AI innovation depends on consistent, trusted data. When disrupted, AI systems and the business decisions they support are at risk. In this session, learn how cloudnative protection models support AI pipelines, reduce recovery time after disruptions, and minimise operational overhead. Discover best practices to protect AI and cloudnative applications in AWS while innovating with confidence.
What this session is about
Playbook
Editorial commentary · what to actually do about this on Monday
The concept
AI training data and feature stores are the new crown jewels. Recovery cost from corruption now exceeds the cost of regenerating model weights.
Why it matters
A poisoned RAG corpus or corrupted training set can ship bad behaviour to thousands of users before you notice. Silent data corruption is worse than data loss.
The hard parts
Backups for ephemeral pipeline state are weird. Idempotent reprocessing usually beats restoring intermediate state. Integrity checking on training data is not the same as backup.
Playbook moves
(1) Define RPO/RTO per data class — raw, processed features, embeddings, model weights. They differ. (2) Set integrity hashes on training corpora; recheck before fine-tune jobs. (3) Test restore quarterly, not annually.
The surprise
Most teams over-protect model weights (cheap to retrain) and under-protect feature stores (expensive to rebuild from raw). Your backup budget is probably allocated wrongly.
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Independent editorial perspective — not an official AWS or speaker statement. Designed for executives evaluating what to brief their teams on next.
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