Answers You Can Share Internally
Procurement-ready FAQs, privacy glossary and practical guides — designed so you can forward them to security, legal and IT without extra context.
Product basics
What Sylure is (and isn't), how intake works and what you get out the other side.
Today Sylure is uploads-first. You bring exports, archives, case folders and logs as ZIP bundles so intake is controlled, auditable and easy to scope. We map exposures back to concrete assets (files/paths) so remediation is practical.
Common exports (Excel/JSON), documents, emails and logs. Intake validates size and structure before scanning, then produces an inventory of assets and where personal data appears.
Review workflows support ignore/suppress decisions at the exposure level with traceability. The goal is consistent governance: the same decision should produce the same outcome across views and exports.
37 formats including documents (PDF, DOCX, ODT, PPTX, RTF), spreadsheets (XLSX, XLS, ODS, CSV), data (JSON, YAML, XML, SQL), email (EML, MSG, MBOX), text (TXT, HTML, MD) and config files. Intake validates structure before scanning.
ZIP bundles up to 3GB compressed (20GB uncompressed). Up to 100,000 eligible files processed per bundle. For higher volumes, contact us about Enterprise plans.
Evidence safety
Masking, minimisation, access controls and retention patterns for review workflows.
Sylure is designed for minimisation. Where possible, identities are matched using hash-first indexing and masked evidence views. Raw values may exist inside the customer-provided source files you upload; presentation layers can be masked by default to reduce exposure during triage.
Yes. You can use role-based access and masked-by-default review views for routine triage, only revealing sensitive values when absolutely necessary for resolution.
You control when data is deleted. Raw upload bundles auto-expire after 30 days. Derived data (exposures, identity hits, assets) persists until you delete it. Deleted on demand via the product.
HMAC-SHA256 with workspace-scoped keys. Identifiers are normalised before hashing so matching works across formatting variations without storing raw values as plain database fields.
DSAR Discovery Tool
Review-first discovery bundles, consistent decisions and draft outputs designed for human validation.
A discovery bundle is a structured export that collects matched evidence (with an audit trail), reviewer decisions and supporting context into a package that is easier to validate internally and share with counsel or the request handler.
Reviewers can mark exposures as irrelevant, suppressed, or ignored with optional notes. Those decisions are tracked so subsequent exports remain consistent and defensible.
Yes. DSAR exports include a masked/unmasked toggle. By default, exports mask personal data values for safe handling. When authorised reviewers need full values for response preparation, they can toggle to unmasked mode. The choice is logged for audit traceability.
Sylure can generate draft summaries and discovery-facing language as a starting point. Draft outputs should always be reviewed by a human and are not legal advice.
Upload processing and initial scan results appear within minutes. Review and bundle preparation depend on volume and complexity, but the structured workflow significantly reduces turnaround compared to manual folder-and-spreadsheet approaches.
Pricing & procurement
Commercial terms and practical expectations for volume, AI outputs and onboarding.
Analytics AI summaries are auto-generated per upload at no extra quota cost. DSAR AI drafts consume a monthly workspace quota. All outputs are designed for review workflows and should be validated before sharing externally.
For higher volumes we can increase allowances under an Enterprise plan. The goal is predictable operations: clear limits, clear guardrails and transparent commercial terms.
No. All plans are month-to-month with no commitment. You can cancel at any time.
Yes. A standard Data Processing Agreement is available on request. Enterprise customers can negotiate custom DPA terms.
Next step
Still have questions?
Tell us your environment (data sources, expected volumes and review model). We'll suggest a safe, practical path to production.