Kubernetes MLSec: Securing AI in space | Andrew Martin & Francesco Beltramini
In the gold rush to unearth the next groundbreaking AI technology, operational security has been one of the first victims. We feed ever greater volumes of PII and proprietary secrets into models running on "other people's computers" and receive fewer guarantees than ever before about the safety and sanctity of our data. High-profile breaches with cross-customer data leaks and training on user inputs lead us to ask: do we trust that mode input data in unpolluted and verified?Are we sure inputs remain ours and isn't used to train other systems? Will your financial history be used to define insurance rates? Cloud native is here to help! In this talk we:
- Break into and poison a Kubernetes model-training environment
- Demonstrate the dangers inherent in feeding data into any LLM
- Threat model ML topologies on Kubernetes
- Suggest cloud native architectural and procedural remediation
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