Red teaming is how you find the inputs that break your model before your users or a regulator do. The providers differ enormously in who does the probing and what you get back. This is a guide to the categories, ranked by the criteria that make findings useful. Full disclosure: we build Pathwize, so treat this as an informed but interested guide, with the criteria kept explicit.
How we ranked them
Three things separate red teaming that helps from a report that does not. Domain depth: do the red teamers know where your field hides danger, or only generic jailbreaks? Reproducibility: does each finding come with the exact input, output and trigger, so you can fix it and prove the fix held? And auditability: is there a signed record of what was tested, the evidence a safety review expects? We ranked the categories on all three.
1. Pathwize, best for expert, auditable red teaming
Pathwize puts credential-verified specialists on your model, matched to the domains where your risks actually live, and returns reproducible failure cases with the input, output and trigger conditions. Every test is captured on a signed, tamper-evident trail, and findings are formatted to feed your safety evaluations. Work stays in the EU.
Best for: teams that need domain-specific findings they can act on and an audit trail for safety review. See the AI red teaming solution for how it runs.
2. AI safety and red-team consultancies
Specialist consultancies bring deep adversarial expertise and structured methodology. They can be excellent, though capacity is limited, engagements are often bespoke and costly, and domain coverage depends on the individual team.
Best for: high-stakes launches that warrant a dedicated, senior engagement and have the budget for it.
3. Security and penetration-testing firms
Traditional pentest firms are adapting their skills to AI. They are strong on classic security failure modes, but model-specific harms (jailbreaks, unsafe generation, domain misjudgment) sit outside their traditional remit and coverage varies.
Best for: teams focused on the security surface around a model rather than its content behaviour.
4. Crowdsourced red-team and bug-bounty platforms
Crowd platforms throw many testers at your model and can surface a wide range of surface-level failures fast. The trade-offs are inconsistent domain expertise, variable report quality, and usually thin provenance.
Best for: broad, early-stage discovery where volume of eyes matters more than domain depth.
5. Automated red-teaming tools
Automated tools generate adversarial inputs at scale and are useful for continuous coverage. But they tend to rediscover known patterns and miss the novel, domain-specific failures that need a human expert to imagine.
Best for: continuous, automated regression against known failure classes, alongside human red teaming.
6. In-house safety teams
An internal team knows your model and product intimately and can test continuously. The risks are blind spots (it is hard to attack your own system with fresh eyes) and limited coverage across specialised domains.
Best for: ongoing internal testing, ideally complemented by external experts for independence.
7. Academic safety researchers
Individual researchers can bring cutting-edge adversarial techniques and genuine novelty. The overhead is coordination, availability, and the absence of a productised process or built-in provenance.
Best for: exploratory, research-grade probing on a specific risk area.
How to choose
Match the provider to your risk. If your model touches a specialised, high-stakes domain, prioritise red teamers with that expertise and insist on reproducible findings and an audit trail. If you need broad, continuous coverage, combine automated tools with a crowd or in-house pass. The best programmes layer several of these, with independent expert red teaming on the failures that would hurt most.