Mage Data has unveiled a new extension to its data protection platform, aimed at enhancing data security and privacy throughout the artificial intelligence (AI) lifecycle. This new feature, Data Security and Privacy for AI, is crafted to help businesses safeguard sensitive information within AI training environments, public generative-AI applications, custom AI agents, and embedded copilots. The platform ensures that data protection policies are enforced at every stage—from before data enters an AI system to during the processing and development phase, and finally when an AI system generates a response.
Traditional data controls in enterprise settings often struggle to adapt to AI environments due to the movement of sensitive information across various channels like extracts, notebooks, feature stores, evaluation datasets, prompts, and AI-generated responses. Addressing this challenge, Mage Data introduces comprehensive protection across five critical areas. These include Training Data Guardrails, which identify and manage sensitive information such as personally identifiable information (PII), protected health information (PHI), and non-public information (NPI) across diverse datasets. Organizations can leverage these guardrails to mask data at its source, secure data entering AI pipelines, or enforce controls via software development kits.
The platform’s AI Usage Guardrails examine employee prompts and file uploads to public generative-AI services, ensuring sensitive information is masked before it leaves a user’s device. Moreover, Dynamic Data Masking for AI can modify AI-generated responses by masking, redacting, generalizing, or blocking them based on user context, request specifics, and the response content. AI Development Guardrails offer control measures for organizations developing their own AI agents, with Mage Data’s SDKs and MCP Server restricting tools and data access according to user permissions. Additionally, the Activity Monitoring feature records AI interactions, providing reporting and alerting capabilities for users, prompts, tools, and policy outcomes.
By allowing organizations to extend existing Mage Data policies to AI workloads, the need for a separate AI-specific policy framework is eliminated. According to Rajesh Parthasarathy, Mage Data’s CEO and founder, the strategy emphasizes applying established data protection principles to the expanding array of environments where enterprise data interacts with AI systems. The company also cautioned against the risks associated with employees using public AI tools to handle sensitive information. Anil Bhat, CTO and Senior Vice President, indicated that their approach is designed to secure data without compelling enterprises to entirely block AI tools, which could inadvertently push employees towards unmanaged services.
Available now, Data Security and Privacy for AI offers organizations the opportunity to test the technology through demonstrations and proof-of-concept deployments. Mage Data continues to provide detailed product information and evaluations for businesses interested in adopting their innovative security solutions.
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