Topics PII & CUI with AI Agents
Series · 6 posts Contact
Series Overview

PII & CUI
with AI Agents

AI agents are powerful precisely because they read files, query databases, and process documents autonomously. That same capability makes them a compliance risk the moment sensitive data enters the loop. This series covers a practical approach: detect PII and CUI accurately, redact or transform it correctly, and configure the whole system through a simple CLI — without degrading the agent's usefulness.

What you'll learn

Accurate detection
Structured outputs + regex anchors that find sensitive fields without hallucinating false positives.
Smart redaction
Token masking, pseudonymization, and format-preserving encryption — matched to your use case.
Easy CLI config
One YAML file and CLI flags let operators tune thresholds and exclusions without touching agent code.
Audit trails
Append-only logs and structured event schemas that satisfy compliance review without slowing you down.

The core tension

Most PII/CUI solutions fall into one of two failure modes: they are so aggressive they block legitimate agent work, or so permissive that sensitive data leaks through. The right approach balances recall (catch all sensitive data) against precision (don't over-redact). This series shows you how to tune that balance deliberately rather than accepting whatever a library's defaults give you.

⚠️

This is not a compliance checklist. Regulations like GDPR, HIPAA, and NIST CUI policy are context-dependent. This series focuses on the engineering layer — building a system that can express whatever policy your legal team defines.

PII vs CUI at a glance

Dimension PII CUI
Governed by Privacy law (GDPR, CCPA, HIPAA…) Federal policy (NIST SP 800-171, EO 13556)
Who it protects Individual persons Government/national interests
Examples SSN, email, medical record Export-controlled data, law enforcement records
Overlap risk A document can contain both — e.g., a federal contractor's personnel file.

Posts in this series

01
What is PII & CUI? Live
A practical taxonomy of sensitive data — what counts as PII, what falls under CUI, and why the distinction matters when you route data through an AI agent.
PIICUITaxonomyDefinitions
02
Detection Without Hallucination Live
Using structured outputs, regex anchors, and model-grounded classification to find sensitive fields reliably — without false positives that block real work.
DetectionStructured OutputAccuracy
03
Redaction Strategies Live
Token-level masking, format-preserving encryption, and pseudonymization — how to pick the right strategy for your accuracy and reversibility requirements.
RedactionEncryptionPseudonymization
04
Easy Config via CLI Tool Live
A single YAML file and a CLI flag system that lets operators tune detection thresholds, exclusion lists, and output modes without touching agent code.
CLIConfigYAMLDX
05
Audit Trails & Logging Live
Append-only logs, redacted evidence records, and structured event schemas so every sensitive data access is traceable and review-ready.
AuditLoggingCompliance
06
End-to-End Pipeline Live
Wiring detection, redaction, config, and audit into a single cohesive pipeline — with a reference implementation you can drop into your own agent.
PipelineReference ImplPython