Keeper Security
Password security is the foundation of cybersecurity. Keeper's powerful password security platform will protect your business from cyberthreats and data breaches related to passwords.
Research shows that 81% of data breaches can be attributed to weak passwords. Password security platforms are an affordable and easy way for companies to address the root cause of most data breaches. Your business can significantly reduce the risk of data breaches by implementing Keeper.
Keeper creates strong passwords for all websites and apps, then secures them on all devices. Each employee receives a private vault to store and manage their passwords, credentials and files, as well as private client data. Employees will save time and frustration by not having to remember, reset, reuse, or remember passwords. Industry compliance is achieved through strict and customizable role-based access controls. This includes 2FA, usage auditing, and event reporting.
Learn more
Venn
Venn is revolutionizing how businesses enable BYOD workforces, removing the burden of buying and securing laptops or dealing with virtual desktops. Our patented technology provides companies with a new approach to securing remote employees and contractors working on unmanaged computers. With Venn’s Blue Border™ software, work lives in a company-controlled Secure Enclave installed on the user’s computer, enabling IT teams to secure company data while ensuring end-user privacy. Over 700 organizations, including Fidelity, Guardian, and Voya, trust Venn to meet FINRA, SEC, NAIC, and SOC 2 standards. Learn more at venn.com.
Learn more
Google Cloud Confidential VMs
Google Cloud's Confidential Computing offers hardware-based Trusted Execution Environments (TEEs) that encrypt data while it is actively being used, thus completing the encryption process for data both at rest and in transit. This suite includes Confidential VMs, which utilize AMD SEV, SEV-SNP, Intel TDX, and NVIDIA confidential GPUs, alongside Confidential Space facilitating secure multi-party data sharing, Google Cloud Attestation, and split-trust encryption tools. Confidential VMs are designed to support workloads within Compute Engine and are applicable across various services such as Dataproc, Dataflow, GKE, and Gemini Enterprise Agent Platform Notebooks. The underlying architecture guarantees that memory is encrypted during runtime, isolates workloads from the host operating system and hypervisor, and includes attestation features that provide customers with proof of operation within a secure enclave. Use cases are diverse, spanning confidential analytics, federated learning in sectors like healthcare and finance, generative AI model deployment, and collaborative data sharing in supply chains. Ultimately, this innovative approach minimizes the trust boundary to only the guest application rather than the entire computing environment, enhancing overall security and privacy for sensitive workloads.
Learn more
Fortanix Confidential AI
Fortanix Confidential AI presents a comprehensive platform that allows data teams to handle sensitive datasets and deploy AI/ML models exclusively within secure computing environments, integrating managed infrastructure, software, and workflow orchestration to uphold privacy compliance across organizations. This service features on-demand infrastructure driven by the high-performance Intel Ice Lake third-generation scalable Xeon processors, enabling the execution of AI frameworks within Intel SGX and other enclave technologies while ensuring no external visibility. Moreover, it offers hardware-backed execution proofs and comprehensive audit logs to meet rigorous regulatory standards, safeguarding every aspect of the MLOps pipeline, from data ingestion through Amazon S3 connectors or local uploads to model training, inference, and fine-tuning, while also ensuring compatibility across a wide range of models. By leveraging this platform, organizations can significantly enhance their ability to manage sensitive information responsibly while advancing their AI initiatives.
Learn more
