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GDPR + CCPA + EU AI ActPII Redaction PipelinesAI Vendor Risk Assessment

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.

Get Your AI Privacy Audit See Our Process
100%
Data Residency
Region-locked processing
<50ms
PII Redaction Latency
Real-time pipeline
0
PII Leaks to LLMs
By architecture, not policy
3
Regulations Covered
GDPR · CCPA · EU AI Act
What Is AI Data Privacy Compliance for SaaS?

AI data privacy compliance for SaaS is the engineering discipline of building LLM-powered features that actually meet GDPR, CCPA, and EU AI Act requirements -- by design, not by accident. That means PII redaction before data hits third-party models, data residency enforcement across regions, consent management for AI processing, and real risk assessment of vendors like OpenAI, Anthropic, and Azure OpenAI. The goal is shipping AI features without handing regulators a reason to come knocking -- or exposing yourself to breach liability.

Your Current Site May Be a Liability

Common gaps we find in nearly every audit.

User prompts with PII go straight to OpenAI or Anthropic APIs -- no redaction layer, nothing
Risk: One GDPR subject access request later and it's clear you've been shipping personal data to US-based processors. That's fines up to 4% of annual revenue.
No data processing agreements that cover LLM-specific data flows
Risk: Your existing DPAs don't mention AI processing at all, which is exactly the kind of gap auditors flag in the first ten minutes.
EU AI Act transparency requirements? Completely untouched
Risk: If you're classified as a high-risk AI system, you're already behind on conformity assessments you haven't started.
Data residency is handled by assumption -- you think Azure stays in eu-west, but you've never enforced it
Risk: One misconfigured API call routes EU user data through US servers, and you've got a cross-border transfer violation.
You picked OpenAI because the docs were good
Risk: No formal vendor risk assessment. When they quietly update their data retention policy, your compliance posture shifts overnight and nobody notices.
Engineering thinks privacy is a legal problem
Risk: Legal thinks it's an engineering problem. Nobody owns the compliance architecture, so it simply doesn't exist -- until due diligence or a breach forces the issue.

What Your Website Could Look Like

Custom-designed for your industry. No templates. No stock photos.

AI-generated motion preview

How We Build This Right

Every safeguard, built in from Day 1.

Real-Time PII Redaction Pipeline

We build inline redaction using Microsoft Presidio and custom NER models that strip PII before any data touches a third-party LLM. Names, emails, addresses, and custom entity types get replaced with reversible tokens, so responses still make sense to your users.

Data Residency Enforcement

Architecture-level controls keep EU user data in EU regions and US data stateside. We configure region-locked API routing, Supabase row-level security by geography, and deploy edge functions that enforce residency at the network layer -- not just at the config level.

GDPR + CCPA Consent Architecture

Consent management built into your actual data model, not a cookie banner. Users can opt out of AI processing specifically, and we build the plumbing to honor that preference at every LLM touchpoint.

EU AI Act Risk Classification

We assess your AI features against the EU AI Act's risk tiers and build the documentation, logging, and human oversight mechanisms your classification actually requires. Technical documentation generated from your real architecture -- not boilerplate someone copied from a blog post.

AI Vendor Risk Assessment Framework

Structured evaluation of OpenAI, Anthropic, and Azure OpenAI covering data retention policies, sub-processor chains, SOC 2 status, and contractual commitments. We also build monitoring that alerts you when vendor terms change.

Audit Trail & Logging

Every LLM interaction gets logged -- redacted inputs, model version, region, consent status, response metadata. Built for DPA audits and incident response. Queryable, exportable, and retention-policy aware.

What We Build

Purpose-built features for your industry.

Presidio-Based PII Detection

Custom-trained NER models that catch domain-specific PII beyond standard entity types, wired in as middleware in your LLM call chain.

Multi-Vendor LLM Gateway

A single API gateway routing to OpenAI, Anthropic, or Azure OpenAI with per-request compliance checks, redaction, and logging baked in.

Region-Aware Edge Routing

Vercel Edge Functions that determine user region and route LLM requests to compliant endpoints -- no added latency.

Consent-Gated AI Features

Database-level flags tied to your auth system that shut down AI processing paths when users haven't consented or have revoked it.

Vendor Policy Monitor

Automated scraping and diff-checking of AI vendor terms, data processing addenda, and sub-processor lists, with Slack or email alerts when something changes.

Compliance Dashboard

An internal admin panel showing PII redaction rates, consent coverage, data residency violations, and audit log queries -- in real time.

Built on a Modern, Secure Stack

Next.jsSupabasePresidioAzure OpenAILangChainOpenAI APIAnthropic APIVercelPostgreSQLRedis

Our Development Process

From discovery to launch. Quality at every step.

01

AI Data Flow Audit

Week 1

We trace every path user data takes through your application to LLM providers. Every API call, every prompt template, every cached response -- documented and risk-scored.

02

Regulatory Gap Analysis

Week 2

We compare your current architecture against GDPR Article 22, CCPA Section 1798.185, and EU AI Act requirements specific to your risk classification. You get a prioritized remediation plan, not a generic checklist.

03

PII Redaction & Residency Engineering

Weeks 3-5

We build and deploy the redaction pipeline, configure region-locked routing, and implement consent gates. Everything gets tested against synthetic datasets that match your actual data patterns.

04

Vendor Risk & Documentation

Weeks 5-6

Full AI vendor risk assessments, updated DPAs, EU AI Act technical documentation, and audit trail infrastructure. Everything your legal team and auditors need, built from real architecture rather than templates.

05

Penetration Test & Handoff

Week 7

We try to leak PII through your AI features using adversarial prompts and edge cases. Anything that surfaces gets fixed. Then we hand off with runbooks, monitoring dashboards, and 30 days of support.

Social Animal

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AI Privacy Compliance from $14,000

Fixed-fee. Includes audit, engineering, documentation, and 30-day post-launch support. See all packages →

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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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