What is ROT data, and why does it matter?
ROT is redundant, obsolete, and trivial data, and removing it lowers cost and risk together. Duplicates, files nobody has touched in years, and trivial content add storage cost, widen the attack surface, and feed stale material into AI. BigID finds it alongside out-of-compliance data, such as data with expired consent or data held past its retention period, and takes it through one disposition workflow. The wider program is covered under unstructured data minimization.
How does BigID find redundant and stale data across file shares, databases, and lakes?
It calculates duplication itself, the same way across sources, rather than relying on each platform's own flags. Detection covers exact duplicates and near-duplicates through cluster analysis of similar and derivative data, stale data by creation, last modified, or last access date, archived data, data with expired consent, data subject to specific regulations, and your own custom rules, across file systems, databases, data lakes, and cloud storage.
How does BigID keep retention policies current as regulations change?
Through a library of pre-built regulation policies and automated updates from filerskeepers. The integration brings in 300,000+ retention rules across 220+ countries, and custom regulation definitions plus a query builder designed for business users cover requirements that are specific to your organization, so keeping policy current does not wait on an engineering ticket.
How does BigID keep a hard delete from breaking something downstream?
The referential integrity check runs inside the disposition workflow itself, before anything is purged. It checks structured data before a hard purge fires, so a row another table still depends on stays in place. Where a purge cannot be proven safe, disposition defaults to soft delete: archive to cold storage instead of an outright purge.
What happens to data that is under legal hold?
Legal hold is checked in the same pass, before disposition touches the data. It runs as part of the same guardrail step as referential integrity, so a hold on one custodian does not mean pausing disposition for everyone else in the batch.
Do the people whose data gets investigated find out?
Yes, the moment it moves. A tombstone marker is left behind as soon as flagged data is moved to the sandbox, and a reviewer with RBAC-approved permissions has to attest before anything is disposed of, with the case routable through ServiceNow or Jira like any other ticket.
Can data that was moved or archived be restored?
Yes, restore is part of the workflow. When a hold clears or a reviewer reverses a decision, data that was moved to the sandbox or archived goes back the way it was. A hard delete is reserved for data that review and the legal hold and dependency checks have cleared, which is why those steps come first.
How does data lifecycle management reduce the attack surface and AI risk?
By removing data nobody needs, and with it the data an attacker or an AI system could reach. Less stale and duplicated data means less to exfiltrate, less to expose through a copilot or agent, and less out-of-date content to ground an AI model on. It works alongside data discovery and classification, which finds what is sensitive, and the broader data and AI resilience program.
How does this support our privacy and regulatory obligations?
It enforces the retention and minimization requirements that CCPA, GDPR, and similar regulations set. Policies come from the regulation library, every action is logged against the workflow, and the same evidence supports audits. It pairs with privacy and compliance for the wider program.
How quickly can we see how much ROT we are carrying?
A free data risk assessment sizes it on your real data before you commit to a program. The assessment is agentless and cloud native, so there is nothing to deploy on the sources, and it shows what qualifies for disposition and what is safe to archive or purge first.
