Compliance Mapping
NIST AI Risk Management Framework
Compliance Mapping
How Transfer Oracle structural auditing maps to the seven characteristics of trustworthy AI defined in NIST AI RMF 1.0.
7 Characteristics of Trustworthy AI
Valid & Reliable
NIST Requires
Disaggregated accuracy across data segments. Ongoing monitoring. Robustness under varied conditions. Accuracy paired with realistic test sets.
Transfer Oracle Provides
Per-class structural coverage analysis. Instead of one average number, per-category coverage scores, gap severity, and blind spot detection. A model scoring 98.8% overall may hide a 7.3% drop in a specific category — our audit finds it.
95% coverage
Safe
NIST Requires
Rigorous simulation and in-domain testing. Real-time monitoring. Ability to shut down or modify deviating systems. Explanations based on empirical evidence.
Transfer Oracle Provides
Abstention signals. When the structural audit finds insufficient training support for a sample, the system says "I don't know" instead of guessing. Real-time monitoring via continuous distribution drift detection.
90% coverage
Secure & Resilient
NIST Requires
Withstand adversarial examples and data poisoning. Maintain function after adverse events. Protect IP and training data.
Transfer Oracle Provides
Novelty detection catches out-of-distribution and adversarial inputs structurally. Mutual privacy architecture — customer model unseen by auditor, audit method unseen by customer.
85% coverage
Accountable & Transparent
NIST Requires
Audit trails. Documentation of decisions. Accessible information about system outputs. Actionable redress for incorrect outputs.
Transfer Oracle Provides
Structural audit certificates with tamper-proof provenance. Per-category coverage reports documenting exactly where a model is strong and where it has gaps. Complete session artifacts for regulatory submission.
92% coverage
Explainable & Interpretable
NIST Requires
Understand why AI produced an output. Provide information to users about AI system limitations.
Transfer Oracle Provides
Structural coverage maps that visually show where a model has knowledge and where it doesn't. Per-category gap reports explain which categories are weak, how weak, and why (coverage loss, distribution shift, neighborhood erosion).
80% coverage
Privacy-Enhanced
NIST Requires
Protect data confidentiality. Prevent exfiltration of models, training data, or IP through AI endpoints.
Transfer Oracle Provides
Mutual privacy — the audit operates on structural representations (embeddings), not raw data. The customer's model internals are never exposed. Hardware deployment keeps audit algorithms in encrypted FPGA gates.
75% coverage
Fair — Bias Managed
NIST Requires
Detect and manage harmful biases. Disaggregate results across affected groups. Recognize that harms may affect varied groups differently.
Transfer Oracle Provides
Per-category structural analysis inherently disaggregates. If categories represent demographic groups, the audit shows exactly where the model underserves each group — coverage gaps, drift, and neighborhood erosion per group.
88% coverage
NIST AI RMF Core Functions
Four functions organize AI risk management. Transfer Oracle is primarily a MEASURE tool, with outputs feeding MAP and MANAGE.
GOVERN — Cross-cutting: policies, roles, culture, documentation
MAP
Identify risks in context
Per-class coverage gap identification
MEASURE
Quantify and assess risks
Structural integrity scores, drift metrics
MANAGE
Respond to and recover
Editability prediction, remediation ranking
API Capability to NIST Function Mapping
| API Capability | NIST Function | NIST Characteristic | What It Measures |
|---|---|---|---|
| Transfer Audit | MEASURE | 3.1 Valid & Reliable | Structural alignment between training and deployment distributions |
| Coverage Scan | MAP | 3.1 Valid & Reliable | Per-category coverage gaps and blind spots |
| Distribution Drift | MEASURE | 3.1 Valid, 3.3 Resilient | Per-category centroid shift and spread change over time |
| Novelty Detection | MEASURE | 3.2 Safe, 3.3 Secure | Out-of-distribution and adversarial input detection |
| Structural Probe | MEASURE | 3.5 Explainable | Directional stability, structural coherence, rank preservation |
| Editability Prediction | MANAGE | 3.1 Valid, 3.7 Fair | Which categories are surgically fixable after compression or drift |
| Distribution Monitor | MANAGE | 3.2 Safe, 3.3 Resilient | Continuous structural drift monitoring with escalation triggers |
| Audit Certificate | GOVERN | 3.4 Accountable | Tamper-proof audit trail with session artifacts for compliance |
“Measurement approaches can be oversimplified, gamed, lack critical nuance, be relied upon in unexpected ways, or fail to account for differences in affected groups and contexts.”
— NIST AI RMF 1.0, Section 1.2.1 Risk Measurement
This is exactly what Transfer Oracle solves. Aggregate metrics hide per-category damage. Structural auditing disaggregates.
Map your compliance
Tell us about your compliance requirements. We'll show you how Transfer Oracle maps to your regulatory framework.
Reference: NIST AI 100-1 · doi.org/10.6028/NIST.AI.100-1 · January 2023