AI Red Team Build Criteria: In-House vs. Outsourced Services Comparison and Selection Guide

Whether to choose in-house or outsourced AI red teaming depends on the frequency of testing, the number of AI systems under evaluation, and the availability of in-house specialized personnel. This article is intended for security leaders, CTOs, and information security officers at AI-adopting companies, and compares the cost structures and operational burdens of in-house, outsourced, and hybrid approaches, while explaining selection criteria and implementation steps by organizational size. By the end of this article, you should have a decision framework suited to your organization's maturity level and be ready to begin drafting a concrete implementation plan. Note that this article presents general decision-making criteria for building an AI red team structure; for individual legal or contractual decisions, consulting a specialist is recommended.

In-house red teaming is a structure that accumulates deep knowledge of an organization's specific systems and sensitive data while conducting continuous evaluation, whereas outsourcing is a structure in which a specialized vendor conducts short, intensive evaluations from an independent standpoint. The two differ fundamentally in how access permissions are designed and how sensitive information is handled. A hybrid model combines both approaches, linking internal knowledge with external expertise through continuous feedback.

An in-house red team is a structure in which a specialized group, independent from the development organization, takes on the role of attacker and designs and executes practical attack scenarios targeting the model, application, data, and infrastructure as a whole. The reason independence is maintained is that when a development team evaluates its own system, oversights and lax assessments tend to occur. The results of the evaluation are compiled not only into remediation proposals but also into reports that contribute to improving the monitoring system.

Organizational design presupposes a multidisciplinary composition. By combining machine learning engineers, offensive security researchers, subject matter experts in the relevant business domain, and ethics/governance personnel, the team can examine not only technical vulnerabilities but also risks rooted in business context. For example, in the case of an AI system in the medical field, while verifying technical attack methods such as prompt injection, a person with clinical knowledge also needs to assess the risk of generating incorrect medical information.

Microsoft's AI Red Team was established in 2018 and has evaluated multiple generative AI products under a continuous structure. What characterizes the role of in-house red teams is that, rather than conducting one-off assessments, they maintain a dedicated team within the organization, enabling continuous evaluation that keeps pace with system changes and new feature additions.

Outsourced red team services come in multiple forms, and the choice depends on the purpose and frequency of testing.

Short-term specialized assessments are suited for testing specific systems or pre-release verification, with experts intensively executing attack scenarios over a defined period. Periodic assessments are contracts conducted annually or on a semi-continuous basis, suitable for organizations that repeatedly test in line with their system change cycles. Managed services are closer to continuous outsourcing, in which an external vendor continues to handle day-to-day monitoring and evaluation. There are also approaches that combine these with bounty or crowdsourcing mechanisms to incorporate reports from an unspecified number of researchers, as well as approaches that combine automated tools with hands-on expert response.

Regardless of the form, the external provider clearly specifies the access level for testing. At the time of contracting, one of three options is defined: black box, in which no internal structure is disclosed; gray box, in which some design information is shared; or white box, in which source code and model internals are disclosed. The common practice is to prepare a dedicated environment with clearly documented reporting formats and handling of sensitive data. The choice of access level depends on the type of attack to be verified and the scope of information that can be disclosed internally.

Organizations that want to run continuous evaluation cycles across many systems and accumulate know-how internally will find in-house red teaming advantageous, while organizations that want to bring in specialized expertise over a short period, or that cannot allocate dedicated personnel internally, will find outsourcing advantageous. The differences between the two can be summarized in six points: initial implementation cost, speed of ramping up expertise, handling of sensitive data, accumulation of in-house know-how, independence of evaluation, and ongoing operational cost.

Each row of the comparison table cannot serve as a decision factor on its own and must be evaluated in combination with the number of systems under test and their update frequency. For example, even if an organization prioritizes confidentiality management and chooses in-house red teaming, if it cannot develop specialized personnel, the quality of evaluation will suffer, ultimately requiring consideration of a hybrid model that combines in-house efforts with outsourcing.

The initial setup cost of building in-house capability breaks down into three components: hiring, training, and tool preparation. Talent with experience in both offensive security and ML is limited in the hiring market, and even after hiring, a training period is required for staff to understand the structure of the company's own systems. When introducing evaluation tools such as PyRIT, the work of adjusting configurations so they can be applied to the company's own models and AI systems is also included in the initial cost.

Outsourcing can be started with a one-time, contract-based cost, reducing the burden of fixed costs in the early stage. For short-term specialized evaluations, verification can be completed with a single scoped contract, saving the time required for hiring and training. However, projects requiring highly specialized red teaming or large-scale verification can involve high-cost contracts depending on company size.

This gap narrows as the number of systems to be verified increases. For a one-off evaluation of a single system, the one-time cost of outsourcing is advantageous, but when multiple systems are verified in parallel, the initial investment in in-house capability is distributed as a per-system cost, so the total cost comparison must take into account both the number of target systems and the frequency of verification.

When estimating initial costs, it is essential to calculate hiring costs, personnel costs during the training period, and tool implementation costs separately, and compare them against outsourcing contract quotes covering the same scope of verification. Comparing quotes with different scopes leads to incorrect conclusions about which option is cheaper. Since outsourcing costs also vary depending on the access level (black-box, gray-box, white-box), a practical point of caution when obtaining quotes is to align the conditions, including testing depth, before making comparisons.

In securing talent for an in-house red team, the greatest constraint is that the market offers only a limited pool of people with experience in both offensive security and ML. If someone with experience in only one of the two is hired, gaps emerge: they may be able to design adversarial inputs but unable to analyze the model's internal behavior, or they may be able to analyze the model but unable to construct practical attack scenarios. Microsoft's AI Red Team, since its founding in 2018, has taken several years to cultivate a dedicated team, requiring a considerable period to build up internal experience spanning both ML and security.

The burden of talent development continues even after hiring. Because a multidisciplinary composition is a prerequisite, hiring offensive security researchers alone is not enough—domain experts and ethics/governance personnel must also be secured separately, or the business context of the system under review cannot be properly evaluated. Hiring one person does not complete the team; the need to assemble personnel for each role is a burden that is easily overlooked when drafting a hiring plan.

In the hiring plan, it is effective to first identify the types of attack scenarios the company wants to verify, define the corresponding roles, and then begin recruiting. If hiring takes place before role definitions are settled, the scope of verification tends to skew toward the strengths of existing members, leaving unverified risks unaddressed. Since the quality of verification is not stable during the development period, one option is to proceed by cultivating internal talent while temporarily receiving support from external experts. When taking this approach, including the method of knowledge transfer in the contract terms in advance will smooth the later transition to in-house operation.

The structure of ongoing operational costs reflects a difference in whether in-house or outsourced approaches place more weight on fixed or variable costs. With in-house operations, the personnel costs of a dedicated team arise as a fixed cost every month, and this cost does not change even if the number of systems to be verified decreases. With outsourcing, the cost is variable, arising with each contract renewal, allowing the scale of the contract to be adjusted according to the desired timing and scope of verification.

The NIST AI RMF recommends treating AI risk management as a continuous process, premised on an operation in which verification, monitoring, and reassessment are repeated regularly. If this continuity is to be ensured in-house, the dedicated team's operations must be maintained even during periods when system changes are minimal, resulting in a lack of flexibility. With outsourcing, adjustments are possible—such as expanding the contract during periods of frequent change and reducing the evaluation frequency during periods of fewer changes.

On the other hand, adjusting variable costs takes time for contract negotiation. When urgent vulnerability response or verification just before a new feature release is needed, if the outsourcing partner's schedule is full, same-day response is difficult, and the flexibility to begin verification within the same day—as an in-house team could—is lost. Which to prioritize, ongoing cost or responsiveness, becomes a key consideration in operational design.

Whether an in-house approach is suitable is determined by a combination of two factors: the frequency of verification cycles and the number of target systems, and whether the company can maintain a multidisciplinary team internally. Even if only one of these conditions is met, in-house operation will not function if the organization cannot withstand the burden of continuous operation.

The frequency of verification and the number of target systems are the most concrete factors distinguishing in-house from outsourced approaches. The more critical an AI system is, the more frequent verification it requires, and an operation that performs additional evaluation after major system changes, in addition to at least an annual evaluation, is recommended. If this frequency is to be met through outsourcing, contract negotiation and scope redefinition occur with every change, and for systems with fast release cycles, verification tends to lag behind.

The same holds true as the number of target systems increases. If verifying one system just once a year is sufficient, a single outsourced contract will suffice, but when multiple AI systems are operated in parallel, each with its own independent update cycle, the scheduling and management of verification itself becomes a burden. By placing a dedicated team in-house, the company can control the prioritization of which system to verify and when, as well as the timing of reassessment, on its own, shortening the time from change detection to the start of verification.

As a general direction for decision-making, the greater the number of systems to be verified and the higher the frequency of major changes, the more likely it is that the contract renewal costs and coordination burden of outsourcing will exceed the fixed costs of in-house operation. Conversely, when there are few target systems and the frequency of change is low, repeating one-off outsourced evaluations is more rational. It is important to take stock of the number of target systems and frequency of change within the company and choose a structure based on which configuration it more closely resembles.

The maturity of specialized talent and organizational structure is a separate condition that must be cleared as a prerequisite for choosing in-house implementation, even if the frequency of testing and the number of target systems can be satisfied. If an organization tries to meet only the frequency requirement without having a multidisciplinary structure in place, the result is testing that fails to ensure comprehensive coverage—for example, offensive security techniques can be confirmed but internal model behavior analysis cannot be performed, or the evaluation lacks the necessary domain expertise.

A practical benchmark for measuring maturity is whether the organization has been able to complete fixes in-house for findings discovered in past testing. An organization where the internal team discovers vulnerabilities on its own and can run the entire cycle—from prioritizing fixes through retesting—by itself, rather than only responding after receiving external pointers, can be said to have a functioning in-house structure.

Conversely, an organization that can discover findings but lacks established criteria for fix triage, or where ethics/governance personnel are not involved in the testing process, has not yet reached the organizational maturity needed for in-house implementation, even if the frequency and number of target systems would otherwise suit it. In such cases, it is appropriate to treat the situation as a stage for developing internal talent while receiving support from external experts, and to decide on a full transition to in-house implementation only after the structure is in place.

When the decision is unclear, it is useful to take stock of the most recent testing cycle and check, role by role, who was responsible for each step from discovery through retesting. Once the missing roles become clear, it becomes possible to distinguish the profile of talent that needs to be developed from the scope that should provisionally be entrusted to outside parties, making it possible to concretize a plan for transitioning to in-house implementation.

Whether outsourcing is appropriate depends on the maturity stage of the testing target and whether specialized talent exists in-house. Since building an in-house structure requires both a development period and cost, for organizations that want to get evaluation up and running quickly, or that cannot afford to dedicate full-time personnel, it is a reasonable decision to bring in external specialized expertise over a short period.

For startups and mid-sized companies in the early stages of AI adoption, a practical approach is to first grasp risk through outsourcing before organizing a dedicated in-house team. In the early stage when there are few testing targets, rather than carrying annual personnel costs as a fixed expense, it is easier to measure return on investment by commissioning a one-off evaluation with a scope limited to areas such as externally exposed APIs or the handling of user data.

When narrowing the scope, a practical point is not to treat all systems as testing targets at once, but to prioritize functions that directly affect users. For example, a company offering a customer-facing AI chatbot could first set the testing scope to risks such as prompt injection and sensitive information leakage against that chatbot, and defer experimental internal-use systems to a later stage.

The findings obtained at this stage become the foundation for building out the structure going forward. By recording the patterns of vulnerabilities found in the outsourced evaluation and the effort required to fix them, this becomes material for distinguishing, as the number of target systems grows in the future, which areas should be brought in-house and which should continue to be handled externally under a hybrid model. The approach of transitioning in stages according to organizational maturity is also discussed in Building an Internal Structure for LLM Red Teaming: Organizing and Operating Security Testing.

When securing specialized talent is difficult, the root issue is not so much the absence of candidates in the job market as the structural scarcity of people who possess experience in both offensive security and ML. Under this condition, rushing to build an in-house team carries the risk of hiring people with experience in only one of the two areas, resulting in skewed testing coverage, or of starting operations with unstable evaluation quality while the team is still being trained. Once it becomes clear that the organization cannot assemble the necessary roles internally, switching to external experts or a managed service is a better way to maintain evaluation quality without compromise.

There are also forms in which cloud vendors provide specialized expertise as their own support service, such as Mandiant's AI security consulting offered by Google Cloud, and such external support serves as an alternative means of filling roles that are lacking in-house. When using such services, it is essential to document the patterns of findings obtained after testing and the criteria used for fix triage, and to receive them in a form that can be handed over internally. Rather than simply outsourcing during the period when talent cannot be secured, including a mechanism for knowledge transfer toward a future transition to in-house implementation as a contractual condition is what separates good long-term organizational design from poor design.

A hybrid model is suited to organizations that have a structure capable of running a routine testing cycle in-house, but that cannot fully handle advanced attack techniques or independent third-party evaluation on their own. By having the in-house team handle day-to-day change detection and re-evaluation, while leaving annual in-depth testing and response to new attack techniques to external experts, it is possible to ensure comprehensive testing coverage while keeping fixed costs down.

MITRE recommends a collaborative structure that maintains internal expertise while incorporating a wide range of external attack techniques and independent evaluation, and this corresponds to operations like purple teaming, in which the in-house side (blue) and the external attacking side (red) exchange findings back and forth while repeating testing and remediation. In pursuing this approach, it is necessary to define in advance the boundary of which tests the in-house team handles and at what point the work is handed off to the external party. If the boundary remains ambiguous while using both together, the same scope may end up being tested redundantly by both sides, or conversely, gaps in responsibility may arise.

A useful benchmark for this decision is whether the internal team has been able to complete fix triage on its own for findings it has discovered in the past. If day-to-day handling of findings can be completed in-house, but the organization lacks experience testing for near-zero-day, unknown attack patterns, a hybrid model that entrusts only that portion to external parties matches the actual state of the organization's structure.

When comparing outsourcing services, the standard should not be low price but whether the contract terms correspond to the depth of attack and access level your organization wants to verify. If you compare quotes without aligning evaluation criteria, you risk mistaking a narrow-scope service for a cheaper option.

Each item in the comparison table should not be judged in isolation but evaluated in combination. For example, even if the confidentiality environment is well established, insufficient business domain understanding may cause risks rooted in operational context to be overlooked. As a practical matter before signing a contract, you should align quotes from multiple vendors under the same access level and testing scope, and review sample report formats in advance.

Regardless of whether you choose in-house, outsourced, or hybrid, the process leading up to implementation is common across all approaches.

1. Scope Definition: Document the AI system under test and the range of attack scenarios
2. Determining Access Level and Confidentiality Rules: Specify in the contract or internal policy whether testing will be black-box, gray-box, or white-box, and how sensitive data will be handled
3. Test Design: Create a mapping of test cases to attack techniques, referencing classifications such as the OWASP LLM Top-10 and NIST AI RMF
4. Execution: Carry out testing under safety constraints, with the scope of impact on production systems delineated in advance
5. Reporting and Remediation: Prioritize discovered findings and conduct remediation triage
6. Re-evaluation: After remediation, retest using the same test cases and incorporate the results into an ongoing monitoring framework

This procedure uses the same basic framework whether in-house or outsourced, but in the case of outsourcing, contractual reporting obligations and access-right revocation procedures need to be inserted between steps 2 and 5. In a hybrid model, dividing the scope of verification between the in-house team and the external team at the stage of step 3 helps prevent duplication or gaps in later stages.

Even after deciding on the scale of the team and the contract format, practical questions remain regarding the specific headcount for staffing and the procedures for switching structures midway. Below are representative examples of these points.

The necessary skill sets span five areas: offensive ML (techniques such as adversarial inputs and data extraction), model internals analysis, application and infrastructure attack techniques, domain expertise related to the business area under test, and ethics/governance. Headcount should be considered according to the organization's scale and the number of systems under test. As a rough guideline, a practical approach is to start with a minimal team in which one person handles multiple of these five areas concurrently, then assign dedicated specialists to each role as the number of systems under test increases. The more roles are handled concurrently, the more the scope of testing tends to skew toward each person's area of expertise, making it essential to periodically check for any missing roles.

Switching is possible. However, it requires knowledge transfer as a prerequisite. Before switching, you can maintain continuity of testing by documenting playbooks, test cases, and the traceability of past findings, and by redefining access rights before the transition. If this documentation step is skipped, patterns of vulnerabilities discovered in the past will not be carried over, resulting in the extra effort of re-identifying the same findings.