge management |
Stakeholder Management
Key Stakeholders
| Stakeholder | What They Need | How to Communicate |
|---|---|---|
| CISO / Security Leadership | Risk-level summaries, program ROI | Monthly dashboard, quarterly reviews |
| AI/ML Engineering | Specific, actionable findings with fix guidance | Technical reports, direct collaboration |
| Product Management | Business impact of findings, launch readiness | Risk-based launch approval framework |
| Legal / Compliance | Regulatory compliance status, documentation | Compliance mapping reports |
| Executive Leadership | High-level risk posture, competitive positioning | Annual program review, incident briefings |
Program Maturity Model
Level 1: Initial (0-6 months)
Ad-hoc testing by security team members with AI interest. Basic prompt injection testing. Manual processes. No dedicated AI red team.
Goal: Demonstrate value and get buy-in for dedicated resources.
Level 2: Developing (6-18 months)
Dedicated team of 2-3 people. Structured engagement methodology. Basic automation (Garak, promptfoo). Regular testing cadence. Simple reporting.
Goal: Establish repeatable processes and build expertise.
Level 3: Defined (18-36 months)
Full team with specializations. Custom tooling. Continuous testing for production systems. Integration with CI/CD pipeline. Standardized metrics and reporting.
Goal: Cover the full AI attack taxonomy with consistent quality.
Level 4: Advanced (36+ months)
Original research capability. Adversarial simulation exercises. Cross-functional AI security influence. Industry contribution (standards, open source). Proactive threat intelligence.
Goal: Lead the industry in AI security practices.
For metrics and ROI measurement, see Metrics, KPIs & Demonstrating ROI. For team composition and career development, see AI Red Teaming Career Guide.
Related Topics
- Metrics, KPIs & Demonstrating ROI -- measuring and justifying program investment
- Team Composition & Skills Matrix -- detailed hiring and skills guidance
- Scaling Red Team Programs -- growing from initial capability to mature program
- AI Red Teaming Career Guide -- individual career paths that feed into team building
- NIST AI RMF & ISO 42001 -- governance frameworks that inform program design
References
- "Building Effective AI Red Teams" - Microsoft (2024) - Organizational guidance on building and operating AI adversarial testing programs
- "AI Risk Management Framework (AI RMF 1.0)" - National Institute of Standards and Technology (2023) - Governance framework that informs red team program structure and mandate
- "CREST Penetration Testing Maturity Model" - CREST International (2024) - Maturity model for security testing programs adaptable to AI red teaming
- "Lessons Learned from Building AI Red Teams" - Anthropic (2024) - Practitioner insights on team design, engagement models, and stakeholder management
What is the most common failure mode for AI red team programs?