AI Privacy
Without Compromising Performance
AI systems can memorize training data, leak PII in outputs, and send sensitive information to third-party APIs. We build AI applications that protect data privacy by design, meeting compliance requirements while delivering the AI capabilities your business needs.
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Key Capabilities
Everything you need to build production-grade solutions
Data Leakage Prevention
Implement output scanning, PII detection, and content filtering to prevent sensitive data from appearing in AI responses.
Privacy-Preserving Architectures
Design systems that minimize data exposure: on-premise deployments, data anonymization pipelines, and zero-trust API patterns.
GDPR & HIPAA Compliance
Build AI applications that meet regulatory requirements from day one. Data subject rights, audit trails, and consent management built in.
Model Privacy Audits
Assess your models for memorization risks and data leakage vulnerabilities. Know what data your AI might expose.
Secure Data Pipelines
Build encrypted, access-controlled data pipelines for AI training and inference. Protect data at rest and in transit.
Third-Party Risk Management
Evaluate and mitigate risks from LLM API providers. Understand what data leaves your environment and implement appropriate controls.
Why Procedure for AI Privacy?
We bring senior engineering expertise and production-tested patterns to every engagement. No junior developers learning on your project.
Compliance expertise: We've built HIPAA-compliant and GDPR-ready AI systems
Technical depth: Privacy isn't just policy. We implement the technical controls
Privacy by design: We architect for privacy from the start, not as a patch
Pragmatic approach: We balance privacy requirements with business needs
Technologies We Use
Production-tested tools and frameworks
Ready to Discuss Your
AI Privacy Project?
Share your compliance requirements and data handling challenges. We'll discuss privacy-preserving architectures and how to meet GDPR, HIPAA, or SOC 2 standards.
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Frequently Asked Questions
Yes, with the right architecture. Options include using enterprise API tiers with data processing agreements, on-premise deployments, or hybrid approaches that keep sensitive data local while leveraging cloud AI for non-sensitive tasks.