Zero Trust & SASE
Detect and Redact Sensitive Data Before It Leaves Your Control
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Identify sensitive values
- 02
Replace with stable tokens
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Restore only inside your boundary
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Requirements reviewed with your team
02
Technical fit and integrations assessed
03
Pilot scope defined around measurable outcomes
The problem
Data loss prevention historically watched email attachments and file uploads. It didn't anticipate an employee pasting a customer database into a chat window on a public AI tool — a channel most DLP policies were never written to inspect.
Generative AI tools made this worse specifically because pasting text feels lower-stakes than attaching a file, even when the content is identical — a spreadsheet full of customer records copied into a prompt leaves an organization's control just as completely as an email attachment would.
Regulated industries face this from both directions: DLP is often a compliance requirement rather than only a security preference, and auditors increasingly ask what controls exist for AI tool usage specifically, beyond traditional exfiltration channels.
How MaskFlare Vault works
Vault is designed to inspect data moving through supported workflows, identify PII and other sensitive content, and apply actions such as redaction, blocking, or audit-only review before the data reaches its destination.
Benefits
Extends DLP to AI chat interfaces
The same inspection engine extends to text pasted into external chat and AI interfaces.
Understands data structure, beyond keyword lists
Detects structured sensitive data like customer records and credentials, going past exact-match keyword lists.
One policy, every exit channel
The same DLP policy applies whether data is leaving by email, upload, or an AI tool's input box.
Supports compliance requirements
Consistent inspection and logging across channels helps satisfy audit requirements for data-handling controls.
No separate agent for AI monitoring
GenAI inspection runs through the same Shield/Access traffic path, without a dedicated tool to deploy and maintain.
Works across managed and unmanaged devices
Coverage extends to any session routed through Access or Shield, regardless of device ownership.
Capabilities
PII and sensitive-data detection
Pattern and structure-aware detection designed for personal identifiers, credentials, financial details, and organization-specific data types.
GenAI data security
Inspects text submitted to external AI tools for sensitive content before it leaves the browser session.
Structured data detection
Recognizes structured formats like payment card numbers, government IDs, and API keys, beyond free-text keyword matches.
Policy by data classification
Different sensitivity tiers can carry different handling rules, rather than one blanket policy for all data.
Block or audit-only modes
Run in enforcement mode to block sensitive submissions, or audit-only mode to measure exposure before enforcing.
Explore related capabilities
See how the capabilities planned for MaskFlare address adjacent security and privacy requirements.
Related from Flarepedia
Frequently asked questions
What is data loss prevention (DLP)?
DLP is the practice of detecting and blocking sensitive data — like customer records or credentials — from leaving an organization's control through channels like email, file uploads, or web forms.
Does Vault monitor what employees type into AI chat tools?
Vault inspects data leaving through any channel covered by Access or Shield, including text submitted to external AI tools, against the same sensitive-data policy used elsewhere.
Can Vault detect data types beyond exact keyword matches?
Yes — Vault matches on structure and pattern, so it catches payment card numbers, government IDs, and API keys that a plain keyword list would miss.
Can we run Vault in audit-only mode first?
Yes — audit-only mode logs what would have been blocked without enforcing, useful for measuring exposure before turning on enforcement.
Does Vault require a separate agent on employee devices?
No — inspection happens on traffic routed through Access or Shield, without a dedicated endpoint agent for Vault specifically.
Can different departments have different DLP policies?
Yes — policy can be scoped by data classification and by user or group, rather than one uniform rule for the whole organization.
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