Your privacy claims are only as strong as your evidence. We provide the technical proof you need for GDPR, CCPA, and the EU AI Act.
Architecture review before implementation
Implementation scope and rollout planning
Clear next-step recommendation
Quantify and verify the privacy guarantees of your AI systems to meet regulatory scrutiny.
Your privacy claims are only as strong as your evidence. We provide the technical proof you need for GDPR, CCPA, and the EU AI Act.
Our audits deliver defensible, quantitative privacy metrics using industry-standard tools and adversarial testing:
TensorFlow Privacy and Opacus.We translate complex privacy guarantees into executive-ready compliance reports and remediation roadmaps. Ensure your AI initiatives are both innovative and legally defensible. Explore our broader approach to Privacy-Preserving AI Computation or learn about building compliant infrastructure with Sovereign AI Infrastructure Development.
A certified privacy audit from Inference Systems delivers more than a compliance checklist. It provides defensible, technical proof of your AI's privacy posture, enabling trust with regulators, partners, and customers while de-risking your AI initiatives.
Identify and remediate latent privacy vulnerabilities—like membership inference or attribute inference attacks—before deployment. Our audit provides a clear roadmap to harden your models, preventing costly post-launch fixes, reputational damage, and potential data breach liabilities.
Quantify the exact privacy budget (epsilon) your models consume and receive expert guidance on tuning differential privacy noise or encryption parameters. We help you maximize model accuracy and utility while maintaining mathematically proven privacy levels, avoiding unnecessary performance degradation.
In regulated procurement processes for defense, government, and enterprise clients, a certified privacy audit is a decisive differentiator. It provides tangible evidence of your technical maturity in privacy-preserving AI, directly addressing stringent RFP requirements.
Our tiered audit approach provides clear, actionable verification of your AI's privacy guarantees, from foundational checks to comprehensive adversarial testing.
| Audit Component | Compliance Check | Technical Deep Dive | Adversarial Certification |
|---|---|---|---|
Privacy Loss Accountant Review | |||
Differential Privacy (ε,δ) Guarantee Verification | |||
Homomorphic Encryption Implementation Audit | |||
Secure Multi-Party Computation Protocol Review | |||
Attack Simulation (Membership/Attribute Inference) | |||
Full Adversarial Red Teaming (MITRE ATLAS) | |||
EU AI Act / GDPR Compliance Gap Report | Summary | Detailed | Detailed + Remediation Plan |
Executive Summary & Technical Findings Report | |||
Remediation Support & Consulting Hours | 2 hours | 10 hours | 40 hours |
Certification of Privacy Guarantees | Letter of Assessment | Technical Certification | Public-Facing Attestation |
Typical Engagement Timeline | 2-3 weeks | 4-6 weeks | 8-12 weeks |
Starting Investment | $15K | $45K | Custom |
Our privacy-preserving AI auditing services are critical for organizations in regulated sectors where data sensitivity is paramount and compliance claims must be technically defensible. We provide verifiable assessments using tools like privacy loss accountants and attack simulations.
Audit AI systems handling Protected Health Information (PHI) and clinical trial data. We verify compliance with HIPAA and ensure diagnostic models using patient records cannot be reverse-engineered, a key requirement for FDA submissions involving AI/ML.
Technical verification of AI used for fraud detection, credit scoring, and algorithmic trading. Our audits measure privacy loss in models trained on transaction histories and personal financial data, ensuring defensibility against regulators like the CFPB and SEC.
Assess AI systems processing classified or sensitive unclassified information. We provide air-gapped audit capabilities and verify that models used for intelligence analysis, autonomous systems, or personnel vetting do not create data leakage vulnerabilities.
Audit predictive models for underwriting and claims processing that use highly personal data (health, driving behavior, property details). We ensure algorithmic fairness and privacy guarantees are mathematically sound to prevent disparate impact claims.
Verify privacy claims for recommendation engines and dynamic pricing AI that process consumer purchase histories, browsing behavior, and location data. Critical for compliance with evolving state-level consumer privacy laws.
Audit AI tools for contract analysis, e-discovery, and litigation prediction that process attorney-client privileged communications and sensitive case data. We ensure these systems uphold legal confidentiality obligations.
Enabling Efficiency, Speed & Accuracy
We build AI systems for teams that need search across company data, workflow automation across tools, or AI features inside products and internal software.
Get specific answers about our technical audit process, timeline, and outcomes for verifying the privacy guarantees of your AI systems.
Our methodology is based on the NIST AI RMF and ISO/IEC 42001 frameworks, adapted for privacy-enhancing technologies (PETs). We use a combination of automated tools and manual analysis, including privacy loss accountants (e.g., Google's TensorFlow Privacy), membership inference attack simulations, and differential privacy verification libraries. We assess the entire pipeline, from data ingestion to model deployment, against your stated privacy claims.

About the author
CEO & MD, Inference Systems
Prasad Kumkar is the CEO & MD of Inference Systems and writes about AI systems architecture, LLM infrastructure, model serving, evaluation, and production deployment. Over 5+ years, he has worked across computer vision models, L5 autonomous vehicle systems, and LLM research, with a focus on taking complex AI ideas into real-world engineering systems.
His work and writing cover AI systems, large language models, AI agents, multimodal systems, autonomous systems, inference optimization, RAG, evaluation, and production AI engineering.
How We Work
One-fit-all AI don't work for modern businesses. At Inferensys, we aim to understand your business & custom requirements; which we use to define most efficient agentic workflows, the data, and the tools for your business.
The first call is a practical review of your use case and the right next step.