Make Smart Data Retention Calls in Cloud Products
Cloud products generate vast amounts of data, but keeping everything forever creates unnecessary security exposure and compliance headaches. This article walks through six practical strategies for deciding what to keep and what to discard, drawing on guidance from security architects and data governance specialists. The goal is to retain only what serves a clear business or legal purpose while reducing the attack surface of stored information.
Erase Unused Raw Data, Preserve Anonymized Trends
How do I decide what customer data to keep? "Will this data still be valuable or is it just introducing unnecessary risk? " From a white hat hacker's perspective, companies hoard customer data longer than they should because data storage seems cheap. The risk catches up to them later, when a data breach happens or a company is undergoing a compliance review or even worse, an insider threat.
I use multiple retention tiers: fast deletion for raw, sensitive customer data and long-term retention for anonymized analytical data. My "cleanliness rule, " as I call it, is one thing that saved a few of my past projects: if a raw log has not been viewed in a year, automatically delete it. Inactive user identifiers will be anonymized (hashed) or will be automatically deleted after two years. Meanwhile, anonymized trends continue to be valuable. We saved a huge amount of liability by deleting the old raw data and a few years later, if something did happen, one of my legacy projects would not have suddenly become such a massive liability overnight. It boils down to: The safest customer data is the data you no longer need to keep.
Tier Old Records, Cut Hot Storage Risk
Retention policy has to start with what your customers require. For regulated industries, the framework often decides for you, i.e. financial records, healthcare data, and audit logs can have 5, 10, even 30-year requirements. For businesses without regulatory drivers, many don't need a retention policy at all if the data has no future use.
For Kalos, we default to one year of data retention, but we expose that as a configurable setting because customer needs vary widely. The design question we always ask is: what portion of this data will actually be queried later, and what's the cost of keeping it all in hot storage indefinitely?
The rule that's had the most impact on risk prevention is tiering. Rather than deleting data on a hard cutoff or keeping everything at full cost in primary storage, we push aging data into progressively deeper storage tiers, so it's still accessible for later analysis, but not burning compute and storage budget. Less active data in hot storage means a smaller blast radius if something goes wrong.
Quarantine Bot Traffic, Remove After Two Weeks
One of the most powerful and underappreciated options when implementing a retention rules schema for a SaaS product, when you're thinking about privacy, usefulness of analytics, and cost of storage, is this:
Implement an aggressive "flag and fast-purge" retention rule for bot-generated data.
This is because in a modern SaaS product in the cloud, a huge amount of data plaguing your storage endpoints comes from bad actors. If you don't aggressively filter this out with retention rules, the telemetry you store and the analytics you compute can become actively harmful to overall product metrics.
In fact, a recent high-profile reputation crisis involving a major consumer brand that the WSJ has covered involves the revelation that nearly half of all ramped engagement numbers are actually fake, and that 70% of the messaging traffic is made up of duplicate bot-driven loops. Storing this artificially created noise on a long-term basis needlessly inflates cloud hosting costs and poisons product telemetry in subtle ways.
So the retention rule I'm advising here is: Implement AI/ML-driven real-time monitoring on the ingestion of data into your observability stack to flag when expected patterns of traffic get weirdly botched by bad actor spikes. If the data can be flagged as artificial and coordinated, then don't delete it immediately, but don't allow it to pass into the main warehouse/metrics system.
Instead, place it into a quarantined low-cost storage silo with an aggressive retention period of two weeks. Then artificially delete it after two weeks. This short retention period is important because it means that your internal security/data science rabbits can learn from this stuff while it happens. They can train their internal ML models on the weird attack signature, the manipulation attempt, and otherwise update the threat playbook. But after two weeks, it's gone.
This is an important policy that prevents a very real-world edge case of fraudulent artificial feedback getting injected into the system, so product teams don't get tempted to pivot their roadmaps. It also keeps storage costs down and gets rid of third-party data that you can't verify with any integrity, so you stay in a compliant posture with respect to privacy/security/contracts/etc. in place, so that the business only pays to store and ingest/learn from legitimate human signals.
Expire Orphaned Files, Retain Minimal Metadata
A rule recommends automatic deletion of failed import files and temporary exports after 30 days. These files often carry messy customer data outside the production model and are easy to forget. Only system logs are kept showing who created the file when processed and success status. This structure reduces clutter in storage systems and improves traceability for audits and monitoring.
A later review identified sensitive data buildup outside the main database. Abandoned files in storage buckets and shared folders were the biggest risk. Because files expired the review became faster and the exposure window was smaller. Process failures were learned through metadata without keeping raw personal data controls in place.
Map Fields To Purpose, Enforce Early Sunsets
Retention decisions go wrong when teams treat storage as cheap and trust as unlimited. In practice, every extra month of customer data expands legal exposure, incident response effort, and audit complexity. The best policy maps each field to a purpose, an owner, and a sunset date before collection begins. If a data element cannot be tied to customer support, security, finance, or a defined product hypothesis, it should not survive by default. That discipline protects privacy while making analytics cleaner and more defensible.
One rule I recommend is to rotate support related diagnostic snapshots out of production linked storage within 14 days, then preserve only sanitized issue patterns. We kept engineering learning intact, but removed a class of stale sensitive artifacts that often becomes a hidden liability during compliance reviews.
Honor Regulatory Floors, Shorten Telemetry Windows
The way I think about retention in a multi-tenant SaaS is what I call the regulatory-floor rule. Find the strictest retention requirement your customers face, then anchor your default to that, plus a small buffer. Everything else is negotiable.
For us at Paperless Pipeline, the strictest floor is real estate brokerage record retention. Most U.S. states require brokerages to keep transaction records for three to seven years after closing. So our default retention for transaction documents is the lifetime of the customer's account, with archive access. We do not auto-delete a closed transaction at 18 months because a state auditor in Texas or California will ask for that file in year four.
That choice has paid off more than once. A brokerage in the Pacific Northwest got a state audit covering deals from five years prior. They pulled the full transaction file in about ten minutes from their archive view. The auditor closed out same week. If we had quietly deleted those files at 24 months to save storage, that customer would have failed an audit because of our default.
The before-and-after change we made about eight years in: we used to keep server logs and product analytics events at the same retention as customer data, which made our analytics warehouse expensive and full of noise. We split the policy. Customer transaction data gets the long retention. Product telemetry (clicks, page views, performance traces) gets 90 days, with aggregates kept indefinitely. We learn just as much from 90 days of behavior as we did from three years, and our storage bill dropped meaningfully.
One rule that has prevented real risk: any field a customer can mark as sensitive (commission amounts, agent SSN equivalents on tax forms, bank routing on commission disbursement) is excluded from product analytics entirely. We never see it, we never store it in a second system, we cannot accidentally leak it in a debugging session. Pipeline has been live since 2009 with 1,700+ brokerages and over 4.6 million transactions, and the boring "exclude the sensitive fields from analytics" rule is the one I am gladdest we wrote down early.
If you have a regulated customer base, let their record retention requirement set your floor. Then split the policy so product telemetry runs short and customer data runs long. That is the whole system.
