Data Security and Privacy for AI
Dynamic Data Masking for AI: The Right Person Sees the Right Data
AI answers are not like database rows — you cannot just mask a column. An AI assistant may need broad access to enterprise data to be useful, but that does not mean every user should receive every result the assistant can retrieve. Traditional masking works on a predictable pattern of query, table, row and column. AI systems return natural-language answers, so a user can ask "answer yes if this person earns over X" and receive a sensitive conclusion without any labeled sensitive field ever appearing. Agents can also be queried iteratively, letting a user piece together sensitive facts across turns where no single answer triggers an alert. And when the assistant is a vendor's — a SaaS copilot, an embedded agent — there is no codebase to build the control into.
Mage Data Dynamic Data Masking for AI controls what each end user sees in an AI-generated response, based on that person's role and access privileges. It deploys as an enforcement proxy in the response path: it resolves the real end user to a role or access policy, evaluates the request and candidate response together, then allows, masks, redacts, generalizes or blocks the response before it reaches the user. The vendor's UI, agent logic and model are never touched — zero application changes. The right person sees the right data every time, regardless of how the question is phrased.
Key Capabilities
Enforcement happens in the response path, at the only point with enough context to make the right call: who asked, what they asked, and what the AI is about to say.
Dynamic Data Masking for AI Overview
Enforcement Proxy — Zero Application Changes
Deploys in the response path alongside vendor SaaS copilots and enterprise assistants, without altering their UI, agent logic or model.
End-User Entitlement Resolution
Before sensitive data is returned, the workflow checks the actual end user's role and entitlement — not the agent's global credentials. The human behind the session decides the outcome.
Context-Aware Prompt-plus-Response Evaluation
Evaluates identity, question and candidate response together — catching inference attacks and iterative exfiltration that output-only scanning misses.
Policy-Based Classification & Masking
Powered by Mage Data's patented discovery and classification engine, with built-in data classifications.
RBAC & ABAC for AI Responses
The access-control models you already use for databases and applications, extended to the AI layer. Same question, three roles, three policy-appropriate answers.
Runs inside your environment. Controls live in your agent's codebase and deployment pipeline, in your VPC or data centre — not a vendor cloud.
See it against your own copilot
Book a 30-minute demo and we will show the same question returning three role-appropriate answers — live, against your access policies, with no changes to the application.