Mage Data has unveiled a new extension to its data protection platform, aimed at safeguarding sensitive information throughout various stages of artificial intelligence applications. Titled Data Security and Privacy for AI, the enhancement seeks to address the complexities of securing data within AI environments, which include AI training setups, public generative-AI applications, custom AI agents, and embedded copilots. This platform ensures data protection strategies are applied at every critical point—from the initial input into AI systems, through processing and development stages, and finally, to the generation of AI responses.
Traditional enterprise data controls often fall short in AI settings due to the dynamic movement of sensitive data across different components such as extracts, notebooks, feature stores, evaluation datasets, and AI-generated outputs. Mage Data aims to tackle these challenges with a comprehensive offering that spans five key protection areas. These include Training Data Guardrails, which identify and manage sensitive data like personally identifiable information (PII), protected health information (PHI), and non-public information (NPI) in both structured and unstructured formats. The system can mask data at its origin, safeguard it as it enters AI processes, or implement controls via software development kits.
To ensure secure AI usage, the platform provides AI Usage Guardrails that scrutinize employee prompts and file uploads to public generative-AI services, masking sensitive data before it leaves the user’s device. Dynamic Data Masking for AI offers the ability to mask, redact, generalize, or block AI-generated responses based on user credentials and the information involved. Additionally, AI Development Guardrails deliver specific controls for organizations developing their AI agents, using Mage Data’s SDKs and MCP Server to limit tool and data access according to user permissions.
The platform also incorporates Activity Monitoring for AI, which logs all AI interactions, including user activity, prompts, tool usage, and any sensitive data actions, providing comprehensive reporting and alerting mechanisms. This integration allows businesses to expand their existing Mage Data policies to AI applications, eliminating the need for distinct policy frameworks for AI. Rajesh Parthasarathy, Mage Data’s CEO and founder, emphasized the company’s strategy of applying existing data protection principles to the expanding environments where enterprise data interfaces with AI systems.
Highlighting the potential risks of employees using public AI tools with sensitive information, the company offers a solution that protects data without completely shutting off AI tools, which could inadvertently encourage the use of unmanaged services. Anil Bhat, Mage Data’s CTO and Senior Vice President, explained that their approach is designed to balance security with utility. The new Data Security and Privacy for AI solution is now available, and Mage Data is offering demonstrations and proof-of-concept deployments for organizations interested in evaluating this technology.
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