Nadhi Audit is a desktop application that performs comprehensive security audits of healthcare software entirely on the user's own machine, ensuring no patient data ever leaves the premises. It combines deterministic code checks with a locally-run fine-tuned AI model to identify vulnerabilities, verify dependencies against an offline CVE database, and generate compliance-ready reports for regulators. The tool is designed for hospital systems and healthcare software teams that need to audit code without introducing a new data processor.
- Artificial Intelligence
- AI Agents
- Cybersecurity
- Developer Tools
- Healthcare Technology
- Regulatory & Compliance Technology
- Software Only
Funding
Founders
Product
Problem
Healthcare software contains sensitive patient data, and sending its source code to cloud-based AI auditors creates a new data processor relationship, requiring business associate agreements and often violating hospital policies. This forces healthcare organizations to choose between comprehensive security audits and strict data privacy compliance.
Solution
Nadhi Audit performs the entire security audit process locally on the user's own machine, using a fine-tuned AI model that never transmits data externally. The tool runs deterministic checks first, then uses its locally-run model to analyze findings in context, map them to CWE classifications, and generate patches. It also downloads and maintains a complete CVE database locally, so all dependency comparisons happen offline. The final output is a dated PDF report that cites specific HIPAA, DPDP, or GDPR sections for each finding, making it regulator-ready.
Target Audience
Primary customers are hospital systems, EHR vendors, lab software companies, and telehealth providers that need to audit healthcare code while maintaining strict data sovereignty and compliance with HIPAA, DPDP, and GDPR regulations.
Features
- Locally-run fine-tuned AI model (Nadhi_Audit_FT.gguf) quantized for Apple Silicon Metal GPU, with no cloud dependency
- Sixteen correlated checks covering source patterns, row-level security, supply chain, and deployed backend verification
- Offline CVE database matching for npm, pnpm, yarn, bun, uv, poetry, and composer lockfiles
- Deterministic fix verifier that re-parses patches with real compilers and re-runs the original rule before reporting success
- Optional read-only deployed backend analysis that compares intended migrations against live enforcement
- Adversarial training data (13,000 conversations) and 30 behavior tracks to ensure reliable tool calling and honest reporting