Mage Data has launched a new suite of tools aimed at bolstering data security and privacy within the realm of artificial intelligence. The initiative, known as Data Security and Privacy for AI, serves as an enhancement to its existing data protection platform. It is specifically crafted to assist businesses in safeguarding sensitive information across the entire AI lifecycle. The platform’s expanded functionalities encompass various AI environments, such as training systems, public generative-AI applications, custom AI agents, and integrated copilots. It ensures data protection from the moment information enters an AI system, throughout its processing and development phases, and even when an AI-generated response is produced.
Traditional enterprise data controls often face challenges when applied to AI settings, where sensitive information might traverse through elements like extracts, notebooks, feature stores, and prompts. Mage Data’s new offering addresses this by introducing five key protection areas. Training Data Guardrails focus on identifying sensitive data, including personally identifiable information (PII), protected health information (PHI), and non-public information (NPI), across both structured and unstructured datasets. To mitigate risks, organizations can choose to mask data at the source or apply controls through software development kits as information enters AI pipelines.
AI Usage Guardrails play a critical role by assessing employee prompts and file uploads to public generative-AI platforms, with the ability to mask sensitive data before it leaves a user’s device. Dynamic Data Masking for AI offers the flexibility to mask, redact, generalize, or block AI-generated responses based on user specifics, requests, and content. Furthermore, AI Development Guardrails provide organizations with control mechanisms when developing their own AI agents, allowing restrictions on tools and data access based on user permissions.
Activity Monitoring for AI adds another layer by recording AI interactions, including user data, prompts, tools, and sensitive data masking, while also offering reporting and alerting functionalities. Mage Data emphasizes the advantage of extending existing data protection policies to AI workloads, avoiding the complexity of maintaining a separate policy framework for AI. According to Mage Data’s CEO and founder, Rajesh Parthasarathy, the company’s strategy revolves around adapting established data protection principles to the expanding environments where enterprise information interfaces with AI systems.
The company also points out the risks associated with employees using public AI tools for sensitive data. Anil Bhat, Mage Data’s CTO and Senior Vice President, highlights that their approach aims to safeguard data without necessitating a complete block on AI tools, which could drive employees toward unregulated services. The new Data Security and Privacy for AI offering is currently available, and Mage Data is providing demonstrations and proof-of-concept deployments for organizations interested in evaluating the technology.
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