NIST Privacy ยท AI & Data Ethics

Privacy engineering for the age of Large Language Models and Machine Learning

The NIST Privacy Framework now intersects heavily with the NIST AI Risk Management Framework. Training models on customer data introduces risks that traditional controls cannot catch. We engineer the safeguards required to build AI that respects privacy.

  • Training Data Sanitization Before data reaches the GPU, it must be clean. We build ETL pipelines that strip PII, scrub identifiers, and verify that training datasets align with privacy commitments before a model ever sees them. Provenance tracking from source to training run.
  • Inference Privacy Guardrails Preventing models from memorizing and regurgitating sensitive data. We architect input/output filtering layers and red-team your models for privacy leakage and training data extraction attacks. The controls that keep PII out of model responses.
  • KSI-Aligned Architecture Design Infrastructure that inherently meets 20x requirements, immutable resources, zero-trust networking, least-privilege access, automated configuration management. When your architecture is built for compliance, evidence generation becomes automatic.
  • Algorithmic Impact Assessments Technical evaluation of automated decision-making systems. We analyze the logic, the inputs, and the outcomes to detect bias, ensure fairness, and validate that privacy choices are not overridden by algorithmic optimization.

You cannot retroactively add privacy to a trained model. We help you build the data infrastructure that allows AI innovation without compromising user trust or compliance posture.

Includes:

  • Training Data Scrubbing Pipelines
  • Model Privacy Testing
  • AI Risk Management Alignment
  • Automated Decision Logic Audit
  • LLM Privacy Controls