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Two related ways to keep the platform working for you without manual clicks: scheduled scans and custom detection rules.

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Scheduled scans

Catch drift and new issues as soon as they appear. A schedule re‑runs one or more engines on a cron expression, against either a dataset or every dataset in a project.

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Where to set them up

FromResult
Dataset detail → SchedulerSchedule scoped to this dataset only.
Project detail → SchedulesSchedule that fans out to every dataset in the project in one sweep.
Settings → General → SchedulesWorkspace‑wide view to list, edit, or disable any schedule.

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What you configure

FieldNotes
Cron expressionUI provides presets (hourly, daily 02:00, weekly Monday, …) or type your own.
EnginesOne or many. They run in parallel as worker capacity allows.
Parameter overridesSame controls as a manual scan (sample cap, GPU, advanced parameters).
NotificationsEmail and / or webhook on completion. See Integrations.
Concurrency policyAuto‑cancel if the previous run is still going, or let them stack.

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Where you see them run

Scheduled runs appear in the scan list with a clock icon and the schedule ID in the metadata column. Failures surface in:
  • The Dashboard’s Recent activity widget.
  • The notification channel you configured.
  • The audit trail (with parameter snapshot).
Use Auto‑cancel previous run on schedules that run more often than scans typically finish. Stacked queued scans waste capacity without giving you fresher data.

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Custom rules

Custom rules extend the detection patterns the engines use. Today they’re consumed by Text Analysis PII / secret detection; additional engines pick them up as the feature expands.

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Rule structure

FieldWhat it controls
NameHuman label, e.g. Internal employee ID.
PatternRegex. Validated for catastrophic backtracking before save.
SeverityCRITICAL, MEDIUM, or INFO. Drives the finding’s badge.
ScoreBase score applied when the rule matches (0–1).
ScopeGlobal, per‑project, or per‑dataset.
CategoryFree‑form, typical values: pii, secret, phi, custom.
EnabledToggle without deleting.

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Where to manage them

Settings → Custom Rules. The page lists every rule with its scope, category, severity, and enabled state. From here you can:
  • Create a new rule.
  • Edit an existing rule’s pattern, score, or scope.
  • Archive to hide from active scans without deleting.
  • Delete outright.
  • Bulk import / export rules as JSON. Useful for moving rules between workspaces.

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Testing before you save

Every rule editor has a Test button. Paste sample text; Antidote runs the pattern against it and highlights matches in real time. Use this to confirm you’re not over‑matching before pushing the rule live.
1

Draft the rule

Pick a name. Write the regex.
2

Test it

Paste sample text that should match and text that should not. Tweak until both are correct.
3

Pick a scope

Global for org‑wide patterns. Project or Dataset when the rule only makes sense in context.
4

Save and confirm

The rule becomes live within ~30 seconds (a small cache TTL). Next text scan will use it.

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Common patterns

Most companies have a structured ID format (EMP-123456, CUST-AB1234, INV-2025-…). Capture each as a rule with severity MEDIUM and category pii.
Code names for unreleased products, M&A targets, internal project names. Severity CRITICAL, category secret, score 0.9.
Industry‑specific identifiers (Swiss AHV, US SSN, EU VAT, ICD‑10 codes). Use one rule per format, scope Global, severity CRITICAL.
Catastrophic‑backtracking guard is on by default. A pattern that could blow up at scale is rejected before save with a clear error. If a rule is rejected, simplify or anchor the pattern.