Enterprise policies for known fields.
Protegrity fits enterprise programs that need governed field protection, centralized policies, and integrations across many platforms.
Protegrity Alternative
Protegrity protects mapped fields. Agent Mask de-identifies PII buried in text.
Protegrity is built for enterprise data protection and field-level data protection programs across systems. Agent Mask is a focused Protegrity alternative for unstructured text: it finds PII that field-level policies are too blunt to handle by themselves, then de-identifies sensitive entities while preserving the useful text around them.
Start a trial on Snowflake Marketplace, or get a self-hosted trial key for Docker.
ProtegrityField-level data protectionCross-system controls for governed sensitive fields
Use case gapPlatform rolloutIntegration architecture and customer-cloud components
Agent MaskBuried sensitive textDe-identify PII in unstructured text while keeping useful context
1SELECT agent_mask_en.app_public.mask(
2 [ticket_note, chat_log]
3) AS redacted
4FROM support_tickets;
Input column
Text
ticket_note
Billing question from
Maya Chen
PERSON_1
about invoice INV-1042. Assigned to
Alex Patel
PERSON_2
. Contact:
maya.chen@example.com
EMAIL_ADDRESS_1
or
415-555-0198
PHONE_NUMBER_1
chat_log
10:42
Alex
PERSON_2
:
Maya
PERSON_1
never got the invoice email.
10:43 Maya Chen PERSON_1 replied from maya.chen@example.com EMAIL_ADDRESS_1 and asked Alex Patel PERSON_2 to call 415-555-0198 PHONE_NUMBER_1
10:43 Maya Chen PERSON_1 replied from maya.chen@example.com EMAIL_ADDRESS_1 and asked Alex Patel PERSON_2 to call 415-555-0198 PHONE_NUMBER_1
Field-level data protection is not the same as text de-identification.
Protegrity governs known sensitive fields across systems as part of enterprise data protection. Agent Mask provides private PII de-identification inside your runtime while preserving the surrounding text your teams still need for review, support workflows, search, and AI/ML training.
PII discovery and de-identification for unstructured text.
Agent Mask scans sensitive text, replaces detected sensitive entities, and returns de-identified output, configurable replacements, entity metadata, and audit evidence inside the private workflow.
Known fields are only part of enterprise data protection.
Enterprise platforms protect what has been modeled and governed. Warehouse datasets and app pipelines often contain useful long-form content with sensitive values mixed in. Mask the whole field, and you lose the context. De-identify the entities, and the text can still do its job.
Policies need discovered data
Protegrity's breadth is useful for enterprise programs, but unmapped sensitive values still have to be found before teams can decide how to preserve the useful parts of the record.
Protegrity Snowflake integrations add more architecture
Protegrity's Snowflake materials describe a broader integration pattern around the warehouse, which can add setup, routing, ownership, and operational complexity. Agent Mask runs as a Snowflake Native App or self-hosted API where the data already lives.
Sensitive values still hide in text
Enterprise field protection starts with mapped data and policies. Agent Mask handles unstructured data, supported file formats, and custom identifiers already inside your private runtime without blanking the surrounding content.
Protegrity vs Agent Mask.
Compare broad enterprise data protection with focused PII discovery and private de-identification across scope, architecture, starting point, and implementation work.
Dimension
Protegrity
Agent Mask
Program scope
ProtegrityEnterprise data-centric security across many systems and environments.
Agent MaskFocused PII discovery, redaction, and de-identification for Snowflake or self-hosted workflows.
Core job
ProtegrityEnterprise field protection and policy enforcement across governed systems.
Agent MaskFind PII in long-form text and prose, preserve useful context, return entity evidence, and transform values with masks, hashes, encryption, synthetic replacements, labels, or keep rules.
Starting point
ProtegrityKnown or mapped sensitive fields governed by enterprise policies.
Agent MaskUnmapped PII, custom identifiers, and values that require row-level entity consistency.
Snowflake rollout
ProtegrityArchitectures can include customer-cloud components, external UDFs, protectors, connectors, and APIs, adding more integration surface around Snowflake.
Agent MaskStart in Snowflake Marketplace, or run the self-hosted container, then call the mask function or private API.
Buying motion
ProtegrityEnterprise platform evaluation with sales, procurement, contracts, and rollout planning.
Agent MaskSelf-service Snowflake Marketplace trial or self-hosted trial key for teams that want to start redacting immediately.
Implementation scope
ProtegrityBroad platform rollout for multi-system governance.
Agent MaskPrivate workflow for PII discovery, redaction, de-identification, and audit evidence.
Reference material
This page uses Protegrity product, Snowflake integration, architecture, and protector documentation.
Protegrity product
Protegrity positioning for enterprise data-centric security, privacy, governance, and protection.
Protegrity Snowflake
Protegrity page describing Snowflake integration and customer-cloud component architecture.
Snowflake architecture docs
Protegrity documentation describing API Integration objects and hosted Protegrity components.
Tokenization docs
Protegrity documentation for tokenization and related data protection behavior.
Agent Mask zero-egress architecture
Agent Mask documentation explaining how Snowflake Native App and self-hosted Docker processing stay inside your chosen runtime without sending payloads to Agent Mask.
Agent Mask self-hosted security
Agent Mask documentation for self-hosted data flow, network requirements, offline license verification, retention, and hardening.
Related comparisons
Keep evaluating redaction options across the same private-runtime, data movement, pricing, and implementation questions.
Alternative to LLM-based PII redaction without token costs or data egress.
Stop paying per-token LLM prices to redact sensitive text at scale. See how Agent Mask compares with OpenAI, Claude, Gemini, and Bedrock when sensitive-text redaction needs to run in Snowflake or a self-hosted deployment.
Agent Mask beat OpenAI Privacy Filter in a 50,000-example PII detection benchmark.
Agent Mask reached 96.9% F1 vs 74.2% for OpenAI Privacy Filter on a 50,000-example English PII benchmark, while also returning redacted output and review metadata.
Snowflake AI_REDACT alternative for long text, documents, and custom entities.
Compare Snowflake AI_REDACT with Agent Mask for PII redaction in Snowflake: long text, documents, custom entities, consistent replacements, and review metadata.
Protegrity alternative FAQ
Answers for teams comparing enterprise field protection with private PII discovery and de-identification.
When is Protegrity the right choice?
Use Protegrity when the project is an enterprise data protection program: governed sensitive fields, centralized policies, protector architecture, and controls that span many systems.
Is the difference structured data versus unstructured data?
Not exactly. Protegrity has discovery and classification products, including support for structured and free-text data. The practical difference is the workflow: Protegrity starts from enterprise policy and protector architecture, while Agent Mask starts from the sensitive text your team needs to de-identify now.
How does Agent Mask supplement field-level controls?
Field-level controls protect the values your team already knows how to govern. Agent Mask finds PII in long-form text, prose, supported files, and custom identifiers, then redacts detected sensitive entities while keeping the surrounding content usable.
What changes in a Snowflake rollout?
Protegrity's Snowflake materials describe integration patterns that can include external UDFs, customer-cloud components, and Protegrity services. For many teams, that also means an enterprise sales, procurement, contract, and rollout process. Agent Mask is installed from Snowflake Marketplace in under 15 minutes, then called from SQL where the data already lives.
When is Agent Mask the better fit than Protegrity?
Choose Agent Mask when the problem is PII mixed into notes, tickets, documents, or training data, not a broad policy layer for mapped fields. Agent Mask de-identifies detected sensitive values in Snowflake or a self-hosted container so teams keep usable text, replacements, and evidence.
Remove the PII without throwing away the text.
Run Agent Mask in a private runtime, through Snowflake Marketplace or a self-hosted deployment, to turn long-form sensitive content into de-identified output with useful context, entity evidence, and configurable replacements.