Mage Data has rolled out its latest addition to its data protection platform, a tool named Data Security and Privacy for AI. This new extension is crafted to assist companies in safeguarding sensitive data across the artificial intelligence lifecycle. The platform’s capabilities encompass a wide array of AI environments, including training settings, public generative AI applications, custom AI agents, and embedded copilots. The core function of this platform is to enforce data protection protocols at various stages: before data enters an AI system, throughout its processing and development, and when AI systems generate responses.
According to Mage Data, traditional enterprise data controls pose challenges when applied to AI environments. Sensitive information often traverses through various channels like extracts, notebooks, feature stores, evaluation datasets, prompts, and AI-generated responses, making conventional methods less effective. To address these issues, the new offering emphasizes five key protection areas. Training Data Guardrails are designed to identify sensitive data like personally identifiable information (PII), protected health information (PHI), and non-public information (NPI) within both structured and unstructured data sets. Organizations can choose to mask this data at its source, secure it before it enters AI pipelines, or apply controls using software development kits.
The platform also introduces AI Usage Guardrails, which scrutinize employee prompts and file uploads to public generative AI services, ensuring sensitive information is masked before leaving a user’s device. Dynamic Data Masking for AI offers the ability to mask, redact, generalize, or block AI-generated responses based on user requests and the information contained in those responses. Additionally, AI Development Guardrails provide organizations developing custom AI agents with effective control measures. Through Mage Data’s SDKs and MCP Server, companies can limit tools and data access according to user permissions. Furthermore, the platform’s Activity Monitoring for AI keeps a record of AI interactions, user prompts, tool usage, and sensitive data masking, while also offering reporting and alerting features.
The company emphasizes that existing Mage Data policies can be extended to AI workloads without the need for a separate policy framework specifically for AI. Rajesh Parthasarathy, CEO and founder of Mage Data, stated that the firm’s strategy involves applying established data protection principles to the expanding environments where enterprise data interacts with AI systems. The potential risk of employees sharing sensitive data with public AI tools was also highlighted. Mage Data’s CTO and Senior Vice President, Anil Bhat, mentioned that the company’s approach aims to protect data without forcing enterprises to entirely block AI tools, which could otherwise lead to employees resorting to unmanaged services.
The Data Security and Privacy for AI solution is currently available, with Mage Data offering demonstrations and proof-of-concept deployments for organizations interested in evaluating the technology. The company provides detailed product information and contact options for further inquiries through its website.
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