AI Governance Layer for Financial Data

Letting banks and fintechs run AI models on customer data without ever exposing it. Powered by Trusted Execution Environments (TEE).

DPDP ActRBI GuidelinesConfidential Computing

The Problem

Every institution wants AI on customer data. None of them are allowed to risk it.

How It Works

Governance by design: data and model, both inside the TEE

Encrypted customer data and the AI or risk model run together, encrypted, inside the enclave — producing a verified decision or score for the lender, regulator or auditor.

Customer financial data flows into a Trusted Execution Environment, where the AI model runs inside encrypted memory, producing an auditable output only — raw data is never decrypted outside the boundary, not even to the vendor.

No raw data or model IP ever leaves the enclave in the clear

Every inference is logged, attested & independently verifiable

Regulators / auditors can verify compliance without seeing raw data

Under The Hood

From encrypted input to verified output, in five steps

Encrypt

Data & code encrypted at the source

Load

Moved into a hardware-isolated enclave (TEE)

Attest

Remote attestation verifies enclave integrity first

Compute

Runs on isolated memory — invisible to host OS/admin

Release

Only the approved output is decrypted & shared

Runs on silicon-level isolation: Intel SGX / TDX · AMD SEV-SNP · ARM CCA · NVIDIA Confidential Computing GPUs

Flexible By Design

One confidential compute layer, deployed however you need it

On Public Cloud

Run inside the confidential-computing offering of any major cloud

Azure Confidential ComputingDCsv3/v5 VMs, confidential containers
AWS Nitro EnclavesIsolated compute within EC2
Google Confidential VMs / SpaceConfidential GKE & data clean rooms

On Your Own Hardware

Open-source frameworks, run in your own data centre

Gramine / Open Enclave SDKRun unmodified apps inside an enclave
EGo & EnarxDeveloper tooling for TEE application builds
Full data sovereigntyOn-prem, air-gapped or private-cloud options

PryvX abstracts the hardware & cloud layer — same policies, same attestation, same guarantees, wherever you run it.

How It Compares

Confidential computing is one of several PETs — each with different tradeoffs

Privacy Tech	What it protects	Performance	Best fit
TEE / Confidential Computing	Raw data — hardware-isolated	Near-native	General-purpose AI/ML on joint, sensitive data
Homomorphic Encryption (HE)	Data stays encrypted, always	Slow (heavy overhead)	High-value compute without hardware trust
Federated Learning (FL)	Raw data — stays on each party's infra	Fast, but network-bound	Joint model training without moving data
Differential Privacy (DP)	Individual records, via noise	Fast	Aggregate analytics, some accuracy loss OK
Secure Multi-Party Computation (MPC)	Inputs from distrustful parties	Moderate – slow	Joint computation with no single trusted party

PryvX is built TEE-first — the only PET that runs full, unmodified AI models on raw sensitive data at near-native speed.

“How do I let my teams use LLMs on customer data without violating DPDP Act or RBI guidelines?”

TEE is the mechanism. Compliance-enabled AI adoption is the answer.