Mage Data has unveiled a new extension to its data protection platform called Data Security and Privacy for AI. This innovative tool is designed to assist businesses in safeguarding sensitive information throughout the entire lifecycle of artificial intelligence applications. The platform’s enhanced features are geared towards AI training environments, public generative-AI applications, custom AI agents, and embedded copilots. The aim is to enforce data protection policies at every stage in the AI process—from the moment data is introduced into AI systems, through processing and development, to the point where an AI system generates a response.
The company recognizes the challenges enterprises face in applying traditional data controls within AI environments. Sensitive information frequently traverses various channels such as extracts, notebooks, feature stores, evaluation datasets, prompts, and AI-generated outputs, making it difficult to manage. To address these challenges, Mage Data’s new offering focuses on five main protection areas: Training Data Guardrails, AI Usage Guardrails, Dynamic Data Masking for AI, AI Development Guardrails, and Activity Monitoring for AI. These tools work together to ensure comprehensive protection of sensitive data.
Training Data Guardrails are particularly crucial as they help identify sensitive data types like personally identifiable information (PII), protected health information (PHI), and non-public information (NPI) within both structured and unstructured datasets. Organizations can utilize these guardrails to mask data at its source, safeguard info as it enters AI pipelines, or implement controls through software development kits. AI Usage Guardrails, on the other hand, review employee prompts and file uploads to public generative-AI services, ensuring sensitive data is masked before leaving the user’s device.
Moreover, the Dynamic Data Masking for AI feature offers the capability to mask, redact, generalize, or block AI-generated responses based on the user’s identity, the request, and the response’s content. AI Development Guardrails provide organizations with the necessary controls when developing their own AI agents, while Mage Data’s SDKs and MCP Server manage tools and data access according to user permissions. Additionally, Activity Monitoring for AI logs AI interactions, detailing users, prompts, tools, sensitive data masking, overrides, and policy outcomes, while supporting reporting and alerting.
Mage Data CEO Rajesh Parthasarathy emphasizes the company’s commitment to extending existing data protection principles to the growing number of environments where enterprise information intersects with AI systems. The risks associated with employees potentially using public AI tools for handling sensitive information are significant, but CTO Anil Bhat notes that their solution aims to safeguard data without compelling companies to completely ban AI tools. This approach is intended to prevent employees from resorting to unmanaged services. Data Security and Privacy for AI is currently available, with Mage Data offering demonstrations and proof-of-concept deployments for organizations interested in assessing the technology.
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