Mage Data has launched a new extension to its data protection platform called Data Security and Privacy for AI. This addition aims to assist enterprises in safeguarding sensitive information throughout the entire artificial intelligence lifecycle. The platform’s enhanced capabilities are designed to secure AI training environments, public generative-AI applications, custom AI agents, and embedded copilots. By applying data protection policies at various stages—before information enters an AI system, during its processing and development, and when an AI system generates a response—the platform seeks to address the challenges of managing sensitive data in AI environments.
Traditional enterprise data controls often struggle to cope with the complexities of AI environments, where sensitive information can traverse through extracts, notebooks, feature stores, evaluation datasets, prompts, and AI-generated responses. To tackle this, Mage Data’s new offering focuses on five main areas of protection. Training Data Guardrails can identify sensitive information like personally identifiable information (PII), protected health information (PHI), and non-public information (NPI) in both structured and unstructured datasets. Organizations can then mask data at its source, secure it as it enters AI pipelines, or apply controls using software development kits.
The platform also includes AI Usage Guardrails that inspect employee prompts and file uploads to public generative-AI services, ensuring that sensitive information is masked before leaving a user’s device. Additionally, Dynamic Data Masking for AI allows for the masking, redacting, generalizing, or blocking of AI-generated responses based on the user, request, and information contained in the response. For organizations developing their own AI agents, AI Development Guardrails offer controls through Mage Data’s SDKs and MCP Server, which can restrict tools and data access according to user permissions.
Activity Monitoring for AI is another feature that records AI interactions, capturing details such as users, prompts, tools, sensitive data masking, overrides, and policy outcomes, while also offering reporting and alerting capabilities. Mage Data emphasizes that organizations can extend their existing data protection policies to AI workloads without the need to establish a separate policy framework specifically for AI. According to Rajesh Parthasarathy, CEO and founder of Mage Data, the company’s strategy is based on applying existing data protection principles to the increasing number of environments where enterprise information interacts with AI systems.
The new platform addresses potential risks associated with employees using public AI tools with sensitive data. Anil Bhat, CTO and Senior Vice President of Mage Data, noted that the approach is designed to protect data without forcing enterprises to completely block AI tools, as such restrictions could lead employees to resort to unmanaged services. Data Security and Privacy for AI is currently available, with Mage Data providing demonstrations and proof-of-concept deployments for organizations interested in evaluating the technology.
Legal Disclaimer:
The information contained in this article has been provided by independent third-party contributors, clients, or content partners. We do not independently verify the accuracy, completeness, legality, ownership, licensing, or reliability of submitted content, including text, images, videos, trademarks, or other media materials. The submitting party is solely responsible for ensuring that all content, including images and media assets, complies with applicable copyright, trademark, licensing, and intellectual property laws. We disclaim liability for any unauthorized use of copyrighted or proprietary materials by third parties. If you believe that any content published on this platform infringes your intellectual property rights, kindly contact the author above for prompt review and resolution.