Overview
Enterprises are racing to scale AI, but the most powerful models rely on highly sensitive data—health records, financial transactions, and citizen information. Traditional security protects data at rest and in transit, but it fails the moment data is decrypted for computation, creating the biggest risk of breaches, penalties, and lost trust.
With 64% of enterprises citing data leakage as their top cloud concern and cybercrime losses hitting $10.2B in 2022, the stakes are higher than ever.
Our white paper, Homomorphic Encryption in Cloud AI Pipelines: Privacy-Preserving ML Without Data Exposure, shows how homomorphic encryption (HE) enables AI to run directly on encrypted data ensuring privacy without sacrificing performance.
Why Download This White Paper?
Inside, you’ll uncover:
- The Technology Foundations – How homomorphic encryption enables AI models to process ciphertext without decryption.
- Performance Benchmarks – Detailed analysis of trade-offs, overheads, and how advances in GPU, FPGA, and ASIC acceleration are closing the performance gap.
- Compliance Alignment – How HE supports frameworks like GDPR, HIPAA, and PCI DSS, reducing breach notification risks.
- Practical Use Cases – Finance (encrypted fraud detection), healthcare (secure genome analysis), government (election integrity), and IoT (private edge analytics).
- Market Insights – Why the global HE market is projected to grow at up to 20% CAGR, and how leading cloud providers like AWS, Azure, and GCP are investing in HE services.
Who Should Read It?
This paper is designed for leaders who cannot compromise on security while driving innovation:
- CIOs & CISOs are architecting zero-trust data strategies.
- Data & AI Leaders are tasked with deploying AI in highly regulated sectors.
- Enterprise Architects navigating secure cloud transformations.
- Healthcare, Finance, and Public Sector Executives are under growing compliance scrutiny.
Why Now?
The quickness is clear: with 93% of U.S. households online and a surge in AI adoption across industries, regulators are tightening controls on data-in-use encryption. Early adopters of homomorphic encryption are already demonstrating a competitive advantage in detecting fraud, accelerating clinical research, and enabling AI collaborations across borders without exposing sensitive datasets.
Organizations that wait risk not just falling behind technologically, but also facing regulatory and reputational setbacks. Those who move early will define the standards for privacy-preserving AI.