A KGreen whitepaper on KVault — US Patented Split & Spread
Securing pharmaceutical data at the intersection of AI-driven operations, patient-data protection and cross-border data sovereignty.
Pharma enterprises in India operate across three converging pressure zones: AI-driven drug discovery and clinical operations, patient data classified as sensitive personal data under the DPDP Act 2023, and cross-border data flows subject to localisation mandates. KVault's Split & Spread cryptographic architecture addresses all three simultaneously — a capability most enterprise DRM and vault platforms do not.
Drug discovery, clinical trials and pharmacovigilance increasingly run on cloud AI.
Patient health, genetic and consent data are sensitive personal data.
Cross-border data flows subject to localisation mandates.
Clinical trial datasets contain health data, genetic markers and patient consent records — all classified as sensitive personal data under DPDP Section 2(t). KVault enforces data-at-rest and data-in-transit encryption with cryptographic key custody retained by the pharma principal. When AI platforms (e.g. Microsoft Azure AI, AWS SageMaker) process this data, the processing node never holds reconstructed plaintext without authorised key release — directly addressing DPDP fiduciary-accountability requirements.
Pharma AI pipelines consume patient cohort data, adverse-event records and longitudinal health outcomes. Under DPDP, this requires consent traceability and purpose limitation. KVault's Split & Spread architecture ensures that even if the AI infrastructure layer is compromised, no single node exposes a reconstructable dataset. The encryption boundary is enforced at the data-asset level, not the infrastructure perimeter.
New Drug Applications (NDAs) and Clinical Study Reports submitted to CDSCO and global regulators require tamper-evident audit trails. KVault provides cryptographic integrity verification on submission packages, ensuring post-submission non-repudiation — audit-ready evidence under both DPDP and international GxP compliance standards.
Pharma supply chains include Contract Research and Contract Manufacturing Organisations with access to IP-sensitive and patient-linked data. KVault enforces cryptographic access segmentation — each CRO/CMO operates within a defined key boundary, preventing lateral data access across the supply chain. DPDP data-processor obligations under Section 8 are met with verifiable controls, not contractual assertions alone.
AI-driven pharmacovigilance systems aggregate adverse-drug-reaction data across geographies. DPDP mandates that data fiduciaries retain accountability for processing even when AI systems operate autonomously. KVault's key-custody architecture ensures the pharma entity — not the AI platform vendor — controls access authorisation, preserving the fiduciary-processor hierarchy mandated under DPDP.
| Use Case | DPDP Obligation | KVault Control |
|---|---|---|
| Clinical Trial Data | Sensitive personal data (Sec 2(t)); fiduciary accountability | Data-at-rest & in-transit encryption; key custody with principal |
| AI Model Training | Consent traceability; purpose limitation | Asset-level encryption; no single node reconstructable |
| Regulatory Submission | Audit-ready evidence (DPDP & GxP) | Cryptographic integrity & non-repudiation |
| CRO / CMO Access | Data-processor obligations (Sec 8) | Cryptographic access segmentation per key boundary |
| Pharmacovigilance | Fiduciary retains accountability | Key custody with the pharma entity, not the AI vendor |
AI inference pipelines introduce a structural vulnerability: the model training and inference layer operates outside the pharma entity's direct control when cloud-hosted. KVault repositions the encryption boundary from the infrastructure perimeter to the data asset itself.
Once inside the AI infrastructure perimeter, data is exposed — and that perimeter sits outside the pharma entity's direct control.
The AI platform processes encrypted inputs; decryption occurs only at the authorised endpoint — aligned with the emerging Confidential AI posture in NIST AI RMF and ISO 42001.
The DPDP Act 2023 establishes personal liability exposure for significant data fiduciaries in the pharma segment. Boards approving AI adoption in clinical and commercial operations without corresponding data-protection architecture carry residual accountability. KVault provides the cryptographic control layer that converts regulatory obligation into demonstrable, audit-ready compliance posture — reducing both regulatory-penalty exposure and reputational risk in patient-facing operations.
