Creto Systems

The problem

Data security teams need to prove privacy compliance while enabling AI use cases that touch sensitive data. Classic DLP catches known patterns but misses the ways generative AI reshapes and exfiltrates in novel forms — a model can be prompted to summarize, translate, or restate PII in ways that evade regex-based filters. Teams get stuck choosing between AI velocity and audit-ready privacy, and the "safe" choice (block everything) is usually the wrong long-term answer. Enterprises need a middle path: AI enabled on sensitive data with defensible controls at every layer of the pipeline. That means detection and redaction at ingest, per-interaction auditability, and compliance reporting that satisfies internal and external reviewers.

Key challenges

Privacy-aware AI pipelines are a multi-layer problem. Ingest-time PII detection has to be precise enough not to over-redact useful context and comprehensive enough not to leak. Audit trails have to be granular per model interaction, retained on defensible schedules, and queryable for compliance reviews. And the whole system has to keep up with a moving landscape — new AI providers, new regulations, new attack patterns — without requiring a rewrite each cycle.

What we built

AR Data supported engineering on Creto's platform — privacy-aware AI pipelines, PII detection and redaction at ingest, and audit trails per model interaction. The platform enables compliant use of generative AI on enterprise-sensitive data through defense-in-depth at the pipeline layer rather than reliance on model-provider promises. The architecture: an ingest classifier that detects PII categories (regulated identifiers, financial data, health-related content) with high precision; policy-driven redaction that preserves semantic context where possible; per-interaction audit records with redaction reasoning attached; and compliance reporting that surfaces patterns (frequency of PII appearance, redaction rate, model interaction volume) at the granularity auditors ask for.

Our approach

1. 1

   Ingest-time detection over runtime hoping

   PII detection happens at ingest, before content reaches the model. Filtering at the model output layer is a fallback, not the primary control.

2. 2

   Redaction preserves semantic context where possible

   Blindly stripping PII destroys useful context. Semantic-aware redaction ("patient's condition" instead of "patient name's condition") preserves usability while removing the sensitive data.

3. 3

   Per-interaction audit records with reasoning

   Every model interaction produces a structured record: what was detected, what was redacted, why, and what the model saw. This is what makes compliance review efficient rather than manual.

4. 4

   Provider-agnostic pipeline abstraction

   New AI providers land constantly; the pipeline abstracts provider details so swapping doesn't require rearchitecting compliance controls.

Key architectural decisions

Results

• Privacy-aware AI pipelines with defensible controls at each layer
• Automated PII redaction with semantic context preservation
• Model interaction audit trails at per-interaction granularity
• Compliance reporting with the granularity auditors expect
• Provider-agnostic abstraction supports rapid AI provider swaps
• Deployed for enterprise clients with regulated data workloads
• Reduced compliance-review friction on AI-enabled workflows

Impact

Creto's platform is a working example of enterprise AI that doesn't require choosing between velocity and compliance. The engagement produced patterns we now bring to any enterprise-AI project: ingest-first controls, semantic redaction, structured audit as first-class output, and provider abstraction that keeps the compliance posture stable as the AI landscape churns.

Tech stack