Head-to-head · updated 13 September 2026

DataShield vs Protecto: detection is the easy part, what happens next?

Protecto is good at finding sensitive data. Hundreds of entity types, dozens of languages, an inline masker that sits in the live request path, and a free demo you can throw text at without booking a call. We're not going to argue with their detection numbers, and you shouldn't ask us to.

The argument is about the next second. Once something is detected and masked, who decided the agent was allowed to make that call, what happens when you revoke its authority halfway through a job, and can you prove three months later that the log of all this wasn't edited? That's the layer DataShield sells. Below is a row-by-row comparison, including the places Protecto is plainly ahead of us.

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The short version

Pick DataShield when

You'll eventually have to prove an agent's access log wasn't altered. Ours is a hash chain with signed, chained checkpoints, and the verifier tells you whether it was tampering, insertion, deletion, or truncation. Run it yourself.
You need an agent's authority pulled mid-session, with the very next governed tool call failing closed instead of riding out a token's lifetime.
You want to issue the agent's credentials, not watch traffic authenticated somewhere else. We're the identity plane: MCP tool tokens, scope ceilings, JWKS rotation, a Connection Vault. How Auth works.
A subject exercises their right to erasure and your auditor still wants the March logs. Crypto-shred plus ISO 27560 receipts gives you both.

Pick Protecto when

Detection breadth is the requirement. 200+ data types, 50+ languages, and a live PII demo you can test in a browser. We publish no detection rate and don't claim multilingual coverage.
You need masking inside the live request path, right as a prompt heads to a model. Their GPT Guard does that. We deliberately don't sit inline; see the FAQ.
Certifications gate the purchase. SOC 2 Type II, ISO 27001, and a HIPAA BAA are on their side of the table today. We have none of those yet.
You want a managed SaaS with a fast setup and a GCP Marketplace listing, plus named customers and case studies to show your board.

Bottom line: Protecto decides what to mask. DataShield decides what the agent is allowed to do and keeps evidence you can verify without trusting us. If your open risk is detection coverage, buy theirs. If it's authorization and proof, start here.

Feature by feature

Competitor cells describe what Protecto's public site and docs say as of the date above. If we've mischaracterised something, email support@myorg.ai and we'll correct it, credited.

What matters	DataShield	Protecto	Edge
PII/PHI detection	Classification, fingerprinting, and k-anonymity generalization in the dataset platform. We publish no detection rate and make no multilingual claim.	99%+ accuracy claimed, 200+ data types, 50+ languages, and a self-serve live demo. This is their home turf.	◇
Audit evidence	SHA-256 row chain with Ed25519-signed checkpoints that are themselves chained. Verification names the failure: tampering, insertion, deletion, or truncation. Public verifier.	Full event logging described as audit-ready. We found no published cryptographic tamper evidence and no verify endpoint.	◆
Agent authorization	Every governed tool call passes a scope ceiling, a declared authority tier, and a revocation re-check before dispatch. Cedar handles admin, platform, config, and token decisions.	Context-Based Access Control decides at inference time, reading identities from your existing AD or Okta. A decision point, not an issuer.	◆
Break-glass	Scoped, time-boxed emergency access for agents that auto-revokes and can't be quietly removed from the chain.	Not described in their public material.	◆
GDPR erasure	Crypto-shred of per-subject key material plus ISO 27560 consent receipts. Because actor identities are HMAC commitments, no log row has to be deleted.	Described as GDPR ready. The interaction between erasure and retained logs isn't documented.	◆
Tokenization	Datasets are tokenized at ingest; agents query tokenized data over MCP; detokenization is a privileged, audited operation. Tokens are deterministic, join-preserving, and vault-reversible.	Masking in the request path with reversal through their Privacy Vault. Both of us are reversible, which matters for the EDPB pseudonymization argument.	◈
Inline interception	None, by design. Nothing to race at request time because the raw value never sits in the queryable store.	Yes. GPT Guard masks live traffic to and from models.	◇
MCP and agents	Native MCP endpoints on Auth, Library, and Lighthouse. Auth issues MCP tool tokens with scope ceilings and per-call metering attributed to the agent.	Hero copy now names MCP pipelines directly, and a Try the API motion exists. Protection sits on the data path; we found no tool-level agent identity model.	◆
Deployment	Self-hosted, in your VPC, or BYOC, with your keys. No managed SaaS.	SaaS, hosted VPC, air-gapped on-prem, and a GCP Marketplace listing. Broader options than ours.	◇
Maturity signals	Auth v1.0 live. Guardian and Lighthouse v0.2, in production since April 2026. No SOC 2 yet, and we say so. Design-partner terms include source escrow.	Founded 2021, SOC 2 Type II, ISO 27001, HIPAA BAA, named logos and case studies. Both of us are early-stage; their trust surface is further along.	◇
Pricing	Published model, scoped instant quote, no sales wall.	Sales-gated. They used to publish a monthly tier; that page now returns a 404 and Talk to an Expert is the only route to a number.	◆

◆ DataShield leads◇ Protecto leads◈ comparable

Protecto claims are drawn from protecto.ai and our battle-card research, last checked 13 September 2026. They've also surfaced a product called DeepSight that we haven't been able to evaluate yet, so we've left it out rather than guess.

Three things you get here that you won't get from a masking layer

Evidence, not event logs

Most audit logs are rows in a database, and rows in a database can be updated by whoever owns the database. Ours commit to each other, checkpoints are signed and chained, and the verifier reports which failure occurred: deletion, truncation, insertion, tampering. EU AI Act Article 12 and HIPAA §164.312(b) reviewers are testing for exactly that property. Break one yourself, no login.

We issue the credential

A filter reads a decision someone else made. We mint the agent's MCP tool tokens, cap them with a scope ceiling, rotate the signing keys, and re-check revocation on every governed call. An analyst leaves at 14:00 and their agent's next tool call fails, not the one after its token expires. How Auth does it.

Erasure your examiner can live with

GDPR says delete. Your examiner says keep the March logs. Crypto-shred settles it: the subject's key material is destroyed, the data is unreadable, the chain still verifies, and you hand over an ISO 27560 receipt. See the diagram.

Where Protecto is genuinely stronger

Their detection work is better than ours and I'd say so on a call. 200+ entity types across 50+ languages is a real engineering investment, and the live PII demo is a smart piece of proof: you can test the claim yourself in a minute instead of sitting through a deck. They have SOC 2 Type II, ISO 27001, a HIPAA BAA, named healthcare and enterprise customers, an air-gapped deployment option, and a marketplace listing. If a procurement checklist is what stands between you and a purchase order, they clear more boxes today than we do.

One narrow objection. Gartner's read through 2028 is that most unauthorized agent transactions will be internal policy violations rather than attacks. Picture that case: the agent was correctly identified, the data was correctly classified, and the call still shouldn't have happened. No detector fires, because nothing anomalous occurred. Neither does a mutable event log when someone asks you to prove the record is complete. One more thing worth noticing: "zero false negatives" isn't a property open text can have, so ask for the eval set.

Questions worth asking both of us

If we were on the buying side, these are the questions we'd bring. Use them on us too.

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

DataShield: yes, and it's a mechanism rather than a policy. Records commit forward, checkpoints are Ed25519-signed and linked to one another so a deleted checkpoint surfaces too, and a failed verification comes back named: deletion, truncation, insertion, or tampering. There's a live chain at /verify you can try to break. Protecto: their material describes full event logging; we found no published tamper-evidence mechanism and no verifier. Ask them to demonstrate one.

What happens to a revoked agent mid-session?

Revocation here is a state change, not a timer. We re-read authority on every governed tool call, so the call after you pull access fails closed. Protecto's CBAC makes a decision at inference time using identities from your IdP, but we couldn't find a documented mid-session revocation path. Ask how many minutes a compromised agent keeps working after you pull its access, and who has to be awake to do it.

How does GDPR erasure interact with the audit trail?

We erase by destroying key material, not by deleting rows. The subject's data stops being readable, an ISO 27560 receipt records the act, and the chain still verifies because the actors in it are HMAC commitments. Protecto says GDPR ready; the effect of an erasure on retained logs isn't described in their public docs. Ask whether deleting a subject invalidates the log you'd hand an examiner.

Protecto claims 99%+ detection and zero false negatives. What's your number?

We don't have one, and we're not going to invent one. We do classification, fingerprinting, and k-anonymity generalization in the dataset platform, but detection breadth is their strength and we score it that way above. What we'd ask them for is the evaluation set behind the number, because zero false negatives on open text is a claim no NER system can hold. Then ask what the system does about the call that was correctly classified and still shouldn't have been made.

Why doesn't DataShield intercept traffic inline like GPT Guard?

Because inline masking means the raw value was in the request path and you're trusting a matcher to catch it in flight. Datasets are tokenized at ingest; agents query tokenized data over MCP; detokenization is a privileged, audited operation. The trade-off is real and it cuts both ways: if you need free-form prompts scrubbed on the way to a model, Protecto does that and we don't.

Does DataShield have SOC 2?

No. Protecto has SOC 2 Type II and ISO 27001 and we have neither, which is a gap rather than a positioning problem. What we can put on the table is a v1.0 Auth with a published threat model, a verifier you can run before you sign anything, Guardian and Lighthouse at v0.2 in production since April 2026, and source escrow in the design-partner terms. Security page.

Protecto's current hero: "Protect Enterprise Data Across Every AI Interaction," with "One platform across LLMs, agents, and MCP pipelines." — protecto.ai, 13 Sep 2026
Primary CTAs are "Talk to an Expert," "Try the API," and "Try the Live PII Demo" — no self-serve price. — protecto.ai, 13 Sep 2026
The former published pricing tier page now returns a 404; pricing is sales-gated. — protecto.ai/pricing, 13 Sep 2026
Products named publicly: Privacy Vault, GPT Guard, Context-Based Access Control, and DeepSight. — protecto.ai, 13 Sep 2026
≥80% of unauthorized agent transactions through 2028 will be internal policy violations rather than attacks. — Gartner, 2026
EDPB names pseudonymization as a GDPR risk mitigation for AI models. — EDPB Opinion 28/2024

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