Topic starter
18/08/2026 2:54 pm
TL;DR: ROT data left in clouds and SaaS environments inflates storage cost, expands breach impact, and weakens AI quality, while Securiti’s blog argues that discovery, classification, and automated deletion can reduce the blast radius. The governance lesson is that data minimization is not a privacy-only exercise; it is a core control for containment, compliance, and safe AI use.
Editorial analysis by NHI Mgmt Group, based on content published by Securiti: “Shrink The Blast Radius”.
By the numbers:
- Many large organisations waste $10–30M/year on ROT storage alone.
- The global average breach cost hit $4.88M in 2024.
Key questions
Q: What should organisations do when they need to reduce ROT data and limit unnecessary data retention?
A: Organisations should define a data retention and deletion strategy that identifies redundant, obsolete, and trivial data, then removes it on a schedule aligned to business and legal needs.
Q: Why does excess retained data make breaches worse?
A: Because attackers, insiders, and misconfigured workflows can reach more copies of the same information, and containment becomes harder when the estate is full of duplicates and forgotten archives.
Q: What breaks when cloud and SaaS data is not classified before deletion?
A: Teams either delete too little and keep the risk, or delete too much and create legal and operational exposure.
Practitioner guidance
- Map and retire ROT-heavy repositories Inventory cloud, SaaS, backup, and self-managed data stores that contain stale, duplicate, or no-longer-needed content, then set disposal priorities by sensitivity and business value.
- Automate retention and deletion decisions Use policy-driven rules tied to age, activity, sensitivity, and recordkeeping obligations so deletion and archive actions are repeatable and auditable.
- Tie AI ingestion to freshness controls Block obsolete or duplicate material from retrieval and fine-tuning pipelines so AI systems do not learn from or answer from dead content.
Bottom line: ROT data is a governance problem because it keeps old information reachable, searchable, and copyable long after its business purpose has ended.
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18/08/2026 3:37 pm
ROT data minimization is now a control plane issue, not a housekeeping task. Once redundant and obsolete data becomes persistent across clouds, SaaS, and backups, every downstream control has more work to do. Security, privacy, and AI teams all inherit the same root problem: excessive retention expands the amount of information that must be governed, defended, and justified. Practitioners should treat minimization as part of the security architecture, not a periodic cleanup.
A few things that frame the scale:
- 19% of organisations give AI systems dramatically more access than human employees, nearly one in five granting unrestricted privilege, according to the 2026 Infrastructure Identity Survey.
- Systems with least-privileged AI access had a 17% incident rate vs 76% for over-privileged systems. Organisations failing to scope AI access properly are 4.5x more likely to experience a security incident, according to the 2026 Infrastructure Identity Survey.
A question worth separating out:
Q: How do teams know if data minimization is actually working?
A: Data minimization is working when the organisation can consistently catalogue data, remove unnecessary records, and prevent collection or sharing that is not tied to a clear purpose. A working programme shows lower data sprawl, fewer retained duplicates, and a smaller set of systems requiring protection. If teams still struggle to locate sensitive fields, the control is not mature.
👉 Read our full editorial: ROT data minimization is now a security and AI control