The question at hand
Your model validation team just wrapped up internal testing on a new AI system. They've run performance benchmarks, checked for bias, and documented everything for the audit file. Should you ship it, or bring in external red teamers first?
This is a real choice for organizations deploying AI systems: rely on internal validation or engage third-party experts to probe for safety flaws. OpenAI's recent system card for the o3-mini model reports using both external red teaming and Preparedness Framework evaluations alongside internal safety work. This multi-layered approach raises a practical question for model risk teams: when does external testing add enough value to justify the cost and coordination?
The debate isn't about whether safety testing matters. It's about who should do it and when. Internal teams know your systems deeply. External experts bring fresh eyes and adversarial creativity. Both approaches have advocates in the model risk community, each with its tradeoffs.
The case for keeping testing internal
Internal validation teams argue they're better positioned to assess model safety because they understand the deployment context. Your team knows which edge cases matter, which regulatory requirements apply, and which failure modes could cause harm in production.
Internal testing also moves faster. You don't need procurement approvals, NDAs, or weeks of onboarding external experts. When you're validating a model update or responding to a post-deployment issue, speed matters. Your team can iterate quickly, retest after changes, and maintain institutional knowledge about what's already been checked.
Cost control is straightforward with internal testing. You're paying salaries you've already budgeted, not hourly rates for specialized consultants. For organizations running many model validations per year, external testing for every model becomes prohibitively expensive.
Internal teams can also maintain tighter confidentiality. You're not sharing training data, model architectures, or business logic with third parties. For models handling sensitive data or proprietary algorithms, that containment matters.
Finally, internal validation builds organizational capability. Your team improves with each iteration, developing institutional knowledge about failure patterns, building reusable testing infrastructure, and training validators who understand both AI systems and your specific risk appetite.
The case for external red teaming
External red teaming advocates argue that internal teams suffer from systematic blind spots. You can't easily see the flaws in systems you helped build. Confirmation bias is real: internal validators may unconsciously test what they expect to work rather than probing for unexpected failure modes.
External experts bring specialized adversarial creativity. Professional red teamers spend their careers finding novel attack vectors and edge cases. They've seen failure patterns across multiple organizations and can apply techniques your internal team hasn't encountered. That cross-organizational perspective surfaces risks you wouldn't discover through internal testing alone.
Third-party validation also carries more weight with auditors, regulators, and boards. When you tell your board that an external firm with no stake in shipping the model found no critical safety issues, that carries different credibility than internal sign-off. For high-risk AI systems, that independent validation may be necessary for regulatory compliance or customer assurance.
External testing provides a forcing function for documentation quality. When you need to explain your model to outsiders, gaps in your Technical Documentation (Annex IV) or Instructions for Use become obvious. That documentation discipline benefits your internal teams and future audit readiness.
Red teamers can also test scenarios your internal team can't ethically explore. Adversarial Simulation may require attempting harmful outputs or probing security vulnerabilities in ways that internal staff shouldn't do without clear external oversight.
Where practitioners actually land
Most mature model risk programs use both approaches but sequence them strategically. Internal validation comes first for every model. You run standard performance tests, check for obvious bias issues, and validate against your model risk policy requirements. That catches most problems and keeps costs manageable.
External red teaming is reserved for higher-risk deployments. Organizations typically engage third-party experts when:
- The model is classified as high-risk under their internal risk tiering framework
- The system will make decisions affecting individuals' legal status, employment, or access to services
- The deployment involves novel model architectures or use cases where internal teams lack experience
- Regulatory requirements or customer contracts mandate independent validation
- Post-Market Monitoring has flagged unexpected behavior that internal teams can't fully explain
This tiered approach balances cost against risk. You're not paying for external testing on every model, but you're getting independent validation where it matters most.
The Preparedness Framework evaluations mentioned in OpenAI's safety work represent a middle ground: structured internal assessments using externally-developed evaluation criteria. Your internal team conducts the testing but follows a framework designed by experts to ensure comprehensive coverage of safety dimensions. This approach captures some benefits of external expertise without the full cost of third-party engagement.
Our take
External red teaming isn't optional for high-risk AI systems. Internal validation catches most issues, but it can't substitute for independent Adversarial Simulation when deployment failures could cause significant harm.
The real question isn't whether to use external testing, but how to make it efficient. Don't wait until final validation to bring in red teamers. Engage them early in model development for threat modeling and evaluation design. That upfront investment helps your internal team build better testing processes and catch issues before they're expensive to fix.
Document everything external red teamers find, even minor issues. Those findings inform your next internal validation cycle and help you build institutional knowledge about failure patterns. Over time, your internal team should get better at catching the kinds of issues external experts would flag.
Budget for external testing on high-risk models from the start. Treating it as optional or waiting for budget approval creates pressure to skip independent validation when timelines get tight. Build the cost into your model risk management program as a standard control for systems above your risk threshold.
Finally, don't confuse external testing with absolution of internal responsibility. Red teamers find issues you missed, but your validation team still owns the model risk assessment and the decision to deploy. External validation informs that decision; it doesn't make it for you.
