Head-to-head · updated 13 September 2026
DataShield vs Protegrity: which tokenization platform can prove what your AI agents did?
Protegrity has been tokenizing private fields since 1996. Banks, insurers, and retailers run their protectors. They sit next to Snowflake and Databricks. Also an old Db2 box. And a core system nobody wants to touch. Their vaultless tokens come from a key. Not a vault lookup. They keep the shape of the field. So old schemas don't break. Say your job is protecting an estate. Big, sprawling, and old. They are a serious answer. We say so twice more below.
The job we do is narrower. We tokenize datasets at ingest. Agents query tokenized data over MCP. Detokenization is a checked, special step. On top of that: we check each governed call. It gets checked against the agent's current access. Each decision gets sealed into a hash chain. You can check it without trusting us. Here is where the two really differ. With sources. This has the rows Protegrity wins.
The short version
Pick DataShield when
- Someone will one day ask you to prove an agent's access log wasn't edited. An examiner, an internal auditor, or Article 12 of the EU AI Act. Our chain answers with math, not a policy PDF. Run the verifier.
- You need to pull an agent's authority mid-session and have the very next tool call fail, instead of waiting for a token to time out.
- You want the vault, the policy engine and the evidence to run on your own infrastructure, on keys you hold, as one stack rather than a fleet of protectors.
- You'd like to see a price before you book a call. Ours is published.
Pick Protegrity when
- Your estate is wide and old. Mainframe, Db2, Oracle, Teradata, Snowflake, Databricks, a dozen SaaS apps. Thirty years of connectors is a real asset and we do not have it.
- You need tokens that keep the shape of the field so downstream schemas and validators keep working untouched. Ours don't do that, on purpose.
- PCI scope reduction is the funded project. They have retail and travel case studies for it; we have no PCI story at all.
- You want data discovery and classification across the whole estate first. We govern data you already know about, we don't go hunting for it.
Bottom line: Protegrity protects the data. DataShield governs what an agent is allowed to do with it, and keeps proof. If your board is asking about the agents rather than the estate, start here.
Feature by feature
Competitor cells describe what Protegrity's public site says. Also its capability pages, and press releases. This is as of the date above. Did we get something wrong? Email support@myorg.ai. We will correct it, and credit you.
| What matters | DataShield | Protegrity | Edge |
|---|---|---|---|
| Audit evidence | SHA-256 hash chain with Ed25519-signed checkpoints. The checkpoints are chained too. The check names the failure. Tampering, insertion, deletion, or truncation. Try the verifier. | Policy-based audit and reporting. Positioned as audit scope reduction. We found no public cryptographic tamper evidence. | ◆ |
| Agent authorization | Every checked tool call passes a scope ceiling. It also passes a declared access tier. And a revoke check. This happens before dispatch. A revoke lands mid-session. | "Verify agents" is the hero line. Team Edition offers agentic policy creation. We found no named per-call token. No access method either. Worth asking them. | ◆ |
| Break-glass | Scoped, time-boxed emergency access for agents. It auto-revokes. It can't be quietly removed from the log. | Not named in their public docs. | ◆ |
| GDPR erasure | Crypto-shred of per-subject key material. Plus ISO 27560 consent receipts. Actor identities are HMAC-committed. So the chain still checks out after erasure. | GDPR compliance is a stated outcome. It comes from field-level protection. The key-destruction method is not spelled out. Nor is its effect on audit history. | ◆ |
| Tokenization | Deterministic, join-preserving, vault-reversible tokens, applied at ingest. We also generalize quasi-identifiers. Dates go to year, decade, or age band. ZIPs go to 3 or 4 digits. Phones, SSNs, and emails get partially masked. Each column gets a measured cardinality-reduction score. Detokenization is checked and logged. | Vaultless tokens, made from a secret key. No lookup vault in the path. Field shape kept. Referential integrity for joins. Plus FPE and dynamic masking. This is their home turf. | ◇ |
| Discovery and classification | We scan a live PostgreSQL source. We profile it, and classify it. It stays in place. No rows leave it. Columns get labelled against 129 field classes. This covers PII, PHI, financial data, and secrets. The catalog carries a business glossary. Also typed lineage, and stewardship queues. That is PostgreSQL today, not your whole estate. SaaS apps, cloud stores, and endpoints? Not ours yet. The other database providers are planned, not built. | ML-powered discovery across structured and unstructured data. This has documents and logs. Shipped down to the free Developer Edition. | ◇ |
| Connector breadth | Modern stack. Postgres, DuckDB over Parquet, Snowflake keypair-JWT. Eight credential strategies in the Connection Vault. Plus polling watch on S3 and Azure Blob. Also GCS, SFTP, and SMB. Nothing like their mainframe and old-system reach. | Protectors for data, application, and cloud. Also SaaS, and core systems. Built up since 1996. Databricks Validated Partner since April 2026. Plus Snowflake, Cloudera, and Exasol. | ◇ |
| MCP and agents | Native MCP endpoints on Auth, Ontology, and Lighthouse. MCP tool tokens carry scope ceilings. Per-call metering ties back to the agent. | They have posted thoughtful commentary on MCP governance. Team Edition targets agentic workflows, in tech preview. We found no MCP tool-token build. | ◆ |
| Inference guardrails | No proxying of your LLM traffic here. We do gate each value. Each one that leaves a checked dataset. This is for a prompt. A PII or PHI column may lack a rule. Not one. Then it gets redacted. A PHI dataset refuses any AI endpoint. It must be marked BAA or ZDR approved. That is a gate. On our own data path. Not an inline product. | Semantic Guardrails inspect prompts and responses. They watch for injection and PII leakage. This runs in each edition. The free one too. | ◇ |
| Deployment | Self-hosted, in your own cloud or data center. Or a dedicated single-tenant server we operate. Your keys. The whole stack. It is not an agent. You don't install it. Not next to someone else's service. | SaaS, self-hosted, on-prem, and hybrid. Listed on the AWS market. Also Azure, and Google Cloud. | ◈ |
| Maturity signals | Auth, Guardian, and Lighthouse are live in production. Guardian and Lighthouse have run since April 2026. SOC 2 not yet certified, and we say so. | Founded 1996. ISO 27001 certified. Named Fortune 500 case studies. Three hyperscaler markets. A global partner network. | ◇ |
| Pricing | Published model, scoped instant quote, no sales wall. | Free Developer Edition on GitHub. Then quote-only for Team and Enterprise. No dollar figures on any edition page. | ◆ |
◆ DataShield leads◇ Protegrity leads◈ comparable
Protegrity claims come from protegrity.com capability pages. Also their own newsroom. Last checked 13 September 2026. We link them below. We don't paraphrase from memory.
Three things you get here that you won't get from a data protection platform
Proof that survives an audit
A log that can be edited without a trace? It has no evidentiary value. Ours is a hash chain with signed checkpoints. The verifier tells you what went wrong. Not just that something did. That's what EU AI Act Article 12 reviewers want. Also HIPAA §164.312(b) reviewers. Try it in your browser. No signup.
Authority that can change mid-flight
An analyst quits on a Tuesday. Their agent is 20 minutes into a 40-minute job. With DataShield, we re-check the next checked tool call. We check it against current access. It fails closed. Field-level protection alone won't stop that call. The token is valid. The rule still says analyst. How Auth does it.
An erasure you can defend
GDPR says delete. Your auditor says keep the log. Crypto-shred settles it. We destroy the subject's key material. The data goes unreadable. The chain still checks out. See the diagram.
Where Protegrity is genuinely stronger
Let's be straight about the gap. Protegrity has been doing this a long time. Before most of our team could drive. Their vaultless tokens skip the lookup. No need for one on the hot path. They keep the shape of the field. So downstream code doesn't care. Their joins stay consistent. They classify private data. Across structured and unstructured sources. They became a Databricks Validated Partner in April 2026. They ship on three cloud markets. In September 2025, they put out a Developer Edition. Free, on GitHub. You can run it in Docker this afternoon. Thirty years of connector work is real. Not something a smaller team talks its way around.
The push-back is about the boundary. Which one does the control sit on? Protegrity is very good at the data boundary. What the field looks like. Who may see it in the clear. What the model gets handed. The agent boundary is not the same. Gartner expects most unauthorized agent transactions through 2028. Most will be internal policy violations. Not attacks. That means the agent had valid access. It did something it shouldn't have. Their own September 2026 post has a title. "Insider Risk After Access Is Granted". That's the right question. Our answer is a method. Access re-checked per checked tool call. Break-glass with an expiry. A signed chain. It tells you afterwards what happened. Ask them for theirs.
Questions worth asking both of us
These are the questions we'd want answered. If we were buying. Ask them on each vendor call, ours too.
Can you cryptographically prove an audit log entry wasn't deleted?
DataShield: yes. Each record commits to the one before it. Checkpoints are signed and chained. The check tells deletion apart from truncation. It also spots tampering. Run it against a sample chain at /verify. Protegrity: their docs describe policy-based auditing. Also audit scope reduction. We found no public tamper-evidence method. Ask them to show one.
What happens to a revoked agent mid-session?
DataShield re-checks access on each checked tool call. So a revoke bites on the next call. Protegrity's hero line says "verify agents". Their Team Edition covers agentic policy creation. But we could not find one. A named per-call authorization method. It was not in their public docs. Ask how long a compromised agent keeps working. This is after you pull its access.
How does GDPR erasure interact with the audit trail?
DataShield crypto-shreds per-subject key material. It also issues an ISO 27560 consent receipt. Actor identities in the chain are HMAC-committed. The proof still checks out. Even once the subject is gone. Protegrity lists GDPR among the regimes it covers. But the key-destruction step is not named. Its effect on the audit history? Also not named. Ask whether erasing a subject breaks the log.
Is DataShield a Protegrity alternative for enterprise tokenization?
Only for part of it. Say you need field-level defense. Pushed into a mainframe. Also a dozen databases, and a SaaS estate. They win, and it isn't close. Say your project is this. Our AI agents need safe queries on customer data. We have to show our work. We're built for that. Our tokens are deterministic, join-preserving, and vault-reversible. They are not shape-preserving. So a column that expected a 16-digit string? It will notice.
Protegrity has a free Developer Edition. What's your equivalent?
Free Demo Center access, with a work email. There you can break a live audit chain. You can revoke an agent mid-session. Watch the next call fail. Theirs is not the same. A Docker install. With discovery, protect APIs, and Semantic Guardrails. Released on GitHub in September 2025. Want to write code against a protect API? Tonight? Theirs is the faster path. We publish our pricing. No gating the number.
Does DataShield have SOC 2?
Not yet, and we will not imply otherwise. Protegrity holds ISO 27001 and has shipped since 1996. On paper, they are ahead. What we offer instead: Auth is live. It has a public threat model. It also has a verifier anyone can run. Guardian and Lighthouse have run in production since April 2026. Design-partner terms include source escrow. A small vendor is then not one point. Not a single point of failure. Details on the security page.
- Protegrity's current hero: "Protect data. Verify agents. Govern inference". Primary CTAs are "Book a demo". Also "Start building for free". - protegrity.com, 13 Sep 2026
- Vaultless tokens come from a secret key algorithm. There is no central vault lookup. They keep joins and analytics consistent. - protegrity.com capabilities, 13 Sep 2026
- Free Developer Edition released on GitHub. It has discovery, Find & Protect APIs, and Semantic Guardrails 1.0. It runs locally in Docker. - Protegrity newsroom, 30 Sep 2025
- AI Team Edition, announced as a tech preview. For agentic workflows. It covers agentic policy creation and semantic guardrails. - Protegrity newsroom, 17 Nov 2025
- Databricks Validated Partner status. It covers tokenization, encryption, and dynamic masking. This spans Unity Catalog checked assets. - Protegrity newsroom, 10 Apr 2026
- At least 80% of unauthorized agent transactions, through 2028. Internal policy violations, mostly. That's the claim. Not attacks. - Gartner, 2026
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DirectDataShield vs VGS
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AllEvery comparison
One honest scorecard per vendor. This has the rows we lose.
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