Trust is a mathematical property, not a feeling. Prism transforms the "Black Box" of AI into a Glass Box, providing rigorous algorithmic auditing, continuous drift monitoring, and explainability (XAI) for high-stakes decision engines.
Data is not accessed; it is granted. Every query against the data vault triggers a Policy Engine Check (OPA). Standard access requires strict PII scrubbing.
Emergency cleartext access is possible but expensive. It requires M-of-N Multi-Sig consensus from senior officers and triggers immediate, immutable notifications to the Data Subject.
Access Control & Audit Flow
In regulated sectors (Finance, Healthcare), "it works" is not a valid explanation. You must prove how it works.
Prism implements Post-Hoc Interpretability techniques (SHAP, LIME) and Counterfactual Analysis ("What if?") to ensure every model decision can be traced back to human-understandable drivers.
Why was this specific loan denied?
What features drive the model generally?
To truly understand a decision, one must ask: "What is the smallest change required to flip the outcome?"
Prism automatically generates counterfactual explanations for every denied request or high-risk classification. This moves beyond static feature importance to provide actionable feedback (e.g., "If Debt-to-Income was 4% lower, this loan would have been approved.").
Statistical tests for Disparate Impact and Equal Opportunity across protected groups (Gender, Ethnicity, Zip).
Real-time detection of Concept Drift (P(y|x) changes) and Data Drift (P(x) changes) to trigger retraining.
Stress-testing models against edge cases, noise injection, and out-of-distribution inputs.
A model that is 99% confident but only 50% accurate is a liability.
Prism optimizes Expected Calibration Error (ECE) to ensure that confidence scores map linearly to ground-truth probabilities. If the model says "90% confident," it must be correct 90% of the time. This reliability is non-negotiable for autonomous agents.
Models degrade the moment they are deployed. Prism establishes a Continuous Evaluation pipeline that treats model performance as a living metric.
Running candidate models in parallel with production to compare outputs without user impact.
Sampling low-confidence predictions for expert review to fine-tune future iterations.
Ensure your AI systems are fair, explainable, and robust with Prism.