Published Work
Constitutional AI governance, adversarial detection, and model-agnostic safety infrastructure. All research published with DOIs and benchmarks.
A pre-filter architecture for model-agnostic adversarial detection. PatternWall intercepts prompts before they reach an AI model, enforcing governance as deterministic infrastructure rather than relying on model-level training or provider-specific tuning.
A post-inference governance engine that evaluates AI outputs across five weighted dimensions to detect harmful content bypassing model-native safety. Benchmarked across five frontier models on 1,507 CVE exploit tasks and 28 multi-turn adversarial campaigns. Demonstrates that infrastructure-level governance is necessary because model-level safety is unreliable, inconsistent, and provider-dependent.