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.
Service
Privacy-Preserving AI Auditing

Quantify and verify the privacy guarantees of your AI systems to meet regulatory scrutiny.
Our audits deliver defensible, quantitative privacy metrics using industry-standard tools and adversarial testing:
- Privacy Loss Accounting: Measure cumulative data exposure with frameworks like
TensorFlow PrivacyandOpacus. - Attack Simulation: Stress-test models against membership inference, attribute inference, and model inversion attacks.
- Gap Analysis: Identify vulnerabilities in data handling, training pipelines, and inference endpoints.
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.
Business Outcomes of a Certified Privacy Audit
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.
De-Risked AI Product Launches
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.
Optimized Privacy-Utility Trade-off
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.
Competitive Differentiation in Tenders
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.
Structured Audit Engagement Tiers
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 |
Industries Requiring AI Privacy Audits
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.
Healthcare & Life Sciences
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.
Financial Services & FinTech
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.
Defense & Government Contracting
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.
Insurance & Actuarial Science
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.
E-Commerce & Retail Personalization
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.
Legal & Compliance Technology
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
Intelligent Analysis, Decision & Execution
We build AI systems for teams that need search across company data, workflow automation across tools, or AI features inside products and internal software.
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Search across company data
Give teams answers from docs, tickets, runbooks, and product data with sources and permissions.
Useful when people spend too long searching or get different answers from different systems.

Automate internal workflows
Use AI to route work, draft outputs, trigger actions, and keep approvals and logs in place.
Useful when repetitive work moves across multiple tools and teams.

Add AI to products and internal tools
Build assistants, guided actions, or decision support into the software your team or customers already use.
Useful when AI needs to be part of the product, not a separate tool.
Privacy-Preserving AI Audit FAQs
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
Prasad Kumkar
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.
Partnered with leading AI, data, and software stack.
How We Work
Custom AI workflows for your Business
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.
01
Review the use case
We understand the task, the users, and where AI can actually help.
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Pick the right approach
We define what needs search, automation, or product integration.
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Build the first useful version
We implement the part that proves the value first.
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Improve from there
We add the checks and visibility needed to keep it useful.
Read moreThe first call is a practical review of your use case and the right next step.
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