Building an Organizational AI Red Team Program
Step-by-step guide to establishing, staffing, and operating an AI red team program within an organization.
Overview
Building an AI red team is fundamentally different from adding a new capability to an existing cybersecurity program. AI systems introduce failure modes that traditional security testing does not cover: adversarial prompt manipulation, safety alignment bypasses, emergent behaviors in agentic systems, and data poisoning risks that may not manifest until months after deployment. An effective AI red team program must be designed from the ground up to address these unique challenges while integrating with existing security governance structures.
This article provides a practical blueprint for building an AI red team program, drawing on published experience from organizations like Microsoft, Google, and Anthropic, as well as frameworks from NIST and MITRE. Whether you are a security leader tasked with standing up a new team, a CISO assessing organizational readiness, or a practitioner advocating for a formal program, you will find actionable guidance for each stage of program development.
Assessing Organizational Readiness
Understanding Your AI Footprint
Before designing a red team program, you need a clear picture of your organization's AI deployment landscape. This assessment should answer several key questions:
What AI systems are in production or development? Catalog all AI-powered applications, including third-party AI services consumed via API (such as OpenAI, Anthropic, or Google Vertex AI), internally developed models, and AI features embedded in purchased software. Many organizations discover that their AI footprint is substantially larger than leadership realizes, particularly when third-party AI integrations are included.
What data do these systems access? AI systems that process customer personal data, financial information, or health records carry higher risk profiles than those working with public data. Systems with access to internal knowledge bases or tool-calling capabilities that can modify data or trigger actions represent an elevated risk tier.
What decisions do these systems influence? AI systems involved in hiring, lending, content moderation, medical diagnosis, or security operations require more rigorous testing than those used for internal productivity enhancement. The EU AI Act's risk classification framework provides a useful starting taxonomy for prioritizing systems by impact.
What is the current security testing coverage? Identify what security testing currently exists for AI systems. Many organizations find that their application security teams test the web application layer but do not test the AI-specific attack surface. This gap is precisely what an AI red team program addresses.
Maturity Model for AI Red Teaming
Organizations typically progress through four maturity levels when building AI red teaming capabilities:
Level 1 — Ad Hoc: AI security testing is performed informally, usually by curious security engineers or ML engineers who self-organize around specific concerns. Testing is inconsistent, undocumented, and not integrated with release processes. Most organizations deploying AI are at this level.
Level 2 — Defined: A formal AI red team exists with defined scope, processes, and staffing. The team executes structured engagements against priority AI systems with documented methodology. Findings are tracked and remediated through established workflows. This is the target state for most organizations beginning to build a program.
Level 3 — Managed: AI red teaming is integrated into the software development lifecycle. Automated testing runs in CI/CD pipelines, complemented by manual red team engagements for high-risk systems. Metrics are tracked and reported to leadership. The team has established relationships with AI development teams and influences architecture decisions.
Level 4 — Optimizing: The AI red team conducts original research, develops novel testing techniques, and contributes to industry knowledge. Testing coverage is comprehensive and risk-prioritized. The team proactively identifies emerging threat categories and adapts its methodology accordingly. Few organizations have reached this level.
Your initial program design should target Level 2 with a roadmap to Level 3 within 18-24 months.
Program Design
Organizational Placement
Where the AI red team sits in the organizational structure significantly affects its effectiveness and independence.
Reporting to the CISO is the most common placement. This provides natural alignment with existing security governance, access to established vulnerability management and incident response processes, and organizational credibility. The risk is that the team may be perceived as an adversarial force by AI development teams, requiring deliberate relationship-building.
Reporting to a Chief AI Officer or VP of AI places the team closer to the systems it tests and can facilitate tighter integration with development processes. However, this creates a potential independence conflict, as the team may face pressure to soften findings that could delay product launches.
Dual reporting to both security and AI leadership can balance these tensions but requires clear escalation paths and agreement on how conflicts are resolved. This model works best in organizations with mature governance structures.
Independent reporting to a risk committee or the CTO provides maximum independence but can create coordination challenges. This model is most appropriate for organizations in heavily regulated industries where regulatory bodies may scrutinize the independence of testing functions.
Regardless of placement, the program must have explicit executive sponsorship and a clear mandate that authorizes adversarial testing of AI systems. Without this, the team will face repeated scope and access challenges that undermine effectiveness.
Scope Definition
Define the program's scope across three dimensions:
System scope: Which AI systems fall within the red team's purview? Start with the highest-risk systems identified in your AI footprint assessment. A common initial scope includes all customer-facing AI applications and any AI system that processes sensitive data or has tool-calling capabilities. Expand scope as the team matures.
Attack scope: What categories of testing will the team perform? The MITRE ATLAS framework provides a comprehensive taxonomy. A reasonable initial scope includes prompt injection (direct and indirect), safety alignment bypass, data extraction and privacy violations, abuse scenario testing, and basic adversarial robustness testing. More advanced techniques like model extraction, data poisoning, and supply chain attacks can be added as the team's capability grows.
Temporal scope: Will the team perform point-in-time assessments, continuous monitoring, or both? Most programs start with point-in-time engagements against priority systems and evolve toward continuous testing as automation capabilities mature.
Team Composition
The ideal AI red team combines several complementary skill profiles:
AI Security Specialists form the core of the team. These practitioners combine knowledge of ML/AI systems with adversarial testing skills. They execute the majority of hands-on testing work. For a new program, plan for 2-4 specialists depending on the volume of AI systems in scope.
ML Engineers provide deep technical knowledge of model architectures, training pipelines, and inference infrastructure. At least one team member should have production ML engineering experience, as this knowledge is essential for understanding how attacks translate to real-world impact. This role can be filled by an ML engineer who develops security skills or a security engineer with deep ML knowledge.
Automation Engineers build and maintain the team's testing infrastructure, including automated scanning pipelines, custom harnesses, and integration with CI/CD systems. This role becomes critical as the program matures toward Level 3. Initially, this work can be distributed among the AI security specialists.
A Program Lead manages engagements, coordinates with stakeholders, handles reporting and metrics, and represents the team in governance discussions. This role requires a combination of technical credibility and organizational leadership. For small teams (under 5 people), this is often a working lead who also performs testing.
A reasonable starting team for a medium to large organization is 3-5 people: a working lead, 2-3 AI security specialists, and an ML engineer. This team can handle 8-12 structured engagements per year alongside continuous monitoring of the highest-risk systems.
Hiring Strategy
Hiring for AI red teaming is challenging because the talent pool is small and the skill combination is unusual. Effective hiring strategies include:
Grow from within: Identify security engineers with ML interest or ML engineers with security interest in your existing workforce. Invest in upskilling these individuals through training, conference attendance, and dedicated learning time. This is often the fastest path to a functional team because these individuals already understand your organization's systems and culture.
Hire for adjacent skills and train the gap: Hire experienced penetration testers and provide ML training, or hire ML engineers and provide security training. The six-month ramp-up time is a worthwhile investment given the scarcity of practitioners with both skill sets.
Engage contractors for immediate capability: While building your permanent team, engage specialized consulting firms to conduct initial assessments and help establish methodology. This provides immediate coverage and gives your developing team access to experienced practitioners they can learn from.
Evaluate practical skills, not credentials: The field is too new for certifications to be reliable signals. Use practical assessments in your hiring process: give candidates a deliberately vulnerable AI system and evaluate their approach to testing it. Assess both their technical findings and their documentation quality.
Operational Processes
Engagement Workflow
Establish a standard engagement workflow that ensures consistency and quality across all assessments:
Phase 1 — Intake and Scoping (1-2 weeks): Receive a testing request or identify a system for proactive assessment. Meet with the system owner to understand the system's architecture, purpose, data flows, and known risk areas. Define the engagement scope, timeline, rules of engagement, and success criteria. Document all agreements in a formal engagement charter.
Phase 2 — Reconnaissance (1 week): Gather information about the target system through documentation review, architecture analysis, and passive observation. Identify the system's attack surface, including all input vectors, tool integrations, data sources, and output channels. Map the system against the MITRE ATLAS framework to identify applicable technique categories.
Phase 3 — Active Testing (2-4 weeks): Execute testing against the defined scope using a combination of manual techniques and automated tools. Document all findings as they are discovered, including evidence (screenshots, API logs, full prompt/response sequences), reproduction steps, and preliminary severity assessment. Maintain a testing log that records all activities for accountability and reproducibility.
Phase 4 — Analysis and Reporting (1-2 weeks): Analyze findings in the context of the system's threat model and operational environment. Assess severity using a consistent framework (such as the one described in the reporting section below). Write the engagement report including executive summary, detailed findings, and remediation recommendations. Conduct internal peer review before delivery.
Phase 5 — Delivery and Remediation Support (1-2 weeks): Present findings to the system owner and relevant stakeholders. Provide remediation guidance and answer technical questions. Schedule follow-up verification testing for critical and high-severity findings. Close the engagement and update the program's findings database.
Evidence Management
Rigorous evidence management is essential for credibility and reproducibility. Establish standards for:
Evidence capture: Every finding must include the complete input/output sequence that demonstrates the vulnerability, timestamps, system version or model version tested, and the tester's identity. For API-based testing, capture full HTTP request/response pairs. For interactive testing, use screen recording in addition to text logs.
Evidence storage: Store evidence in a centralized, access-controlled repository. Evidence may contain sensitive data (extracted PII, bypassed safety outputs, etc.) and must be handled accordingly. Establish retention policies that balance the need for historical analysis with data minimization requirements.
Chain of custody: For findings that may lead to regulatory reporting or legal action, maintain a chain of custody log that records who accessed evidence and when. This is particularly important for findings that demonstrate violations of data protection regulations.
Integration with Development Workflows
For the program to drive meaningful security improvement, its findings must integrate with the workflows that development teams use daily.
Issue tracking integration: Create findings as tickets in the development team's issue tracker (Jira, Linear, GitHub Issues, etc.) with consistent labeling and severity classification. This ensures findings enter the team's existing prioritization and sprint planning processes rather than sitting in a separate report that never gets actioned.
CI/CD integration: As the program matures, integrate automated AI security testing into CI/CD pipelines. Tools like Garak and Promptfoo can be run as pipeline stages that gate deployment. Start with non-blocking informational scans and progress to blocking gates for critical finding categories as the process matures and false positive rates are understood.
Architecture review participation: The AI red team should participate in architecture reviews for new AI systems and significant changes to existing ones. This enables threat modeling before deployment rather than only finding vulnerabilities after the system is live.
Building Executive Support
Making the Business Case
Executive support requires articulating AI red teaming's value in business terms:
Risk reduction: Quantify the risk of AI system failures using scenarios relevant to your industry. A chatbot that leaks customer data, an AI agent that can be manipulated to take unauthorized actions, or a content moderation system that can be bypassed all carry quantifiable business impact in terms of regulatory fines, litigation costs, reputational damage, and remediation expenses.
Regulatory compliance: Map red teaming activities directly to regulatory requirements. The EU AI Act requires providers of high-risk AI systems to conduct adversarial testing. The NIST AI RMF's MAP and MEASURE functions explicitly call for red teaming. Financial regulators (OCC, Fed, FCA) increasingly expect adversarial testing of AI systems used in regulated activities. Frame the program as a compliance investment, not just a security expense.
Competitive advantage: Organizations that can demonstrate rigorous AI security testing have an advantage in enterprise sales, particularly in regulated industries. An established AI red team program is increasingly a factor in vendor security assessments and due diligence processes.
Metrics and Reporting
Report program effectiveness through metrics that resonate with executive audiences:
Coverage metrics: Percentage of production AI systems that have been tested in the last 12 months. Percentage of new AI systems tested before deployment. These metrics are easy to understand and directly communicate the program's reach.
Finding metrics: Number and severity distribution of findings. Mean time to remediate by severity level. Finding recurrence rate (how often the same vulnerability class appears in new systems). These metrics demonstrate both the program's productivity and the organization's security improvement over time.
Efficiency metrics: Average engagement duration by system complexity. Cost per engagement. Ratio of automated to manual testing. These metrics demonstrate operational maturity and help justify staffing and tooling investments.
Risk metrics: Number of critical findings identified before production deployment versus after. Estimated risk exposure reduced through remediation. These metrics connect the program's work directly to business risk language that executives understand.
Maintaining Momentum
Programs often face budget pressure after the initial novelty wears off. Strategies for sustaining support include:
Regular cadence of executive reporting: Quarterly briefings that highlight significant findings (appropriately sanitized), program metrics trends, and emerging threat landscape developments. Keep these brief and focused on business impact.
Demonstrated responsiveness: When a new AI vulnerability class is published or an AI security incident occurs at another organization, proactively assess your organization's exposure and report to leadership. This demonstrates the team's value as an early warning system.
Continuous scope expansion: As the team demonstrates value, advocate for expanding scope to cover additional AI systems, new attack categories, or adjacent areas like AI supply chain security. Growing scope with demonstrated results is the most effective way to justify growing investment.
Scaling the Program
From Team to Function
As the program matures, it typically evolves from a single team to a function with multiple dimensions:
Specialized sub-teams may form around system types (LLM applications, computer vision, agentic systems), engagement types (pre-release testing, continuous monitoring, incident response), or customer segments (internal systems, customer-facing products, third-party assessments).
Center of excellence model: The AI red team becomes a center of excellence that maintains standards, develops methodology, builds tooling, and provides training, while embedded security engineers in AI product teams handle routine testing using the methodology and tools the center provides.
Federated model: In large organizations with multiple AI development groups, a federated model places red team practitioners within each development group while maintaining central coordination for methodology, tooling, and metrics. This maximizes integration with development teams while preserving consistency.
Building Institutional Knowledge
Long-term program success depends on capturing and systematizing knowledge:
Findings database: Maintain a searchable database of all findings across engagements. Categorize by vulnerability class, system type, and technique used. This becomes an invaluable resource for training new team members, identifying patterns across systems, and demonstrating program impact over time.
Playbooks: Document testing procedures for common AI system architectures and vulnerability classes. Playbooks enable consistent testing quality even as the team grows and ensure that knowledge is not lost when individuals leave.
Training program: Develop a structured onboarding program for new team members that combines self-study, mentored engagements, and practical exercises. A well-designed onboarding program reduces the time to productivity for new hires and helps scale the team without diluting quality.
Common Pitfalls
Organizational Pitfalls
Launching without executive sponsorship: A red team that lacks clear organizational mandate will face constant friction over access, scope, and remediation follow-through. Secure explicit sponsorship before launching.
Adversarial relationship with development teams: The red team must be perceived as a partner that helps improve security, not as an antagonist that creates work and delays launches. Invest in relationship-building, provide remediation guidance (not just findings), and celebrate security improvements alongside the development teams that implement them.
Overcommitting on scope: It is better to thoroughly test five systems than to superficially scan fifty. Start with a manageable scope and expand as the team proves its capability and capacity.
Technical Pitfalls
Relying entirely on automated tools: Automated tools are essential for coverage and efficiency, but they miss novel vulnerabilities, complex attack chains, and context-dependent issues. Maintain a balance between automated and manual testing.
Ignoring the application layer: AI-specific attacks are the team's distinctive competency, but do not ignore the traditional web application, API, and infrastructure vulnerabilities in AI systems. A SQL injection in the AI system's database is just as impactful as a prompt injection in the model.
Testing in isolation from deployment context: A vulnerability that is exploitable in a lab may be mitigated by production controls, and vice versa. Test in environments that accurately represent production deployment, including guardrails, rate limits, and monitoring.
References
- NIST AI Risk Management Framework (AI RMF 1.0), January 2023. https://www.nist.gov/artificial-intelligence/ai-risk-management-framework — Federal framework for managing AI risks, including organizational testing requirements.
- MITRE ATLAS (Adversarial Threat Landscape for AI Systems). https://atlas.mitre.org/ — Comprehensive knowledge base of adversarial techniques against AI systems.
- Microsoft AI Red Team. "Planning Red Teaming for Large Language Models (LLMs) and Their Applications," 2024. https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/red-teaming — Microsoft's published guidance on AI red team program design.
- OWASP Top 10 for LLM Applications, 2025 Edition. https://owasp.org/www-project-top-10-for-large-language-model-applications/ — Industry standard ranking of LLM application security risks.