Head-to-head · updated 13 September 2026
DataShield vs Immuta: mask the answer, or tokenize the data?
Immuta has done data access rules since 2015. They're good at it. Rules by trait, over Snowflake and Databricks. Live masking at query time. A data market, so analysts stop filing tickets. Their Agentic Data Access launch hands short-lived database roles to AI agents. That was April 2026. It logs both the agent and the human behind it. Does your sensitive data live in a warehouse? Is your problem who may see which rows? They're a serious answer.
We come at it from the other end. We tokenize data at ingest. Agents query it over MCP. Turning a token back is a special, audited step. So the copy the agent reads never holds the raw value. The token can turn back into a name. Only when the rule says yes. Immuta's masking runs one way, at read time. The data stays raw underneath. That single gap drives most of the rows below. It includes the ones they win.
The short version
Pick DataShield when
- You want the agent's copy of the data to be tokenized already. Not raw with a mask over the output. Tokens are fixed and join-preserving. Analytics still work. How the tokens behave.
- An auditor will ask you to prove the access log wasn't edited. Ours is a hash chain with signed checkpoints. You can check it yourself. Run the checker.
- You need to pull an agent's access mid-session. The very next tool call should fail. Not the next task.
- You'd rather read a price than book a call. Ours is published.
Pick Immuta when
- Your sensitive data sits in Snowflake, Databricks, BigQuery, Redshift or Lake Formation. You want rules pushed into the platform itself. That's their home turf. They've held it for a decade.
- You need deep ABAC. Rules like "allow if User.Region = Data.Region" scale across thousands of tables. No minting new database roles. Ours is a scope and tier model, not a full trait algebra.
- You want a data market: browsable data products, self-serve requests, auto approvals. We don't ship that.
- Buying committee comfort matters. Eli Lilly, JP Morgan Chase, GM and Novartis sit on their homepage. We're a young vendor, and we say so.
Bottom line: Immuta governs who may read raw data in your warehouse. DataShield changes what the data is, before an agent ever touches it. Then it proves what happened. Are your agents the risk? Does your proof need to survive an examiner? Start here.
Feature by feature
Competitor cells describe what Immuta's public site and docs say. We use the date above. Did we get something wrong? Email support@myorg.ai. We'll fix it and credit you.
| What matters | DataShield | Immuta | Edge |
|---|---|---|---|
| How sensitive fields are protected | Fixed, join-preserving, vault-reversible tokens, applied at ingest. Plus generalization on quasi-identifiers. Dates become year, decade, or age band. ZIPs become 3 or 4 digits. Phones, SSNs, and emails get partial masking. Each column gets its own score. The stored copy holds no raw value. Turning a token back is a special, logged step. | Live masking, applied to query results. Raw values stay in the source table. The mask is one way, made at read time. | ◆ |
| Warehouse-native enforcement | We govern the data sets and the MCP surface we manage. We don't push rules into your Snowflake or Databricks account. | Rules run right inside Snowflake and Databricks. Plus BigQuery, Redshift, AWS Lake Formation. Ten years of depth here. | ◇ |
| Policy model | Cedar rule engine, with grant expansion. Rule explain for admin, config, and token calls. On the dispatch path, each checked tool call passes a token scope ceiling. Plus an access tier and a revoke re-check. | Mature ABAC and policy as code. Trait matching between user and data. Immuta AI and Copilot help write the rules. | ◇ |
| Audit evidence | A SHA-256 hash chain with Ed25519-signed checkpoints, themselves chained. Verification names the failure: tampering, insertion, deletion, or truncation. Try the checker. | Audit of actions in Immuta, and queries against your data. Plus dual-identity logs for agents. We found no public tamper proof. | ◆ |
| Agent authorization | Each checked tool call passes a scope ceiling. Plus an access tier. It gets a revoke re-check before dispatch. A pulled grant lands mid-session. | Short-lived database roles, handed out per task and revoked after it. Called a kill switch. Agents can also file access requests for approval. | ◆ |
| Break-glass | Scoped, time-boxed urgent access, just for agents. It auto-revokes on its own. It can't be quietly deleted from the log. | Access request flows, with manual or auto approval. No urgent path with a forced expiry described. | ◆ |
| GDPR erasure | Crypto-shred of per-subject key material. Plus ISO 27560 consent receipts. The audit chain still checks out after erasure. | Immuta governs access to data it doesn't hold. Erasure happens in the source system. We found no crypto erasure tool in their docs. | ◆ |
| MCP and agents | Native MCP endpoints on Auth, Ontology, and Lighthouse. MCP tool tokens carry scope ceilings, always. Per-call metering ties back to the agent. | MCP support sits in their resource library. Named ties include Claude, ChatGPT, and Gemini. We found no documented tool-token model. | ◈ |
| Self-service data products | A catalog with asset and column metadata. A glossary. Typed lineage you can trace hop by hop. Steward queues with rule sets. No market with request and approval flows. | Internal Data Marketplace since late 2024: data products, self-serve requests, auto approvals. | ◇ |
| Deployment | Self-hosted, or a dedicated single-tenant server. Your keys, including the ones that turn tokens back. | SaaS and self-managed options. Ties with Okta, Active Directory, Alation, and Collibra. | ◈ |
| Maturity signals | Auth, Guardian and Lighthouse are all live. Guardian and Lighthouse have run since April 2026. SOC 2 isn't certified yet, and we say so. | Founded 2015, Boston. Named Fortune 100 customers. Steady release pace. CRN Big Data 100 listing in 2025. | ◇ |
| Pricing | Published model, scoped instant quote, no sales wall. | Quote-only. We couldn't reach a pricing page at all. Not on the date checked. | ◆ |
◆ DataShield leads◇ Immuta leads◈ comparable
We drew Immuta claims from immuta.com, their newsroom, and their docs. Last checked 13 September 2026. We link the sources below, not just a paraphrase from memory.
Three things you get here that you won't get from data access governance
Data that's already safe to read
A mask hides the answer. The raw value still sits in the table. One rule mistake away. We tokenize at ingest. The copy the agent queries never holds the name. Or the number. Tokens are fixed, so joins across data sets still line up. Masking versus tokenization, in detail.
Proof that survives an audit
A log that can be quietly edited proves nothing. Ours is a hash chain with signed checkpoints. The checker tells you what broke. Not just that something did. That's what EU AI Act Article 12 reviewers care about. HIPAA §164.312(b) too. Try it in your browser, no signup.
Authority that can change mid-flight
An analyst resigns at 2pm. Their agent is 20 minutes into a 40-minute job. With DataShield, we re-check the next checked tool call. Against current access, right then. It fails closed. A short-lived role still lives until the task ends. How Auth does it.
Where Immuta is genuinely stronger
Let's be fair about the gap. Immuta has spent ten years pushing rules into Snowflake and Databricks. Plus BigQuery, Redshift, and AWS Lake Formation. That work is hard and unglamorous. Their ABAC model reaches further than ours. One trait rule can govern thousands of tables. No one has to mint a new database role. They have an internal data market. An Alation tie-up for the catalog handoff. Customers like Eli Lilly and JP Morgan Chase. Names that have clearly gone through procurement. Is your governance problem warehouse-shaped? That depth is worth paying for. We'd tell you so on a call.
Here's the push-back. Live masking makes an assumption. The raw value stays in the table, hidden on the way out. That was a fair bet, once. Back when queries came from analysts on a VPN. It's a worse bet when the reader is an agent. One a bad actor could talk into asking a different question. Their own framing agrees the model has to change. The CEO calls it a governance reset. We just think the reset should go further than handing out faster roles. Change the data itself. Keep the key somewhere special. Make the record of every call something a stranger can check.
Questions worth asking both of us
These are the questions we'd want answered, if we were buying. Ask them on every vendor call, including ours.
Can you cryptographically prove an audit log entry wasn't deleted?
DataShield: yes, it can. Each record commits to the one right before it. We sign and chain checkpoints. Verification tells deletion apart from truncation and from tampering. Run it against a sample chain at /verify. Immuta: their docs describe audit of Immuta actions. Plus queries on your data. Plus dual-identity logs for agents. We found no tamper-proof method published. Ask them to show one.
Is the masking reversible, and does that matter?
It matters more than people expect. Immuta's live masking hits query output and runs one way. A system further along that needs the real value. It goes back to the raw table. Our tokens are vault-reversible, under rule. Turning one back is a special, audited step. The trade is honest. One-way masking is simpler to reason about. If nothing further along ever needs the real value, it's enough.
What happens to a revoked agent mid-session?
DataShield re-checks access on each checked tool call. The pull takes effect on the next call. Immuta hands out short-lived database roles and pulls them back when the task finishes. They call this a kill switch. Ask how long "until the task finishes" really is for a long-running agent. And what happens to work already in flight.
How does GDPR erasure interact with the audit trail?
DataShield crypto-shreds per-subject key material and issues an ISO 27560 consent receipt. Actor IDs in the chain carry HMAC marks. The proof still checks out. Even once the subject is gone. Immuta governs access to data it doesn't store. So erasure lands as a request in the source system. Ask both of us whether erasing a subject breaks the log.
We're on Snowflake and Databricks already. Doesn't that favour Immuta?
On rules run inside the warehouse, yes. We won't pretend otherwise. Immuta pushes rules into the platform. We don't. Where we fit is the layer in front of the agent. Tokenized data sets served over MCP. Per-call access checks. Proof you can check. Plenty of teams would run both. Can you only fund one? Are your agents the thing keeping you up? The order changes then.
Does DataShield have SOC 2?
Not yet. We won't imply otherwise, ever. Auth is live now. It ships with a public threat model. Plus a checker anyone can run. Guardian and Lighthouse have run 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.
- Immuta's hero positioning: "Provision your data." Platform pillars: Govern, Provision and Comply. Primary CTA is "Book a Demo." - immuta.com, 13 Sep 2026
- Agentic Data Access is headlined "Secure data access for AI agents." It describes ABAC, ephemeral access vending, delegated access policies, and dual-identity logging. The page names no tokenization or encryption mechanism. - immuta.com/platform/agentic-data-access, 13 Sep 2026
- Agentic Data Access launched April 2026. Immuta AI and Copilot launched March 2025. Internal Data Marketplaces launched late 2024. The Alation partnership started September 2025. - Immuta newsroom, 13 Sep 2026
- Immuta's documentation describes audit of actions in Immuta. Plus audit of queries on your data. No cryptographic tamper evidence found. - documentation.immuta.com, 13 Sep 2026
- MCP support appears in Immuta's resource library, as product videos. Not as a public tool-token authorization model. - immuta.com/resources, 13 Sep 2026
- EDPB names pseudonymization as a GDPR risk mitigation for AI models. - EDPB Opinion 28/2024
Other head-to-heads
DataShield vs Privacera
Access governance over the lake versus tokens plus evidence.
DirectDataShield vs Okera
Attribute-based masking, and what happens after the query.
DirectDataShield vs BigID
Discovery finds the data. Someone still has to govern it.
AllEvery comparison
One honest scorecard per vendor, sources at the bottom.
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