LLMs are becoming increasingly popular in various sectors, as they simplify processes and boost productivity. However, most of the models available have been trained on publicly available data, making them unsuitable for enterprise use. To make LLMs work in specific enterprise settings, companies need to train them on their internal knowledge. This is where Skyflow’s GPT privacy vault comes in.
Delivered via API, the solution establishes a secure environment, allowing users to define their sensitive data dictionary and have that information protected at all stages of the model lifecycle. Once fully integrated, the vault uses the dictionary and automatically redacts or tokenizes the chosen information as it flows through GPT — without lessening the value of the output in any way.
“Skyflow’s proprietary polymorphic encryption technique enables the model to seamlessly handle protected data as if it were plaintext,” said Anshu Sharma, Skyflow cofounder and CEO. “It will protect all sensitive data flowing into GPT models and only reveal sensitive information to authorized parties once it has been processed by the model and returned.”
The offering can be integrated into an enterprise’s existing data infrastructure. It also supports multi-party training, where two or more entities could share anonymized datasets and train models to unlock insights.
Multiple use cases
The GPT privacy vault is already helping protect sensitive clinical trial data in the drug development cycle, as well as customer data used by travel platforms for improving customer experiences. IBM is also a customer of Skyflow and has been using the company’s products to de-identify sensitive information in large datasets before analyzing it via AI/ML.
While there are alternative approaches to the problem of privacy, such as creating a private cloud environment for running individual models or a private instance of ChatGPT, those could prove to be far more expensive than Skyflow’s solution.
Currently, in the data privacy and encryption space, the company competes with players like Immuta, Securiti, Vaultree, Privitar, and Basis Theory.
Skyflow, a popular blockchain infrastructure company, recently introduced a new ‘Privacy Vault’ to build and launch permissionless and privacy-preserving ledger networks called LLMs (Ledger Less Networks). This is a significant development in the blockchain sector and could drive the sector forward in terms of privacy and data security.
Through the use of decentralized ledger technology, LLMs are designed to provide users with increased access control, privacy protection, and added security for shared assets and data. As a result, users can remain anonymous while participating in platform transactions and exchanging personal data.
The Privacy Vault includes a set of powerful cryptography and privacy-focused protocols which establish a secure bridge between the user and the network. These protocols are designed to ensure that data is never recorded in plain text on the network. This means that data is encrypted and diffused before it is shared with any other party, making it impossible to intercept and access without the user’s explicit permission.
In addition to the privacy benefits, the use of LLMs also provides users with improved scalability, speed, and cost efficiency. The use of decentralized ledger technology also enables communication and protocol coordination between multiple parties without the requirement of a single gateway or trusted intermediary.
The Privacy Vault is the first of its kind and is being hailed as a major milestone for the distributed ledger technology sector. With the launch of this system, many are hoping that it will start a new era of privacy and security for users and businesses.
Skyflow has reinforced their commitment to providing customers with secure and private ledger technologies, making them a leader in the field. With their Privacy Vault, users can now have increased control over their data, setting a new industry standard for data privacy and security.
