Update organization card: add Galvanize 2 and the evaluation dataset

README.md

@@ -10,19 +10,34 @@ Robust detection under distribution shift, efficient encoders on CPU
 budgets, representation learning for structured payloads, calibration
 of operating points, and evaluation methodology for agent security.
 
-## Artifacts
+## Models
 
-**Galvanize-60M**: 60M parameter prompt injection classifier distilled
-from ModernBERT base, Apache 2.0. Certified 1.00% tool false positives,
-91.60% out of distribution injection recall, 11.52 ms p50 CPU, native
-8,192 token context, INT8 build at 176 MB.
+**Galvanize 2** (current): multilingual prompt injection classifier,
+about 141M parameters, Apache-2.0. Fine-tuned from
+[Horizon-Labs/prompt-injection-guard-small](https://huggingface.co/Horizon-Labs/prompt-injection-guard-small)
+(Apache-2.0). On a private held-out test set of 3,309 examples, at the
+serving threshold of 0.662: 83.4% recall and 0.81% false positive rate.
+
+Model: [usezn/Galvanize-2](https://huggingface.co/usezn/Galvanize-2) ·
+Launch notes: [Introducing Galvanize 2](https://usezn.com/blog/introducing-galvanize-2/)
+
+**Galvanize-60M** (previous): 60M parameter prompt injection classifier
+distilled from ModernBERT base, Apache-2.0. It remains available and
+serves as the automatic fallback.
 
 Model: [usezn/Galvanize-60M](https://huggingface.co/usezn/Galvanize-60M) ·
-Methods and benchmark: [Introducing Galvanize-60M](https://usezn.com/blog/introducing-galvanize-60m/) ·
-Lab page: [usezn.com/galvanize](https://usezn.com/galvanize/)
+Launch notes: [Introducing Galvanize-60M](https://usezn.com/blog/introducing-galvanize-60m/)
+
+## Dataset
+
+**zn Prompt-Injection Evaluation Corpus**: binary text classification
+corpus of prompt injection attacks and hard benign negatives, with
+per-row source, license and language. CC-BY-4.0.
+
+Dataset: [usezn/zn-prompt-injection-bench](https://huggingface.co/datasets/usezn/zn-prompt-injection-bench)
 
-## Reproduce
+## Links
 
-Weights, tokenizer and quantized graph ship in the repo. The launch
-notes document the 32 core protocol, tool holdouts and long context
-suites so every number can be remeasured.
+- Website: [usezn.com](https://usezn.com)
+- Code: [github.com/usezn/zn](https://github.com/usezn/zn), the open-source local security layer for AI coding agents
+- Security reports: security@usezn.com