Preventing AI Model Theft: Security Best Practices
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if you fine tuned a model, you need to protect it. heres how companies are securing their ai assets
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zero trust is fine in theory but what does that actually look like when your inference endpoint needs to serve 50k requests a minute? latency hits are real and most teams cave on policy strictness under load.
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the threat surface people forget is the inference API itself. rate limiting and output monitoring matter as much as protecting the weights. model extraction attacks via repeated queries are very real.
model watermarking is underrated here. there are techniques to embed fingerprints into weights so if your model leaks you can actually prove provenance. not many teams are doing this yet but it's worth looking into.