Private AI Use Cases and Configuration
RAG Pipeline Design That Prevents Document Exfiltration to External APIs
Four distinct attack paths let documents escape RAG systems, each requiring different defenses.
Preston Nardone
Reporter · · 14 min read
Private AI Review — Page 3
Private AI Use Cases and Configuration
Air-Gapped AI Inference for Classified and Sensitive Government Environments
Federal agencies are racing to deploy AI faster than their infrastructure was built to handle it.
Kian Vance12 min read
Private AI Use Cases and Configuration
Preventing Sensitive Data Leakage in Enterprise AI Copilot Configurations
Audit permissions and classify data before deploying AI copilots to your organization.
Anouk Mercer12 min read
Private AI Use Cases and Configuration
HIPAA-Compliant AI Inference Architecture for Clinical NLP
Architectural controls must protect PHI inside AI pipelines, not just around them.
Kian Vance16 min read
Cryptographic Primitives for Private AI
Post-Quantum Cryptography Migration for AI Inference APIs
Quantum computers could decrypt your inference traffic today if captured now.
Lorena Sundqvist12 min read
Cryptographic Primitives for Private AI
Private Set Intersection Protocols in Healthcare AI Data Pipelines
Cryptography lets rival hospitals find shared patients without exposing their own data.
Preston Nardone14 min read
Cryptographic Primitives for Private AI
Secure Multi-Party Computation Protocols for Private ML Inference
Math-based protocols let AI models process data without ever seeing it in plain form.
Kian Vance16 min read
Cryptographic Primitives for Private AI
CKKS Parameter Selection for Private Neural Network Inference
Homomorphic encryption's four interdependent parameters demand coordination, not checklist defaults.
Preston Nardone13 min read
Cryptographic Primitives for Private AI
Homomorphic Encryption Schemes for ML Inference Comparison
Choosing the right homomorphic encryption scheme determines your ML inference speed and cost.
Anouk Mercer12 min read
Cryptographic Primitives for Private AI
Randomized Response and Local Differential Privacy in On-Device AI
Privacy is added on devices before data leaves, making breaches irrelevant.
Kian Vance15 min read
Cryptographic Primitives for Private AI
Differential Privacy Mechanisms in Large Language Model Fine-Tuning
Differential privacy reduces model leakage but degrades accuracy at scale.
Kian Vance11 min read
Trusted Execution Environments (TEEs) and Secure Enclaves
ARM CCA Realms as a TEE Model for Mobile and Edge AI
Arm's new Realm architecture brings cloud-grade confidential compute to edge AI workloads.
Preston Nardone12 min read
