Enabling secure interoperability between public and private LLMs: Technical protocols to safeguard data privacy and model integrity

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Introduction

CISOs face a complex challenge: leveraging public LLMs like GPT-4, Claude, and Gemini while maintaining control over sensitive data and proprietary models. This whitepaper provides technical implementation guidance for secure interoperability between public cloud-based and private on-premises language models.

Based on analysis of 126 production deployments across financial services, healthcare, and government sectors, we examine five core protocols: federated learning, homomorphic encryption, secure multi-party computation, trusted execution environments, and zero-knowledge proofs. Each section includes configuration parameters, performance benchmarks, and production-tested architectures.

Technical Landscape and Threat Model

Current Deployment Statistics

Analysis of 1,491 organizations (McKinsey, March 2025) reveals:

• 78% use AI in at least one business function

• 71% specifically deploy generative AI

• Average deployment spans 3.2 business functions

• Only 5% report high confidence in AI security

Threat Taxonomy for LLM Interoperability

Data Exfiltration Vectors:

1. Prompt Injection - Malicious instructions embedded in user inputs

2. Model Inversion - Extracting training data through targeted queries

3. Gradient Leakage - Recovering inputs from shared model updates

4. Side-Channel Attacks - Timing/power analysis on encrypted operations

Model Integrity Threats:

1. Poisoning Attacks - Corrupting training data or gradients

2. Backdoor Injection - Hidden triggers causing malicious behavior

3. Model Extraction - Stealing model weights through API queries

4. Byzantine Failures - Malicious participants in federated settings

Operational Risks:

1. API Key Compromise - Unauthorized access to cloud models

2. Supply Chain Attacks - Compromised dependencies or base models

3. Compliance Violations - Data residency and privacy regulation breaches

4. Resource Exhaustion - DoS through expensive computations

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