These days, it feels like everyone is building something with AI. From specialized analytics assistants, diagnostics and decision-making systems to project management and CRM platforms. AI is becoming part of every tool we use. All powered by large language models and agents that increasingly depend on complex data pipelines.
But what happens when the data behind those applications breaks?
The short answer is that things go wrong very quickly, and not always in obvious ways!
While at re:Invent this year, I attended a session from Commvault on Best practices to simplify resilience at scale for Gen AI data & apps to understand how enterprise customers are doing AI resilience properly.
Gen AI is only as good as its data pipeline
Gen AI applications rarely fail because of the model itself. Instead, failures often start much lower down the stack in the data layer.
AI is only as good as the data we feed it. Missing records, corrupted partitions, overwritten tables, or deleted objects don’t just cause downtime. They cause:
- Models to hallucinate
- Dashboards to show incorrect insights
- Applications to behave unpredictably
The concerning thing is that you may not even realize it, applications can keep running while quietly becoming wrong.
In the session, this was demonstrated using real-world AWS services like DynamoDB, Amazon S3, and Apache Iceberg, showing how failures in each layer of the data pipeline can directly impact Gen AI behavior - even when the application itself appears healthy.
The data protection gap
During the session, the speakers asked a simple question: How many people are building GenAI applications in AWS? Unsurprisingly, many hands were raised. However when asked
How many are actively protecting the data layer that powers those applications? I saw far fewer hands.
This gap isn’t surprising. Most teams invest heavily in scaling compute, tuning models, and improving performance, while assuming data protection is handled somewhere in the background. Many organizations expect their existing backup system will handle the restoration of data following a loss or corruption.
In reality, many modern data pipelines are only partially protected, or protected in ways that don’t support the fast, clean recovery that customers expect.
Why traditional backup isn’t enough
Traditional backup tools weren’t designed for the way modern cloud-native and Gen AI data pipelines fail, particularly when recovery needs to happen quickly and at a very granular level.
A recurring theme throughout the talk was that recovery speed and simplicity matter more than theoretical durability.
Traditional recovery approaches often look like this:
Step 1: Restore an entire dataset
Step 2: Create temporary tables or buckets
Step 3: Repoint applications
Step 4: Rebuild downstream dependencies, like manually recreating dashboards
This process is slow, error-prone, and expensive, especially when customers are waiting for systems to be back online.
What stood out to me was the emphasis on in-place, granular recovery.
Recover only what broke, restore it directly to the original location, and avoid reconfiguration wherever possible. Less operational work means faster recovery, and fewer mistakes during already stressful incidents.
Clumio Backtrack enables individual records or partitions to be recovered in-place without creating temporary tables or repointing applications.
The “last known good state” problem
Another subtle but important insight: in distributed cloud systems, there is rarely a single “last known good” timestamp.
Different components fail at different times. DynamoDB partitions may be corrupted minutes apart, S3 objects may be deleted individually, Iceberg tables may be overwritten by a single bad query. Effective recovery needs to work at the level of records, partitions, objects, and snapshots, not just entire databases or buckets.
Clumio Backtrack also supports point-in-time and granular recovery across distributed services like DynamoDB and S3 rather than forcing teams into lengthy all-or-nothing restores.
RAG pipelines are especially vulnerable
Retrieval-augmented generation (RAG) pipelines introduce an extra layer of fragility. Vector stores depend on underlying source data, if that disappears, embeddings quickly become useless, if you’ve ever ingested data into an Amazon Bedrock knowledge base you’ll know that recreating vectors is time-consuming and costly for large datasets.
Even small amounts of data loss can lead to wildly inaccurate AI responses, delivered with high confidence. Protecting raw source data turns out to be one of the most important steps in keeping Gen AI outputs trustworthy.
S3 object-level recovery from Clumio Backtrack enables only the impacted objects to be restored, avoiding the need to recompute whole vector stores or rebuild the entire RAG pipeline.
Apache Iceberg: powerful, but not immune
The session also covered Apache Iceberg, which has become a hugely popular foundation for modern lakehouse architectures. Supporting large-scale analytics, transactional consistency and schema evolution.
However it also introduces new failure modes. A single overwrite or schema change can silently break dashboards and analytics without obviously deleting data.
If you are only backing up the general purpose S3 bucket data that is powering the Iceberg tables, you’ll need to first do a full restore of every S3 bucket that holds Iceberg data, then restore the Iceberg table structure, including reconfiguring your manifest files, metadata and data files. Then reconfigure your applications to talk to the new table and recreate all your dashboards that existed for the previous dataset.
**The key takeaway for me is that Iceberg-aware recovery matters. Restoring raw files alone isn’t sufficient. **Table structure, metadata, and dashboards must be preserved to truly recover.
The first Iceberg-aware and S3 tables data protection solution
For organizations that require true AI maturity, Commvault recently announced Iceberg-aware recovery capability from Clumio Backtrack. Providing industry-first Iceberg recovery capability, preserving table structure, metadata, and snapshots and delivering recovery without rebuilding dashboards or reconfiguring applications. Just pick a snapshot, pick a point in time, and recover, all in one step.
Key takeaways I’ll be applying going forward
Design resilience at the data layer, not just the model layer
Protect the entire data pipeline not individual services in isolation
Optimize for fast, in-place recovery
Plan for granular failures, not entire rollbacks
Focus on user experience, not uptime metrics
As GenAI applications become more central to how all of us operate, AI maturity demands that data resilience becomes non-negotiable. The quality, accuracy, and trustworthiness of AI outputs all depend on it.
To learn more, check out Best practices to simplify resilience at scale for Gen AI data & apps or request a demo of Commvault’s cloud-native cyber resiliency, data protection and recovery solutions.
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