(SeaPRwire) – BURLINGAME, CA – 24/03/2026 – (SeaPRwire) – As enterprises increasingly adopt artificial intelligence within cloud environments, concerns regarding data exposure during processing persist as a significant obstacle. To address this challenge, DataKrypto has launched its FHEnom for AI
solution on Google Cloud Marketplace, representing a major stride towards enabling secure, end-to-end encrypted AI operations at scale.

The availability of FHEnom for AI on Google Cloud Marketplace follows DataKrypto’s involvement in the Google Cloud ISV Startup Springboard program, indicating an enhanced partnership between the two companies. This integration empowers organizations to deploy AI workloads with improved security assurances while utilizing Google Cloud’s extensive global infrastructure.
At the heart of DataKrypto’s offering is a continuous encryption framework designed to eliminate the “cleartext gap” that commonly occurs when AI systems process data. By integrating fully homomorphic encryption (FHE) with Trusted Execution Environments (TEEs), FHEnom for AI facilitates computations directly on encrypted data, ensuring that sensitive information, models, and outputs remain protected throughout the entire AI lifecycle.
This approach introduces a novel paradigm for Confidential AI, where encryption is not applied sporadically but is an intrinsic characteristic of the architecture. Consequently, enterprises can maintain control over their data from ingestion and model training through to inference and real-time processing, even within shared or cloud-based environments.
With the solution now accessible through Google Cloud Marketplace, organizations can more confidently implement AI-driven applications while adhering to strict regulatory and compliance mandates. The platform supports secure workflows aligned with frameworks such as HIPAA and GDPR, making it suitable for industries that handle highly sensitive data.
FHEnom for AI is engineered to tackle persistent issues that have hindered enterprise adoption of cloud-based AI, particularly in regulated sectors. By leveraging Google Cloud’s scalable infrastructure, the solution transforms the cloud into a confidential execution environment where both data and models remain continuously encrypted—even in situations where underlying systems might be compromised.
Key features of the platform include the ability to perform real-time AI inference on encrypted data without significant performance degradation, support for a TEE-agnostic architecture compatible with hardware technologies like Intel TDX and AMD SEV, and seamless integration into existing development pipelines. This enables organizations to deploy Confidential AI solutions without needing extensive modifications to their current systems.
Industry experts observe that the combination of advanced cryptographic methods and cloud-native scalability signifies a substantial advancement in AI security. By embedding encryption directly into the computation process, DataKrypto’s solution helps reduce operational complexity while bolstering confidence in AI outcomes.
Through its participation in the Google Cloud ISV Startup Springboard program, DataKrypto has been able to expedite the development and deployment of its technology. This program is designed to assist emerging companies in leveraging Google Cloud’s AI capabilities, enabling them to deliver innovative solutions to enterprise clients.
FHEnom for AI is now available for deployment, providing organizations with a means to build and scale AI systems with enhanced security, compliance, and operational assurance.
About DataKrypto
DataKrypto develops cryptographic infrastructure for Confidential AI, focusing on eliminating vulnerabilities in conventional AI processing workflows. Its primary solution, FHEnom for AI
, ensures that data, models, and computational results are always encrypted, enabling secure AI operations without exposing sensitive information. By making encryption a continuous and inherent aspect of AI architecture, DataKrypto aims to transform how organizations approach data protection in intelligent systems.
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