Securing data sharing in Semiconductor value chain & Ecosystem
As the sensitivity of materials is getting greater and greater, the ecosystem across the supply chain is stepping up to help facilitate more sharing of data. SafeLiShare helps govern app access and data sharing with fully encrypted confidential computing.
Policy-Enforced, Encrypted, Secure Data Platform
Run you data and application workloads with complete data privacy, residency, sovereignty and compliance control transparently. Encrypt all customer’s data in use, in transit and at rest.
Enable efficient communication and collaboration between different stakeholders in the semiconductor industry, such as manufacturers, suppliers, researchers, and customers. Ensure consistency and accuracy in the representation of semiconductor data across different platforms or applications when collaborating in secure enclave federated workflows.
Safelishare use cases are divided into uni-party and multi-party confidential data access. When applicable, instead of moving data, we move models to the data, train models safely at secure enclaves, then aggregate the models with policy-enforced output access. The solution maximizes privacy and security without data transfer latency and compute costs.
High-volume, high-velocity data from multiple sources need a evolutional security approach
TEE IaC ready Between the Cloud and the Edge
APIs and CLI toolkits are available to common DevOps, as are pre-built automation templates. Ready to deploy solutions for data scientists to create and deploy hardened infrastructure as code easily between the edge and the cloud.
Move Compute to Data
SafeLiShare minimizes data transfer and compute costs, privacy risks, latency issues, and cloud service dependency all with policy enforced access control through the data pipeline.
Empower Federated Learning
SafeLiShare is cloud agnostic and portable. Federated learning capabilities allow multiple parties to contribute their data while maintaining privacy and security.
Fine-Grained Auditing & Reporting
SafeLiShare traces all data and algorithmic models in TEEs with the immutable, tamper-protected, and append-only logs without exception.
“Confidential computing put zero trust in practice. It helps seal the attack surface as dynamic applications and distributed data are exposed in system memory at runtime.”
John Kindervag, Creator
Zero Trust
People also ask
Can SafeLiShare’s Data Platform secure semiconductor value chain?
Why is SFTP or other encryption not enough for data transfer and sharing?
How can CHIPS for America Act & FABS Act benefit the adoption of Confidential Computing?
Additional materials
February 21, 2024
RSAC 2024: What’s New
SafeLiShare unveils groundbreaking AI-powered solutions: the AI Sandbox and Privacy Guard in RSAC 2024
February 21, 2024
Cloud Data Breach Lifecycle Explained
During the data life cycle, sensitive information may be exposed to vulnerabilities in transfer, storage, and processing activities.
February 21, 2024
Bring Compute to Data
Predicting cloud data egress costs can be a daunting task, often leading to unexpected expenses post-collaboration and inference.
February 21, 2024
Zero Trust and LLM: Better Together
Cloud analytics inference and Zero Trust security principles are synergistic components that significantly enhance data-driven decision-making and cybersecurity resilience.
Experience secure collaborative data sharing today.
Maximize accessibility and monetization of sensitive, regulated, or confidential data without compromise.