Data Security and Privacy for AI
Data Security and Privacy for AI: Guardrails for Every Place AI Risk Lives
AI has created three distinct places where sensitive data is now at risk: the prompts and files employees send to public GenAI tools, the responses enterprise assistants and agents return to users, and the datasets models are trained on in the first place. Most organizations address one of these and discover the other two during an audit or an incident. A runtime prompt filter does nothing about PII already baked into a training set, and training-time anonymization does nothing about an employee pasting a customer record into ChatGPT.
Mage Data's Data Security and Privacy for AI covers all three layers on a single platform — govern and monitor AI use, protect AI in use, and protect the data foundation underneath. Five capabilities share one discovery and classification engine, one policy model and one pane of glass. Everything runs inside your own VPC or data centre, so sensitive data never leaves your environment. Deploy any capability standalone, or extend the Mage Data platform that already governs your databases, files and endpoints to cover AI.
Key Capabilities
Data Security and Privacy for AI Overview
Training Data Guardrails
Layer 1 · Protect the Data Foundation. Protects sensitive data before a dataset is used to train, fine-tune, evaluate or test a model — mask, anonymize, tokenize or synthesize by policy, with model utility preserved.
Learn moreAI Usage Guardrails
Layer 2 · Protect AI in Use. Inspects employee prompts and file uploads to public GenAI tools like ChatGPT, Gemini and Claude, masking sensitive content before anything leaves the device. It masks, never blocks.
Learn moreDynamic Data Masking for AI
Layer 2 · Protect AI in Use. Controls what each end user sees in an AI-generated response, based on their role. An enforcement proxy for vendor copilots and assistants you cannot change — zero application changes.
Learn moreAI Development Guardrails
Layer 2 · Protect AI in Use. Embeds authorization and response masking into the agents you build, via MCP Server components and SDK/API — checking what the end user is entitled to receive, not what the agent can retrieve.
Learn moreActivity Monitoring for AI
Layer 3 · Govern & Monitor. Records every AI interaction as one event — user, tool, data category, outcome — with severity-ranked alerts, Shadow AI detection and personalized end-user reports.
Learn moreRuns inside your environment. Every capability runs in your own VPC or data centre — not a vendor cloud. Deploy any of them standalone, or extend the Mage Data platform that already governs your databases, files and endpoints to cover AI.
See it on your own data
Book a 30-minute demo and we will show Data Security and Privacy for AI running against your own classification policies.