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Stop Storing Risk: An Executive's Guide to ROT Data Minimization

In this whitepaper, you’ll learn about:

  • The critical need for ROT data minimization across hybrid multicloud, SaaS, and on-premises data estates.
  • The top reasons minimization programs stall early, and what ROT data looks like.
  • The core arguments for the business case: cost, breach exposure, and AI readiness.
  • The fundamental phases of a program, from metadata scanning and data classification to auto- remediation.

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Redundant, obsolete, and trivial (ROT) data accumulates across on-prem systems, cloud storage, SaaS platforms, and file shares nobody has mapped. ROT data minimization is the practice of finding that data and minimizing it under policy. To put things into perspective, IBM's 2024 Cost of a Data Breach Report found that 35% of breaches involved data in unmanaged sources, costing USD 5.27 million on average. IBM's 2026 edition puts the global average at USD 4.99 million.

However, the exposure does not end at the attack surface. Obsolete and trivial data also degrade the AI projects enterprises fund, because a model treats whatever it ingests as current and reliable. For instance, a superseded policy sitting on an active SharePoint site can surface in an AI assistant's answer months after the legal team replaced it. Manual review doesn't close any of this, which is why most programs die in a million-row spreadsheet.

Here, ROT data minimization, built on automated discovery, context-aware data classification, and federated auto-remediation, can play a critical role in overcoming these challenges.

Download the whitepaper to learn how Securiti, a Veeam company, helps security and governance leaders discover, prioritize, and remediate ROT data at scale.

 

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Stop Storing Risk: An Executive's Guide to ROT Data Minimization

Award-winning technology, built by a proven team, backed by confidence. Learn more.

Frequently Asked Questions (FAQs)

ROT stands for redundant, obsolete and trivial data. It builds up across on-premises systems, cloud storage, SaaS platforms and file shares nobody has mapped, and it adds risk without adding business value.

ROT data minimization means finding redundant, obsolete and trivial data across your data estate and reducing it under policy. It lowers storage costs, shrinks breach exposure and improves AI readiness.

ROT data widens the attack surface. IBM's 2024 Cost of a Data Breach Report found that 35% of breaches involved data in unmanaged sources, at an average cost of USD 5.27 million per breach.

AI models treat whatever they ingest as current and reliable. Obsolete or trivial data can make AI assistants give outdated answers, such as citing a policy that has already been replaced.

Securiti, a Veeam company, uses automated discovery, context-aware data classification and federated auto-remediation. Security and governance leaders can find, prioritize and remediate ROT data at scale without reviewing it by hand.

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