Managing requests isn't the same as enforcing policy.

DataGrail handles privacy requests. Ethyca enforces the policies governing your data inside your systems. As complexity grows, the difference matters.

Request management and data infrastructure aren't the same thing.

DataGrail is built to give privacy teams control over the consumer request lifecycle. Ethyca is built to enforce the policy that governs data across your entire stack — including the systems engineers build and the pipelines AI runs through.

DataGrail — how it's built

Privacy request automation across a broad SaaS catalog

DataGrail's strength is automating consumer privacy requests — access, delete, and opt-out — across a broad catalog of SaaS tools. Privacy teams can stand up a program, process DSRs, run risk assessments, and manage consent banners without significant developer involvement.

For organizations running standard SaaS stacks and processing high volumes of consumer requests, that model works. DataGrail earns strong G2 ratings on this use case: operationalizing CCPA and GDPR compliance for privacy and legal teams.

→ Operationally strong for consumer request automation

Ethyca

Consumer request management isn't the same as data infrastructure

DataGrail handles the consumer-facing request lifecycle. It doesn't enforce the policy that governs what happens to that data inside your systems. When your stack includes proprietary databases, custom internal applications, AI model training pipelines, or multi-jurisdiction data processing, gaps emerge.

Reviewers document the pattern: integrations that lack API capability require manual work. Custom systems and internal databases fall outside the integration catalog. When engineering teams aren't equipped with privacy tooling, every system they build is coverage that has to be mapped and integrated after the fact. Ethyca closes that gap at the infrastructure level.

→ The gap widens with every internal system you build

Companies building trust into data with Ethyca

Customer Examples

• The New York Times
• WeTransfer
• Ramp
• SurveyMonkey
• Vercel
• Axios

Consumer request automation vs. privacy infrastructure.

The gap between the two models is clearest when you ask: what happens when a regulator asks you to prove a policy was applied — not just processed?

DataGrail

Request management

• Policy compliance recorded, not enforced
• Compliance state surfaces in dashboards. DSR workflows are tracked, logged, and reported — but the enforcement depends on every integration executing the deletion or access request correctly.
• Integration catalog stops at the SaaS edge. DataGrail's 2,000+ integrations cover popular SaaS applications. Custom databases, internal systems, and proprietary infrastructure require manual effort or fall outside automated coverage entirely.
• No engineering-native tooling. No CLI. No CI/CD integration. No privacy linting in pull requests. The platform is designed for privacy and legal teams to operate independently — which means engineers have no mechanism to enforce policy in the code they ship.
• AI governance is a documentation layer. Risk assessment templates document AI-related risks. There is no mechanism to control what data enters a training run, under what consent conditions, or in compliance with regulatory requirements at the pipeline level.
• Closed, proprietary platform. No open-source foundation. Enforcement logic is not independently inspectable. Audit trails are vendor-mediated. When a regulator asks to see how a rule was applied, you show a dashboard export.

Ethyca

Privacy infrastructure

• Policy enforced as executable code. Fides translates legal obligations into machine-readable rules that run inside CI/CD pipelines, data warehouses, and API calls. Policy adherence is a property of the system, not a log of requests processed.
• Direct hooks into databases and internal systems. Helios discovers and classifies data in real time with direct integrations into the systems that hold it — Snowflake, Postgres, proprietary databases, AI pipelines. Erasure and access run against the actual data store.
• CI/CD-native — engineers are first-class participants. Fides CLI, privacy linting in pull requests, policy validation before code ships. Engineers adopt privacy tools the same way they adopt a linter or security scanner. Every system they build enforces policy from the start.
• AI governance native to training & inference. Astralis enforces which data can enter AI model training and inference pipelines, under what consent conditions, in compliance with the EU AI Act. Not a documentation layer — enforcement at the pipeline level.
• Fides — open-source, inspectable, IAPP-recognized. Apache 2.0 license, 7,000+ GitHub stars, recognized by the IAPP as a governance standard. Every enforcement decision is inspectable. When a regulator asks how a rule was applied, you show the log.

DataGrail is a closed platform. Ethyca's foundation is open.

Fides is the world's most widely used open-source privacy engineering standard. DataGrail has no equivalent — its platform, enforcement logic, and audit trails are all vendor-mediated. When you need to prove policy was applied, that matters.

7k+ GitHub stars

Actively maintained, community-contributed, and deployable independently of Ethyca's commercial platform. Every enforcement decision can be inspected by your team.

Apache 2.0

Open license. No vendor lock-in on the taxonomy itself. Your privacy standard is yours — built on an open specification that DataGrail cannot match with a closed product.

IAPP

Recognized standard. Fides is recognized by the International Association of Privacy Professionals as a governance standard — not a vendor tool, but a shared language the entire industry can use.

Feature comparison

DataGrail vs. Ethyca — side by side

Across the dimensions that determine whether your privacy program holds up under regulatory scrutiny, engineering scale, and AI complexity.

Organizations that need privacy enforced inside their stack.

Data-intensive enterprises where consumer request automation isn't enough — where policy needs to run inside the systems, the pipelines, and the AI models that process the data.

Switching from DataGrail

A straightforward transition.

Teams switching from DataGrail have typically established some privacy program foundations. The migration is additive — adding enforcement inside your systems, engineering-native tooling, and AI governance at the pipeline level.

↳ Step 1 — Port your data map into Helios

Your existing DataGrail Live Data Map inventory migrates as the starting point. Helios then takes over automated discovery — enriching it with real-time classification across your internal systems and databases that DataGrail's SaaS catalog couldn't reach.

↳ Step 2 — Translate your consent configuration with Fides

Existing consent categories map to the Fides taxonomy. User preferences migrate without re-collection. Consent is then enforced at the system level — not just captured at the banner.

↳ Step 3 — Replace request routing with direct enforcement

DataGrail's DSR workflows that route requests to SaaS integrations are replaced with Lethe's direct system-level fulfillment. Erasure and access run against the actual database — not through the integration chain.

↳ Step 4 — Embed Fides into your engineering workflow

Engineering teams install the Fides CLI and add privacy linting to pull requests. For the first time, every system your engineers build enforces policy from day one — without manual mapping after the fact.

↳ Step 5 — Extend coverage to AI pipelines

Astralis enforces data policy inside AI training and inference — controlling which consented data can be used, under what conditions, in compliance with the EU AI Act. DataGrail's risk assessment templates don't go here.

FAQ

Common questions

Questions that surface when teams realize the gap between consumer request management and enforcement inside their data stack.

DataGrail has 2,000+ integrations. How is Ethyca's coverage better?

Integration count measures breadth, not depth. DataGrail's catalog excels at SaaS-to-SaaS connections for consumer request routing. Where it breaks down — and reviewers document this repeatedly — is custom systems, proprietary databases, and internal applications, where API coverage is incomplete and manual work is required. Ethyca's approach is narrower but deeper: Helios discovers and classifies data in real time with direct hooks into the systems that actually hold it. And Fides enforces policy at the query level inside those systems — not by routing a request through an integration.

DataGrail's approach means our privacy team can operate without engineering. Isn't that an advantage?

DataGrail is designed to let privacy teams operate without engineering involvement. That reduces operational friction for the privacy team. But it also means engineering teams have no tools to enforce policy in the systems they build — which creates a gap that grows with every new database, pipeline, or AI model added to the stack. Ethyca gives engineers the tools to build privacy in from the start — Fides CLI, CI/CD integration, linting in pull requests. The outcome is that your engineers become privacy participants rather than gaps in your compliance coverage.

We're a consumer-facing brand. DataGrail seems built exactly for us. Why would we look at Ethyca?

If your stack is primarily SaaS, your data processing is straightforward, and you have no plans to build custom systems or train AI models on customer data — DataGrail may be sufficient for your current program. Where the calculation shifts: the moment you have proprietary databases, internal applications, ML pipelines, or AI systems processing customer data, you need enforcement inside those systems. DataGrail's design doesn't extend there. Ethyca is built for companies that are already past the standard SaaS stack — or who expect to get there. NYT, Ramp, and Vercel chose infrastructure over request management for exactly that reason.

What's the AI governance comparison?

DataGrail's AI Risk Assessment template gives privacy teams a structured way to document and assess AI-related risks. That's documentation and workflow management — useful for recording your program. Ethyca's Astralis goes further: it enforces which consented data can enter AI model training runs and inference pipelines, at the pipeline level, in compliance with the EU AI Act. The difference is between recording that a policy exists and enforcing it where the data actually flows. As AI Act obligations become operational, that distinction will determine whether your documentation holds up under scrutiny.

DataGrail has strong G2 ratings and IDC recognition. Is Ethyca a credible alternative?

DataGrail's G2 ratings are real — they're earned on consumer request automation, and that product is well-built. Ethyca's credibility comes from a different set of evidence: The New York Times, Ramp, Vercel, WeTransfer, and SurveyMonkey. The NYT manages 10M+ subscribers across 200 countries with compliance obligations spanning every major global privacy regulation. These are organizations with zero tolerance for compliance gaps — they chose infrastructure precisely because request management wasn't enough. And Fides, Ethyca's open-source foundation, has 7,000+ GitHub stars and is IAPP-recognized as an industry governance standard.

We're evaluating both. What's the clearest single question to ask both vendors?

Ask both: "If a regulator asks us to prove that a specific privacy policy was enforced on a specific record in a specific internal database on a specific date — what do you show them?" DataGrail shows a DSR workflow log and a dashboard reflecting request status. Ethyca shows the Fides policy that was applied, the data category it governed, and the enforcement record from the system itself — a log, not a dashboard. That question cuts through the integration count and the feature list. The answer tells you whether you have request management or infrastructure.