Give the model a bounded job
A language model can help explain a sequence of events, compare it with a known pattern or identify what evidence is missing. These are useful research questions for a security product. They are different from granting a model permission to stop a customer’s checkout. A fluent explanation alone is not sufficient authority for that action.
Blokk currently uses deterministic rules for its deployed monitoring and account policy. Advisory infrastructure exists for future evaluated use, but production model inference is off and baseline learning is not implemented. The design discussed here is a development direction, not a claim that an AI classifier is already reviewing merchant traffic.
Make every conclusion traceable to an observation
Provide a bounded set of relevant observations and require advice to reference them. If the record contains repeated cart reports, the model may describe that pattern. It should not invent a purchase, a payment failure or the identity of the person behind the account. Unknown facts need an explicit unresolved state.
Validate the response in ordinary application code. Reject references to events that were not provided, unsupported labels and malformed output. An explanation that passes a format check can still be wrong, so compare its conclusions with a reviewed set of examples. Keep the policy’s own eligibility checks independent of the model’s prose.
Treat visitor-controlled content as data
OWASP describes prompt injection as a risk when outside content influences a language model’s behaviour. A security analyst may encounter content supplied by the very client being assessed. Page text, paths or other submitted fields must not become instructions to change a policy, disclose information or run a tool.
Keep the analysis narrowly scoped and minimise the material sent. Blokk’s advisory design does not give the model checkout-publication credentials or permission to create an account restriction. Validation and limited privileges reduce the consequences of a bad response; they do not make a provider’s judgement infallible.
Evaluate the cases that could mislead the model
A useful evaluation includes permitted automation, insufficient evidence, missing collection, stale activity, contradictions and manipulated text. Include examples where the appropriate answer is uncertainty. Re-running a favourable handful of scripted bots cannot establish how the system will treat ordinary shoppers or unfamiliar automation.
Keep a held-out set of reviewed examples separate from the examples used to develop the prompt. Record the provider and model version, inputs, validated output and review result under the applicable retention policy. Evaluate changes before rollout, including timeouts and unavailable providers, so optional advice does not become a hidden dependency for the core workflow.
Keep the authority for action understandable
A merchant should be able to tell whether a card describes a recorded signal, an advisory interpretation, a policy match or a confirmed account state. Combining them into one score hides the important transitions. In Blokk, the implemented checkout policy still requires its specific recent signed-account cart evidence, store eligibility and explicit approval.
Future training is another separate decision. Keeping operational evidence does not by itself authorise using that evidence to train a model. Establish the purpose, permissions, minimisation, retention and deletion process before creating a training corpus. The useful objective is advice that helps explain a decision while the customer’s approved rule remains accountable and testable.