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Head-to-head · updated 13 September 2026

DataShield vs Tonic.ai: safe test data, or data your agents can still use?

Tonic.ai is very good at a real job. Tonic Structural de-identifies and subsets your live database. Engineers get a safe copy. Tonic Textual redacts or synthesizes free text, PDFs, audio and code. Tonic Fabricate invents data from scratch through a chat interface. Is your staging setup starving? Buy Tonic. We don't build test setups. We won't pretend.

DataShield answers a different question. Not "how do I make a safe copy?" Instead: "what can this agent touch live, and can I prove what it did?" We tokenize datasets at ingest. Agents query tokenized data over MCP. Turning a token back is a special, logged step. It goes back to the real value when someone with access needs it. Redacted text never does. That one trait is most of this page.

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DataShield vs Tonic.ai at a glanceEight questions regulated buyers ask us. Scored from each vendor's public documentation. DataShield vs Tonic.ai at a glance Eight questions regulated buyers ask us. Scored from each vendor's public documentation. DataShield Tonic.ai Reversible, join-preserving tokens Tamper-evident audit chain you can verify Authority re-checked on every tool call Break-glass access for agents GDPR erasure that keeps the chain valid Synthetic test data from production patterns Unstructured redaction across PDFs, audio, code Published entry-level pricing shipped partial / roadmap not offered Sources at the bottom of this page.

The short version

Pick DataShield when

  • The de-identified data feeds a production agent, not a test database. Someone will eventually need the real value back, under policy, with a log. How tokens work.
  • You need to prove an agent's access log wasn't edited. Ours is a hash chain with signed checkpoints, and the verifier names the failure. Run it yourself.
  • You need to pull an agent's access mid-session and have the next tool call fail, not wait for a token to expire. How Auth does it.
  • A GDPR erasure request has to land on live data without breaking the audit trail you promised your examiner.

Pick Tonic.ai when

  • You need safe test data. Tonic Structural has patented subsetting and keeps primary and foreign keys intact across a real schema. That is hard, and they did it years ago.
  • You need to generate data that never existed, for load tests, demos or agent training runs. Fabricate does it from a chat prompt and even builds mock APIs.
  • Your sensitive data is free text, PDFs, audio, images or code. Textual runs its own NER models across 50+ languages. We classify structured datasets; that is a narrower surface.
  • You want the output to be irreversible on purpose, with an Expert Determination partner willing to certify it.

Bottom line: Tonic makes a safe copy of your data. DataShield governs the live data your agents reach, and keeps proof. Plenty of teams buy both, and we'd tell you to. If the fight is over an AI agent touching live records, start here.

Feature by feature

Competitor cells describe what Tonic.ai's public site and docs say. We use the date above. If we got something wrong, email support@myorg.ai. We'll fix it and credit you.

What mattersDataShieldTonic.aiEdge
De-identified outputDeterministic, join-preserving, vault-reversible tokens, applied at ingest. An authorized human can get the real value back. That step is logged.Redaction or synthesis. One way by design. A synthesized name looks real, but it's not the real one. That's exactly what a test database wants.◆
Synthetic test dataNone. We don't generate data and we don't build test setups.Three products for it. Structural masks and subsets live data. Fabricate generates from scratch with a chat agent and a paired Validation Agent.◇
Unstructured dataPII and PHI classification across 129 field classes. Plus quasi-identifier generalization: dates to year, decade or age band; ZIPs to 3 or 4 digits; partial phones, SSNs and emails. Each column in Ontology gets a measured cardinality-reduction score. Files and free text are not our strength.Tonic Textual: proprietary NER across 50+ languages, over PDFs, text, audio, images and code. Deeper than ours here.◇
Audit evidenceSHA-256 hash chain with Ed25519-signed checkpoints that are themselves chained. Verification names the failure: tampering, insertion, deletion or truncation. Try the verifier.Audit trails and privacy reports inside the product. We found no listed cryptographic tamper evidence.◆
Agent authorizationEach checked tool call passes a scope ceiling and an access tier. It gets a re-check before dispatch, each time. A pulled grant lands mid-session.Role-based access control and workspace sharing for people using the product. We found no per-call agent access model.◆
Break-glassScoped, time-boxed emergency access for agents. It auto-revokes and can't be quietly deleted from the log.Not offered, and not really their problem to solve.◆
GDPR erasureCrypto-shred of per-subject key material plus ISO 27560 consent receipts. The audit chain still verifies afterward.Their answer is upstream: the copy never held the real values. That helps test data. It does nothing for the live row.◆
MCP and agentsNative MCP endpoints on Auth, Ontology and Lighthouse. MCP tool tokens carry scope ceilings. Metering ties back to the agent, always.A Textual MCP server lets an external agent send redaction requests. Real and shipped. It covers the redaction job. Not what an agent may read.◆
DeploymentSelf-hosted, in your own cloud or data center, your call. Or on a dedicated single-tenant server we operate. Your keys, always.Cloud, self-hosted via Kubernetes or Docker, plus AWS, Google Cloud and Snowflake marketplace listings. Genuinely flexible.◈
Compliance postureDesigned to map to EU AI Act Article 12 and HIPAA §164.312(b). No SOC 2 certificate yet, and we say so on each page.HIPAA Safe Harbor support and a partner network of Expert Determination providers who certify output against HIPAA, GDPR and PCI.◇
Maturity signalsAuth, Guardian and Lighthouse are live in production (Guardian and Lighthouse since April 2026).Founded 2018. $8M Series A, $35M Series B. Acquired Fabricate in 2025. Customers named on their own site include eBay, Philips, GitLab, Cisco and PayPal.◇
PricingListed pricing, scoped instant quote, no sales wall.Fabricate is $0 free and $29 a month for Plus. Textual is pay-as-you-go per 1,000 words. Structural is quote-only at both tiers.◈

◆ DataShield leads◇ Tonic.ai leads◈ comparable

Tonic.ai claims come from tonic.ai, docs.tonic.ai and Tonic's own product and pricing pages. Last checked 13 September 2026. We link them below, not just a paraphrase from memory.

Three things you get here that you won't get from a test data platform

A token that can go back

Redaction is a one-way street. That's fine when the destination is staging. It's a wall when a support agent finds the right client. Now the right person needs the real account number. Our tokens are deterministic and join-preserving, so analytics still work. Turning a token back is a logged, controlled call. See the data model.

Proof that survives an audit

A log that can be silently edited proves nothing. Ours is a hash chain with signed checkpoints. The verifier tells you what went wrong, not just that something did. That's the property EU AI Act Article 12 and HIPAA §164.312(b) reviewers care about. Try it in your browser, no signup.

Authority that can change mid-flight

An analyst leaves on a Friday. Their agent is still grinding through a 40-minute job. DataShield re-checks the next checked tool call against current access, right then. It fails closed. How Auth does it.

Where Tonic.ai is genuinely stronger

Most of it, honestly, in their own lane. Tonic has been at this since 2018. Structural holds a patent on its database subsetting. Keeping keys straight across a live enterprise schema is harder than it sounds. Textual's NER models cover 50+ languages. They read PDFs, audio, images and code too, a wider range than we classify. Fabricate lets an engineer describe a dataset in a sentence. It hands back relational data plus mock APIs. They post real prices for two of three products. They self-host. Three cloud marketplaces carry them too. They also have an Expert Determination partner network that will certify output against HIPAA. We have none of that. A test data buyer should pick them.

Here's the push-back. Their whole model assumes you can leave the sensitive data behind. Copy it, scrub it, ship the copy. That works until the agent is live, reading real records. Then the answer has to be right for a named client at 9am. Being one-way stops being a safety trait right there. It becomes a dead end instead. The audit trail stops being a nice-to-have, too. Someone is asking who let the read happen. Tonic's buyer is a data engineering lead trying to unblock a release. Ours is whoever has to sign the thing afterward.

Questions worth asking both of us

These are the questions we'd want answered if we were buying. Ask them on each vendor call, including ours.

Can you cryptographically prove an audit log entry wasn't deleted?

DataShield: yes. Each record commits to the one before it. We sign and chain each checkpoint too. Verification tells deletion apart from truncation and from tampering. Run it on a sample chain at /verify. Tonic.ai: their products include audit trails and privacy reports. We found no listed tamper-evidence mechanism. Ask them to show one.

What happens to a revoked agent mid-session?

DataShield re-checks access on each checked tool call. A pulled grant bites on the very next call. Tonic's access controls cover users of the product. We found no description of agent access tiers or mid-session pulls. Fair enough: a masking job isn't a long-running agent. If yours is, ask how long a pulled agent keeps working.

Is Tonic.ai a good alternative for de-identifying data for LLMs?

For training corpora and RAG stores, yes. Tonic Textual exists for exactly that. It redacts or synthesizes the sensitive spans before data reaches a model. The catch is direction of travel. Redact the text, and you can't recover the original. Synthesize it, and the plausible-looking value in your index is simply wrong. For a fine-tune, that's fine. For an agent answering about a real client, it isn't. That's where our tokens fit: same protection going in, a policy-gated route back out.

Do you replace Tonic Structural for test data?

No. We don't generate synthetic data. Nor do we subset databases. We don't set up test copies either. If that's the rule, Tonic is the better buy. We'll say that on the call. We care about the live side. What agents may read. What they actually read. And what you can prove later.

How does GDPR erasure interact with the audit trail?

DataShield crypto-shreds per-subject key material and issues an ISO 27560 consent receipt. Actor identities in the chain carry HMAC commitments, not names. It still checks out after the subject is gone. Tonic's answer works upstream: the safe copy never held the real value. That's a good answer for staging. It's not an answer for the live record an examiner is asking about.

Does DataShield have SOC 2?

Not yet, and we won't say otherwise. Tonic's site references SOC 2 alongside HIPAA, PCI and GDPR support. On paper, they're ahead of us there. What we offer instead: Auth is live, with a public threat model and a verifier anyone can run. Guardian and Lighthouse have been live since April 2026. Design-partner terms include source escrow. That way a small vendor isn't a single point of failure. Details on the security page.

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Every comparison

One honest scorecard per vendor, including the rows we lose.

See both mechanisms run in your browser. Break a live audit chain. Revoke an agent mid-session. Then turn a token back under policy, and watch us log it. Demo Center access is free with a work email.

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