2027 AI Red Teaming Vendor Evaluation Criteria: How to Choose Providers and Tooling

The Problem: A Clean Report Does Not Mean a Secure System

Most companies that buy AI red teaming services end up with a tidy PDF, a list of findings, and a vague sense of reassurance. The harder questions are whether the testing covered the attack paths that matter for your system, whether your own team can reproduce what was found, and whether the same weaknesses would still be caught after you change a prompt, swap a model, or add a new agent tool.

Selection is difficult because "AI red teaming" now covers very different things. Some vendors are security consultancies that mainly run manual adversarial testing. Others sell automated scanners that send thousands of prompts to an endpoint. Some platforms offer both. A good demo can make any of them look impressive. The criteria below are meant to separate one-off projects from testing capability your company can rely on over the long term.

Start from Your Threat Model, Not the Vendor's Attack Catalogue

A common mistake is to compare vendors by how many attack categories they advertise. A long list of jailbreak templates says little about whether those attacks resemble your real risks. A support assistant that answers only from a knowledge base faces a very different threat landscape from an agent that reads email, calls internal APIs, and writes to a ticketing system.

Before talking to any vendor, write a short threat model for the system under test. Describe what the system can see, what it can do, who can interact with it, and what a bad outcome looks like, such as customer records being exposed, unauthorised actions being taken, harmful advice being given, or policy being violated. Then ask each vendor to map its proposed testing to that model. A strong vendor will challenge your scope and suggest tests you had not considered, such as indirect prompt injection through retrieved documents or privilege escalation through tool calls. A weak vendor will simply quote a standard package.

When reviewing a proposal, check whether the coverage is explained against a recognised framework, such as the OWASP guidance for large language model applications. Also ask the vendor what it will not test and why. Clear exclusions are a sign of a disciplined provider.

Judge the Method by Whether Results Can Be Reproduced

Red teaming is only valuable if its results can be repeated. Ask each vendor how it records attacks, which inputs and model versions it used, and how your own staff could replay a given finding. If the answer amounts to "our experts found it during testing," the finding will be hard to verify, hard to fix, and impossible to turn into a regression test.

A strong methodology separates three things: the attack pattern, the specific input that triggered the failure, and the criteria used to judge whether it succeeded. For example, a finding should state that a particular indirect injection in a retrieved PDF caused the assistant to reveal internal system instructions, and it should include the document, the prompt, the model version, and the pass or fail rule. Before signing, request a sample report. If the sample cannot be converted into test cases, the methodology is not yet mature enough for long-term use.

Also ask how the vendor handles model non-determinism. A language model can answer the same prompt differently across runs. A credible method repeats risky probes several times and reports results as rates or ranges, rather than a single pass or fail.

Assess Data Handling and Rules of Engagement

Red teaming means feeding adversarial content into your system, and it often requires testers to access prompts, retrieval indexes, logs, or internal tools. That access is sensitive. Before any work begins, you need a written agreement that states where test data is stored, who can see it, whether it may be used to train the vendor's models, how long it is kept, and whether it will leave your jurisdiction.

Rules of engagement matter just as much. They define which environments may be tested, whether production systems are in scope, how testers should handle real customer data they encounter, and how testing can be halted if it causes operational problems. Experienced vendors usually have standard templates for these questions. If a vendor is vague here, treat that as a risk signal rather than a formality.

If your team is already working through pre-launch security checks for AI pilot projects, this section can be reviewed in the same cycle so that testing access is approved alongside your other security controls.

Separate Tools from Testers, and Decide What You Keep

Many buyers think they are purchasing one thing when they are actually buying two: human expertise and software. The two should be evaluated separately. Human testers are good at discovering new attack paths, especially in agents and multi-step workflows. Automated tools are good at running known attack categories at scale, repeating tests after every change, and producing consistent records. A sound testing programme usually needs both, but the balance depends on your release frequency and system complexity.

For tooling, check whether you can add your own attack cases, customise pass or fail criteria to your business rules, and connect test runs to your build pipeline. Also ask what happens to your library of attack cases if you change vendors. If the cases you build can only be used inside the vendor's platform, you are creating dependency rather than capability.

A practical way to compare is to have two or three vendors run the same small set of probes against an agreed test endpoint, then compare the findings, the quality of the evidence, and the time needed to reproduce each issue. A short, paid pilot often tells you more than any scoring sheet.

Measure Whether Testing Improves the System, and Act This Week

The final criterion is outcomes. Ask vendors how they verify that a fix actually works. Retesting should be written into the contract, not offered as an optional extra. A good programme tracks findings by severity, component, and attack category, and shows whether similar failures reappear after a model or prompt change.

Be cautious with headline metrics. A single "attack success rate" means very little unless the attack set, the judging method, and the target configuration are all disclosed. Apply the same scepticism to any model safety figures a vendor publishes. The approach in how to verify hallucination rate claims before choosing a provider applies here too: insist on your own test set, your own judging rules, and your own repeated runs.

A practical way to start is to build a small internal regression set from your first test round and run it against every new release. Teams that do this can judge future vendors against a stable baseline, which makes comparisons fairer.

This week, you could draft a one-page evaluation sheet with five columns: threat coverage matched to your system, reproducibility of findings, data handling and rules of engagement, ownership of tooling and test assets, and retesting commitments. Use the same sheet to score each shortlisted vendor, and before signing, ask every vendor for a sample report and a sample test case.

If you want to make this kind of structured comparison without building the process from scratch, you can sign up at https://www.duaer.com and use one workflow to organise your threat model, test run records, and vendor scoring sheet. It does not replace the judgement of your security team, but it helps keep evidence consistent across vendors and across versions.

Ready to set up a repeatable evaluation process? Sign up at https://www.duaer.com and try the workflow. Start with a first comparison on one system, then expand the scope gradually.

Summary: when choosing an AI red teaming vendor, the key question is not whose attack list is longest. It is whether the testing matches your real risks, whether it produces reproducible evidence, whether it handles your data responsibly, and whether it leaves you with test assets you can keep running.