When OpenAI and Anthropic tested each other's models for misalignment, instruction following, hallucinations, and jailbreaking, they weren't just sharing research. They showcased a validation approach that could change how your team evaluates AI safety.
You're deciding whether to conduct model safety evaluations internally, collaborate with external parties, or use a hybrid approach. This choice impacts the quality of your validation evidence, your audit defensibility, and your ability to spot risks your team might overlook.
The Decision You're Facing
Your model validation program must ensure your AI systems meet safety requirements before and after deployment. The question isn't whether to validate, but who should do it and what access they need.
You have three options:
- Internal evaluation using your own teams
- Cross-organizational collaboration with peers or competitors
- Hybrid approaches combining internal and external validation
Each option provides different evidence, requires different governance, and exposes you to different risks.
Key Factors That Affect Your Choice
Regulatory requirements for independence. SR 11-7 requires effective challenge in model validation, meaning validators can't report to model developers. However, it doesn't specify if "independent" means internal-but-separate or truly external. The EU AI Act demands evidence of testing but doesn't specify who conducts it.
The nature of risks you're testing for. Some failure modes are easier to spot from outside your organization. Your team knows where you've implemented guardrails, which might lead to unconscious bias in testing. External evaluators don't know your defenses, so they probe differently.
Your threat model. If you're mainly concerned about unintentional failures like hallucinations or instruction-following errors, internal testing might be enough. If you're worried about adversarial attacks or jailbreaking, you need evaluators who think like attackers.
Intellectual property and competitive concerns. Collaboration requires sharing model access or evaluation methods. Legal frameworks must protect proprietary information while allowing meaningful testing.
Auditability requirements. Your board, regulators, or customers might demand evidence that validation was done by parties without conflicts of interest. Internal teams can be independent, but external validation offers clearer separation.
Path A: Internal Evaluation (When Your Risk Profile Allows It)
Choose this path when:
- You're in a regulatory environment that accepts internal validation with effective challenge
- Your models aren't used in high-stakes applications where failure could cause severe harm
- You've established separation between development and validation teams
- Your validation team can identify critical issues before deployment
- Intellectual property concerns outweigh the benefits of external perspectives
Implementation requirements: Set up a validation function that reports outside the model development chain. Document your validation methods, including test cases for misalignment and adversarial scenarios. Ensure validators can block deployments.
Your validation team should include members who didn't work on the model being tested. Rotate assignments to prevent bias. Use standardized frameworks like the NIST AI RMF Playbook for consistency.
Limitations: You're testing within your own mental models. Your team shares assumptions about model behavior, which failure modes matter, and what constitutes acceptable risk. You might miss risks obvious to outsiders.
Path B: Cross-Organizational Collaboration (When You Need External Perspective)
Choose this path when:
- You're developing General-Purpose AI Models with systemic risk implications
- Your models will be used in high-risk applications under the EU AI Act
- You need validation evidence for external stakeholders skeptical of internal testing
- You're ready to invest in legal frameworks for information sharing
- The benefits of identifying blind spots outweigh intellectual property concerns
Implementation requirements: Create agreements defining what gets tested, who sees results, and how findings are disclosed. Define your scope based on your risk assessment.
Ensure independence in collaboration. External evaluators shouldn't be financially dependent on your organization or have conflicts that could bias findings. Document evaluation methods and criteria before testing.
You'll need technical controls for model access. Consider API-based evaluation that limits what external parties can extract, or conduct testing in controlled environments where model weights stay secure.
Challenges: Cross-organizational evaluation requires significant coordination. You're aligning timelines, agreeing on metrics, and negotiating what constitutes a "finding" versus expected behavior. Legal teams must draft agreements that protect both parties' interests.
Path C: Hybrid Approach (Layered Validation)
Choose this path when:
- You need both depth (internal knowledge) and breadth (external perspective)
- You're subject to multiple regulatory frameworks with different independence expectations
- Your risk tiering identifies some models that require external validation and others that don't
- You want to validate your internal validation program's effectiveness
Implementation requirements: Conduct internal validation first, then engage external parties for high-risk models or novel capabilities. Use external validation to check your internal program's rigor.
Define clear handoff points. Internal teams complete functional testing and known-risk evaluation. External teams focus on Adversarial Simulation and edge cases.
Treat discrepancies between internal and external findings as gaps. If external evaluators find risks your team missed, update your internal methods to catch similar issues in future models.
Summary Matrix
| Factor | Internal Evaluation | Cross-Organizational | Hybrid Approach |
|---|---|---|---|
| Regulatory acceptance | Accepted with effective challenge | Emerging practice; strong evidence | Best of both |
| IP protection | High | Requires legal framework | Medium (selective sharing) |
| Cost | Lower ongoing cost | Higher coordination cost | Variable by model |
| Blind spot detection | Limited to internal perspective | Strong external perspective | Balanced |
| Speed | Faster execution | Slower (coordination overhead) | Medium |
| Audit defensibility | Good with documentation | Excellent | Excellent |
| Best for | Lower-risk models, established validation teams | High-risk models, systemic risk concerns | Organizations with tiered risk profiles |
The OpenAI-Anthropic collaboration shows that external evaluation can uncover issues internal teams might miss. Your decision should start with your risk assessment: what failure modes could cause the most harm, and who's best positioned to find them before your users do?
If you're deploying models in high-risk applications, external validation isn't optional. It's proof that your internal controls work.
