Keeping Secrets Safe in the World of AI with Confidential Computing
As the use of Artificial Intelligence (AI) continues to grow, so too does the importance of keeping sensitive data and confidential models safe from prying eyes. Enter confidential computing, a powerful technology that helps safeguard data and AI models from unauthorized access. In this blog post, we’ll explore the world of confidential AI models and discuss how confidential computing can be leveraged to keep your AI secrets safe. Whether you’re a data scientist or an AI enthusiast, understanding the importance of confidential AI models is crucial in today’s data-driven world. So, let’s dive in and discover how you can keep your confidential AI models secure with confidential computing.
Confidential Compute (CC) is a powerful new paradigm, embarking on the cloud computing space. In simple terms, executes applications in a secure and trustworthy, and encrypted black box, so that the cloud provider cannot see any of the code and/or data being processed. The truly innovative aspect of the technology is that not only the storage (“data in rest”) or transport (“data in transit”), but for the first time also the processing of the data is always intransparent (“data in use”). This isolates the data processing from the operating system and the applications running on it. During processing, neither the (cloud) service provider, administrator, nor a (compromising) third party has access to the data.
This type of technology is becoming increasingly important as more and more companies look to leverage the power of AI and machine learning to gain insights and make powerful predictions from their data.
The benefits of CC for leveraging the power of AI
One of the key benefits of confidential computing is that it allows organizations to process sensitive data without having to worry about it being compromised or stolen. This is because the data is encrypted and only decrypted within the secure enclave, this “black box”, making it virtually impossible for an outside party to access it. This is particularly important for organizations that deal with sensitive information such as personal data, financial transactions, or confidential business information.
Another benefit of confidential computing is that it allows organizations to share data with other organizations or third-party vendors without having to worry about the data being compromised. This is because the data is encrypted and can only be decrypted within the secure enclave, making it virtually impossible for an outside party to access it. This is particularly important for organizations that need to share data with other organizations for research or collaboration purposes.
One of the most promising areas for the application of confidential computing is in the field of AI and machine learning. With the increasing amount of data being generated and the growing complexity of AI models, it is becoming increasingly important to ensure that sensitive data is protected. Because by leveraging confidential Computing, organizations across industries can ensure complete security for their data. Unlike traditional encryption methods that can leave data exposed in memory, secure enclaves based on Intel® SGX technology offer a protected environment for execution, with a direct connection to the hardware, effectively blocking unauthorized access to confidential customer data.
How organizations can leverage CC for AI models
There are several ways that organizations can leverage confidential computing to improve their AI and machine learning models.
One way to leverage confidential computing is through the use of “federated learning” algorithms. These algorithms allow organizations to train AI models on data that is distributed across multiple devices, without having to move the data to a central location. This allows organizations to train AI models on sensitive data without having to worry about the data being compromised. This could be particularly interesting within the fintech industry, for instance when it comes to efforts against money laundering. This approach would be based on an AI-based money framework, utilizing federated learning. It involves different companies that work collaboratively to obtain a shared prediction model. Federated learning allows the data to be kept in local environments, such as banks’ internal systems. They upload data to a centralized node where AI algorithms provide risk assessments, allowing banks and other financial institutions to spot potential risk candidates. Furthermore, banks could share and use each other’s transaction data to build predictive models and create an anti-money laundering system. They can do all of this without exposing sensitive data to their competitors.
Furthermore, organizations could leverage confidential computing to improve their AI and machine learning models by combining data sets from different institutions and organizations, without exchanging the actual sensitive data. While using secure enclaves before sending the data sets, data owners can ensure that any AI model based on these data sets is not exposing the actual users’ private information, but rather only the encrypted data sent.
For example, multiple hospitals can combine their data to train AI for detecting diseases, say, given pictures from CT scans. Exchanging data for research purposes would not come at the detriment of data privacy, though. Working within enclaive’s Confidential Containers ensures that patients’ data remains confidential during each step of the process. This way, the patient’s privacy is protected and hospitals or other data owners (i.e. research institutions) remain in control of their valuable data.
In conclusion, confidential computing may be a relatively new technology, but it is gaining a lot of attraction across industries. It’s also becoming increasingly important as more and more organizations look to leverage the power of AI and machine learning to gain powerful insights and make predictions from their data. By enclosing sensitive data and computations in a secure enclave, confidential computing provides a way to protect sensitive data and computations from being compromised. Additionally, the technology enables organizations to share data with other organizations or third-party vendors without having to worry about the data being compromised. With the increasing amount of data being generated and the growing complexity of AI models, confidential computing provides a way to ensure that sensitive data is protected and to improve AI and machine learning models.