Securing ML Datasets and @TensorFlow Trained AI Models with Bloombase StoreSafe PQC Encryption

Bloombase StoreSafe Intelligent Storage Firewall delivers high-bandwidth, application-transparent, agentless post-quantum cryptography (#PQC) #encryption security of ML training data and trained AI model using @TensorFlow, an open-source software library for machine learning and artificial intelligence. By leveraging Bloombase StoreSafe, organizations are able to mitigate data exfiltration vulnerabilities and meet data privacy compliance requirements with the sensitive business data and trained model of their AI applications easily and cost-effectively.

In this video, we will show you how Bloombase StoreSafe Intelligent Storage Firewall provides on-the-fly encryption protection of dataset for machine learning alongside the trained AI model by utilizing #TensorFlow, allowing the text classification application to be able to deliver the large language model #LLM as if they were in the clear, and provide AI inferencing services and generative AI capabilities based on the encrypted model, with zero operational impact to AI developers, system administrators and operators, and helps organizations achieve #GDPR, and a variety of information confidentiality mandates.

The dataset used in this demo has been adapted from:

For more, check out the links below:

#AI #ML #GenerativeAI #GenAI #Anaconda #jupyterlab #jupyternotebook #opensource #Google #DLP #DataLossPrevention #DataProtection #DataExfiltration