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Your AI Feature Just Failed SOC 2 -- Here's Your Fix

If you're a SaaS founder watching compliance block your LLM roadmap, you're 90 days from shipping without shipping user data.

We build privacy-compliant AI architectures for SaaS platforms. PII redaction, data residency, vendor risk controls -- engineered into your stack, not bolted on.

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AI Privacy Compliance Engineering

AI privacy compliance engineering is the practice of designing data handling, retention, and vendor risk controls into LLM-powered systems at the architecture level. It covers PII detection and redaction before data reaches third-party model APIs, data residency routing, and contractual and technical controls over AI subprocessors. The result is a system that passes SOC 2 Type II, GDPR Article 28, and emerging EU AI Act audits without requiring a rebuild of your core product.

What is holding your current website back?

Common gaps we find in nearly every audit.

Your SOC 2 auditor flagged OpenAI or Anthropic as an unreviewed subprocessor and your enterprise deals are on hold.
Risk: Without a documented AI vendor risk assessment and a signed Data Processing Agreement, enterprise procurement will stall every deal over $50k and some will walk entirely.
User-submitted data — names, emails, free-text fields — is being passed raw into your LLM prompts with no redaction layer.
Risk: A single data subject access request or breach disclosure will expose that PII was sent to a third-party model provider, triggering regulatory review under GDPR Article 44 or CCPA.
Your EU customers require data residency but your current LLM calls route through US-based API endpoints by default.
Risk: Continued cross-border transfers without Standard Contractual Clauses or equivalent safeguards put you in direct violation of GDPR Chapter V and expose the company to supervisory authority action.

How We Build This Right

Every safeguard, built in from Day 1.

AI Subprocessor Risk Assessment

We audit your OpenAI, Anthropic, Azure OpenAI, and any other model API vendor against SOC 2, GDPR Article 28, and your existing vendor risk framework, then produce the documentation your auditors and enterprise customers will request.

PII Redaction Before Model Ingestion

We implement a pre-prompt redaction layer using entity recognition and pattern matching to strip or tokenize PII from user inputs before any data leaves your infrastructure boundary and reaches a third-party model API.

Data Residency and Routing Controls

We configure regional API routing, model selection logic, and infrastructure boundaries so EU and other jurisdictionally constrained user data never transits outside its required region, with audit logs to prove it.

What We Build

Purpose-built features for your industry.

Pre-Prompt PII Redaction Pipeline

A purpose-built middleware layer that detects and redacts or tokenizes personally identifiable information — names, emails, phone numbers, financial identifiers — from prompts before they reach any external model API, with configurable entity types per endpoint.

Data Residency Routing Architecture

Infrastructure and application logic that evaluates user jurisdiction at request time and routes LLM calls to the appropriate regional model endpoint or self-hosted alternative, with fallback handling and per-request audit logging.

AI Vendor DPA and Risk Documentation

Structured vendor risk assessments and gap analyses for your LLM providers against SOC 2 CC6, GDPR Article 28, and EU AI Act Annex III requirements, delivered in formats your auditors, legal team, and enterprise procurement teams accept.

Prompt and Completion Audit Logging

Tamper-evident, structured logging of all LLM interactions — redacted where required — with retention policies, export tooling for data subject access requests, and integration into your existing SIEM or compliance toolchain.

Built on a Modern, Secure Stack

Next.jsSupabasePresidioAzure OpenAILangChainOpenAI APIAnthropic APIVercelPostgreSQLRedis

Our Development Process

From discovery to launch. Quality at every step.

01

Compliance Gap Assessment

1 week

We map your current LLM data flows against GDPR, CCPA, SOC 2, and EU AI Act requirements, identify every point where user data crosses a compliance boundary, and produce a prioritized remediation plan with engineering effort estimates.

02

PII Redaction and Data Flow Controls

2 weeks

We design and implement the redaction pipeline, data residency routing logic, and any tokenization or pseudonymization layers your architecture requires, integrated into your existing API gateway or backend service without rewriting your product.

03

Vendor Risk and Documentation Package

1 week

We complete AI subprocessor risk assessments, review or draft DPAs with your model API vendors, and produce the audit-ready documentation set covering data flows, retention policies, and third-party controls your SOC 2 auditor will request.

04

Audit Logging, Testing, and Handoff

1-2 weeks

We deploy structured audit logging across all LLM interactions, run end-to-end compliance validation tests against your redaction and residency controls, and hand off runbooks so your engineering team can maintain and extend the system without us.

Social Animal

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Frequently Asked Questions

Yes. When EU user data goes to OpenAI's API, you're transferring personal data to a US-based processor. You need a valid transfer mechanism -- usually Standard Contractual Clauses -- a data processing agreement that covers AI-specific processing, and technical safeguards like PII redaction. GDPR doesn't care that OpenAI is doing the processing. You're the controller, and you're liable.
It depends on your risk classification. Most SaaS AI features fall under limited or high risk. Limited risk just means transparency -- telling users they're interacting with AI. High risk is a different story: conformity assessments, technical documentation, human oversight mechanisms, logging requirements. General-purpose model providers like OpenAI have their own obligations, but you as a downstream deployer have separate ones.
We use reversible tokenization. Before a prompt reaches the LLM, PII entities are swapped out for consistent placeholder tokens -- something like [USER_001] or [EMAIL_001]. The model processes the sanitized prompt and returns a response using those same tokens. We re-hydrate them with real values on your server. The LLM never sees actual PII, but your user gets a coherent, personalized response.
Azure OpenAI gives you the most control -- region-specific deployments, no training on your data by default, and mature enterprise DPAs from Microsoft. Anthropic has strong data handling policies but fewer regional deployment options. OpenAI's API hasn't trained on API data since March 2023, though regional control is more limited. The right answer depends on your residency requirements and what cloud infrastructure you're already running.
For a typical SaaS with one or two LLM integration points, you're looking at around 6-7 weeks from audit to deployment. That scales with complexity -- more touchpoints, more data types, multi-region requirements all add scope. The PII redaction pipeline alone usually takes 2-3 weeks including testing. Documentation and vendor risk assessment run in parallel to keep things moving.
Mostly, yes. One unified consent and data rights framework satisfies both. The main differences are around opt-out versus opt-in models and the specific rights involved. CCPA requires honoring "Do Not Sell/Share" signals for AI processing; GDPR requires explicit consent for automated decision-making. One architecture handles both, with region-specific logic sitting at the consent layer.
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