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Tero analyzes your data and surfaces rules. Each rule is a specific statement about what’s wrong: this field duplicates that one, this log is a health check probe, this event fires a million times but represents one problem. Rules are organized by category. Each category is a type of mistake, something concrete that shouldn’t be in your data.

The trust ladder

Tero organizes issues by risk level. Start at the top and work your way down.

Zero risk

You’ll agree immediately: redundant attributes, leftover debug logs, malformed data

Low risk

Straightforward but worth a glance: health checks, bot traffic, unintended tool metadata

Medium risk

Requires discussion with the team: excessive payloads, debug mode left on, sampling decisions
Pick a category, look at examples from your data, approve if you agree. You review categories, not individual rules. Your ruleset grows over time.

Categories

Logs

Some categories affect attributes (fields within a log). Others affect entire events. Attribute-level — the log stays, specific fields get removed: Event-level — the entire log gets dropped or sampled:

Metrics

Enforcement

Once you approve a rule, you choose how it gets enforced: configure your provider directly, push to the edge, open PRs, create tickets. If a problem resurfaces — an engineer re-enables debug logging, a new service emits the same waste pattern — Tero catches it. Problems you fix stay fixed. See enforcement options →