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DataGrail reports.Ethyca governs.
DataGrail automates the consumer-facing privacy request layer across your SaaS catalog. Ethyca automates privacy requests, consent management, and policy enforcement across your entire data estate.
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Why Ethyca?
"5 minutes of working with Ethyca is the equivalent of working with DataGrail for months."
Datagrail helps you fulfill users' privacy requests, but the AI era calls for a more powerful end-to-end governance solution for privacy. Ethyca is just that: built for new levels of speed and scale. It maps data, enforces consent management, and fills access requests automatically in real-time, with no human support needed.
DataGrail vs. Ethyca Comparison
| Feature | DataGrail | Ethyca | What it means for you |
|---|---|---|---|
| Core architecture | Privacy request automation for legal and privacy teams — DSR workflows, data mapping, and consent management across a SaaS catalog. (Request management platform) | Privacy enforced inside systems via Fides taxonomy — policy as executable code, running in CI/CD and at query time. (Infrastructure layer) | Your privacy controls live inside the systems that handle data, so they keep working as those systems change rather than depending on someone updating a separate map. |
| Engineering involvement | Designed to operate without developer involvement — privacy and legal teams run the platform; engineering not equipped with privacy tooling. (Legal-team primary) | CLI tools, linting in PRs, privacy validation in CI/CD pipelines — engineers enforce policy in every system they build, from day one. (Engineers as privacy participants) | Your engineers catch privacy issues in code review, before they ship, instead of a compliance team finding them after the fact. |
| Custom & internal systems | Broad SaaS catalog coverage; reviewers document manual work required when integrations lack API capability for custom and internal systems. (Manual gaps at custom systems) | Direct enforcement hooks into proprietary databases, internal applications, custom infrastructure — Helios classifies and maps in real time. (Deep integrations) | Your proprietary systems and internal tools get the same real-time enforcement as your SaaS stack, so nothing depends on a manual review queue. |
| Open-source foundation | Closed, proprietary platform — enforcement logic is not independently inspectable; audit trails are vendor-mediated. (Closed) | Fides — Apache 2.0, fully inspectable, IAPP-recognized, deployable independently. No black box in your enforcement stack. (Fully open) | You can inspect and audit exactly how your enforcement logic works, rather than taking a vendor's word for what happens behind closed doors. |
| AI governance | AI Risk Assessment template — documentation and risk recording workflow, not enforcement inside model training or inference pipelines. (Assessment layer) | Astralis — native enforcement inside AI training and inference pipelines. Policy applied at the data and model layer, EU AI Act ready. (Native enforcement) | Your AI systems are governed at the point they train and run, so you can show regulators evidence instead of a checklist. |
| Data inventory | Live Data Map with continuous system detection — strong across SaaS catalog; custom system and internal database coverage varies. (SaaS-forward) | Helios — real-time automated discovery and classification across cloud, databases, and third-party services. Always-current RoPA. (Real-time, automated) | Your data inventory reflects what's actually in your systems right now, so audits work from current facts rather than a snapshot that's already aging. |
| Legal + engineering alignment | Built for privacy and legal teams. Engineering is not a user of the platform — no mechanism for developers to enforce policy in the systems they build. (Legal-team only) | Fides taxonomy is the shared language — legal defines obligations in human-readable policy; engineering implements as code. One source of truth for both teams. (Unified taxonomy) | What your legal team approves is what actually runs in production, because both teams work from the same definition of policy. |
| Pricing model | Custom pricing; buyer community data documents volume-based variables at renewal as usage and integrations grow. (Variable at renewal) | Flat license based on integration scope. No volume escalation. No SKU add-ons required. Support included. Predictable over the full contract term. (Scope-based flat rate) | Your costs stay predictable as you grow, so budgeting doesn't get disrupted by a renewal surprise. |
| Deployment speed | Fast for standard SaaS stacks; implementation complexity increases with custom systems and internal databases. (Variable) | Large publishers live across 90+ websites within a month. Weeks-first deployment model with forward-deployed engineering support included. (Fast time to value) | You get live enforcement across your full stack within weeks, engineering support included, so value shows up fast instead of stalling in a long rollout. |
Night and day. Ethyca turned our data governance from a bottleneck into a strategic enabler for scale.
SurveyMonkey chose Ethyca instead. These were the results.
SurveyMonkey's implementation began by stabilizing their most fragile process: data subject access requests. Ethyca configured DSAR fulfillment workflows to integrate directly with their unique SQL Server environment, delivering consistent, policy-driven responses within 48 hours - down from seven-day manual coordination cycles.
7 days -> 48 hours
Data subject request response time.
4 PB
Volume of data governed by Ethyca
40m users
Protected by Ethyca
Ethyca does governance, risk, and compliance in a single layer
Ethyca does governance, risk, and compliance in a single layer
Discover personal and sensitive data across your infrastructure, websites, and third-party apps, or enrich existing DSPM and catalogs. The result is a unified source of data intelligence, risk, and insight to support governance obligations.
Replace forms and template maintenance with continuous risk assessments. Astralis, made by Ethyca, knows the internal and external policies you must follow, and continually analyzes risk across your data, purpose, and vendor inventories.
Purpose based access control monitors and enforces data access across AI agents, teams, and data projects. Policies provide a governance framework that proves how data is used, not just who accesses it, ready for audit.
Fine-grained, high-performance consent and preference management. Model any consent or preference framework, from GDPR to CCPA, and respect user choice wherever data is accessed - models and agents included.
A flexible, policy-based data access and de-identification engine enables targeted, scaled data rights enforcement across your infrastructure, integrating with databases, warehouses, and third-parties to meet data obligations.
Ethyca's platform is built on Fides, the world's most used open-source taxonomy for data governance and privacy. It lets you consistently describe the data you process, its purposes, and ownership for consistent enforcement.
Frequently Asked Questions
For most teams it replaces the Al governance layer, and it can replace the wider privacy program too - SurveyMonkey used Ethyca for end-to-end privacy compliance. Where a Datagrail deployment is entrenched, Ethyca's Astralis runs alongside it and supplies the enforcement and evidence that the program record can't produce on its own.
Ethyca's Astralis enforces policy directly inside the systems running your AI, at the point data moves: training, retrieval, inference. The same policy that classifies your data also governs what your models can train on and output. Datagrail's agent works differently. It sits beside the core workflow platform as a separate product, assessing risk and generating documentation for review, but nothing in that process changes how your AI systems behave at runtime. Astralis was built for the EU AI Act's demand for demonstrable control, and it delivers that control natively, instead of asking you to bolt on another dashboard and hope the underlying systems comply.
Infrastructure-first means policy runs inside the systems where your data lives and moves, enforced automatically every time that data is touched. Ethyca sits inside your data pipelines, applications, and AI systems, so a policy change takes effect the moment it's deployed, the same way a code change does. OneTrust operates a layer above: a workflow and dashboard platform that tracks compliance status, assigns tasks, and produces reports for auditors. It monitors what your systems are supposed to be doing. It doesn't change what they do. That gap is why Datagrail deployments depend on manual configuration to stay accurate as systems change, while Ethyca's Astralis policy stays enforced by the infrastructure itself, with nothing left for someone to check by hand.