Mage Data has launched a new feature, Data Security and Privacy for AI, enhancing its data protection platform to safeguard sensitive information throughout the AI lifecycle. The platform’s latest capabilities are designed to secure data within AI training environments, public generative-AI applications, custom AI agents, and embedded copilots. By applying data protection policies at various stages—before data enters an AI system, during processing and development, and when AI generates a response—Mage Data aims to offer comprehensive protection.
The company highlights the challenge of applying traditional enterprise data controls to AI environments, where sensitive information can traverse through extracts, notebooks, feature stores, evaluation datasets, prompts, and AI-generated responses. To address these challenges, the new offering provides protection in five key areas: Training Data Guardrails, AI Usage Guardrails, Dynamic Data Masking for AI, AI Development Guardrails, and Activity Monitoring for AI. These areas ensure sensitive data like personally identifiable information (PII), protected health information (PHI), and non-public information (NPI) are identified and protected at various stages of AI interaction.
Mage Data has implemented features such as Training Data Guardrails to identify and mask sensitive data at its source, and AI Usage Guardrails to inspect and potentially mask sensitive information in employee prompts and file uploads before they reach public AI services. Additionally, Dynamic Data Masking for AI is designed to tailor AI-generated responses based on user permissions and request context, while AI Development Guardrails provide organizations developing AI agents with the ability to control data access and tool usage. Furthermore, Activity Monitoring for AI records and reports on AI interactions, ensuring transparency and compliance.
Rajesh Parthasarathy, CEO and founder of Mage Data, emphasized the company’s strategy to adapt existing data protection principles to the new environments where enterprise data interacts with AI systems. The platform allows organizations to extend current Mage Data policies to AI workloads, thus avoiding the need for a separate policy framework for AI. Anil Bhat, CTO and Senior Vice President, warned of the risks associated with employees using public AI tools with sensitive information, highlighting the importance of protecting data without completely blocking AI tools, which might drive employees to unmanaged alternatives.
Data Security and Privacy for AI is currently available, with Mage Data offering demonstrations and proof-of-concept deployments for organizations interested in evaluating the technology. For more information, organizations can visit Mage Data’s website or contact their media team.
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