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Trust Center

Trust Center

Where your security review starts.

Lawnise sits inside Enterprise Public AI TRiSM — the procurement-grade framework for governing what public AI says about your institution. The platform is built for buyers whose security review starts with data handling and ends with audit readiness.

Methodology Status

Methodology in preparation.

Methodology in preparation

The Lawnise Trust Index methodology is being prepared for public release. Until then, the working draft, its review history, and the evidence-anchored adjudication approach are documented here for procurement review.

The methodology describes how Lawnise scans public AI engines, deduplicates observations against context snapshots, and adjudicates findings against evidence anchored to public, dated source material. It documents PII redaction with Malaysian recognizers, the separation between customer configuration and benchmark reference data, and the right-to-reply path for institutions named in published findings.

The draft is reviewed on a fixed cadence and revised against platform reality. Material revisions are versioned, dated, and attributed. The published methodology will carry the same audit metadata as this draft entry.

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Data Handling

Customer data handling is documented.

Lawnise separates customer-side configuration from benchmark reference data, and publishes its data-handling posture for procurement review. Each principle below points to the methodology document where it is defined.

P-01

PII redaction with Malaysian recognizers.

The methodology documents how customer-supplied prompts and observed AI responses are processed for personally identifiable information before review. The recognizer set is documented and revised against jurisdictional updates.

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P-02

Customer configuration vs. benchmark reference data.

The methodology documents how customer-side configuration — the prompts, brand terms, and review policy a customer authors — is held separately from the benchmark reference data used to score AI answers. The separation is versioned and reviewed.

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P-03

Evidence-anchored adjudication.

Every published finding is bound to a dated, public source document or a customer-supplied policy artifact. The evidence-to-claim ledger preserves the AI response, the extracted claim, the anchoring evidence, and the adjudication outcome as a replayable record.

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P-04

Data-handling posture is documented.

The data-handling posture — what is collected, where it is processed, how long it is retained, and who reviews it — is published here and revised on the same cadence as the methodology document. Revisions carry version, date, and reviewer attribution.

This page

Subprocessors

Procurement-grade transparency.

The full subprocessor list is public — required by GDPR, MY PDPA, and SG PDPA. The Trust Center publishes data-handling, retention, and incident-response posture. See the full list before you sign.

See the subprocessor list →

GDPR · MY PDPA · SG PDPA

Retention Modes

Three retention modes for source documents.

Source documents collected during a scan are retained against one of three lifecycle modes. The mode is set per workspace in the customer configuration and recorded against every observation written to the ledger.

Mode · Full

Full retention

Under this mode the source document is retained for the lifecycle of the customer agreement, supporting replayable audit access on every published finding.

LifecycleRetained for the lifecycle of the customer agreement.

Mode · Ephemeral

Ephemeral retention

Under this mode the source document is retained only during the active scan window and discarded once adjudication completes. The retention posture is documented per workspace.

Active scan windowRetained only during the active scan window.

Mode · Immediate

Immediate discard

Under this mode the source document is not retained after scan completion. The retention posture is documented per workspace.

On scan completionDiscarded immediately after scan completion.

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