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Head-to-head · updated 13 September 2026

DataShield vs Protect AI: scanning the model, or proving what the agent did?

Protect AI made ML supply-chain security a real category. They coined MLSecOps, shipped Guardian to scan model artifacts for embedded code, built Recon to red-team AI systems, and ran huntr, the first bug bounty platform for AI and ML. Palo Alto Networks bought them for $634.5M in July 2025. Today protectai.com redirects to the Prisma AIRS page, and Guardian and Recon live on as AI Model Security and AI Red Teaming.

We don't scan models and we don't red-team them. We govern what an agent is allowed to do with your data, and we keep proof. Datasets are tokenized at ingest; agents query tokenized data over MCP; detokenization is a privileged, audited operation. Below is where the two fit together, and the rows where they win.

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DataShield vs Protect AI at a glanceEight questions regulated buyers ask us. Scored from each vendor's public documentation. DataShield vs Protect AI at a glance Eight questions regulated buyers ask us. Scored from each vendor's public documentation. DataShield Protect AI 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 Tokenized data for agent queries Pricing you can see before a call Model artifact and supply-chain scanning AI red teaming at scale shipped partial / roadmap not offered Sources at the bottom of this page.

The short version

Pick DataShield when

  • An examiner, or the EU AI Act's Article 12, will ask you to prove an agent's access log wasn't edited. A scan report says the model was clean in March. It doesn't say what the agent read in June. Run the verifier.
  • You need to pull an agent's authority mid-session and have the very next tool call fail, not wait for a token to expire.
  • The data the agent touches is PII or PHI, and you'd rather it query tokens than raw values. How that works.
  • You want a vendor you can buy on its own, self-host, and price from a public page.

Pick Protect AI when

  • You're pulling models off Hugging Face and need to know none of them carry a malicious payload. Palo Alto scans 35+ model file types for 25+ threat categories. We do none of that.
  • You want adversarial testing, not just controls. Their red teaming runs 50+ techniques mapped to OWASP Top 10 and NIST AI RMF, with multi-turn attacks added in 2026.
  • You already run Palo Alto. The AI security line rides your existing contract, channel, and support relationship.
  • You want the huntr researcher community and Advanced WildFire threat intel feeding your model scans. That's scale we can't match.

Bottom line: Protect AI tests whether your AI can be broken. DataShield decides whether the agent was allowed and proves it afterwards. Those are different jobs, and plenty of teams should buy both.

Feature by feature

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

What mattersDataShieldProtect AIEdge
Audit evidenceSHA-256 hash chain with Ed25519-signed checkpoints that are themselves chained. Verification names the failure: tampering, insertion, deletion, or truncation. Public verifier.Scan reports and findings history. We found no published cryptographic tamper evidence for runtime logs.◆
Agent authorizationEvery governed tool call passes a scope ceiling, an authority tier, and a revocation re-check before dispatch.Red teaming tells you which agent behaviours are reachable. Enforcement of per-call authority isn't described in the Protect AI lineage.◆
Break-glassScoped, time-boxed emergency access for agents that auto-revokes and can't be quietly deleted from the log.Not offered.◆
GDPR erasureCrypto-shred of per-subject key material plus ISO 27560 consent receipts. The audit chain stays verifiable after erasure.Not a data-subject product. No erasure mechanism described.◆
Tokenization and data handlingDatasets are tokenized at ingest; agents query tokenized data over MCP; detokenization is a privileged, audited operation. Tokens are deterministic, join-preserving, and vault-reversible, with quasi-identifier generalization (dates to year, decade or age band; ZIPs to 3 or 4 digits; partial phones, SSNs and emails) with a measured cardinality-reduction score per column for quasi-identifiers.Scans model artifacts, not your records. No tokenization layer in this lineage.◆
Model supply-chain scanningNone. We assume you got the model from somewhere sensible.Guardian's descendant scans models in place at selection or predeployment across 35+ file types, for 25+ threat categories, backed by Advanced WildFire and huntr intel.◇
Red teamingNone. We publish a threat model and a verifier you can attack yourself.Recon's descendant stress tests agents against thousands of scenarios, 50+ techniques mapped to OWASP Top 10 and NIST AI RMF.◇
MCP and agentsNative MCP endpoints on Auth, Ontology, and Lighthouse. MCP tool tokens with scope ceilings; per-call metering attributed to the agent.MCP threat research and webinar content. Concrete per-tool-call controls weren't detailed on the pages we read.◆
Open sourceNo open-source product today. A public verifier and a published threat model instead.ModelScan is Apache-2.0 and still up. LLM Guard, the Layer lineage, was archived on 9 July 2026 as no longer maintained.◇
DeploymentSelf-hosted in your own cloud or data center, or on a dedicated single-tenant server we operate. Your keys.Delivered as part of Prisma AIRS. Palo Alto's SaaS and hybrid options apply; standalone Protect AI deployment is gone.◆
IndependenceBuy one module. Run it yourself. Design-partner terms include source escrow.protectai.com now redirects to Palo Alto's Prisma AIRS page. There's no separate Protect AI product, brand, or SKU to buy.◆
Maturity signalsAuth, Guardian and Lighthouse are live in production (Guardian and Lighthouse since April 2026). SOC 2 not yet certified, and we say so.Founded 2022, acquired for $634.5M by a public company with a global enterprise sales force. Nobody gets fired for this one.◇
PricingPublished model, scoped instant quote, no sales wall.No published pricing at any point. The Prisma AIRS page still leads with "Get preview and early access."◆

◆ DataShield leads◇ Protect AI leads◈ comparable

Protect AI claims are drawn from Palo Alto Networks' AI security pages, the July 2025 acquisition release, and the surviving Protect AI open-source repositories, last checked 13 September 2026. We link them below rather than paraphrase from memory.

Three things you get here that you won't get from a model scanner

Proof that survives an audit

A clean scan report tells you the artifact was safe. It says nothing about the call an agent made last Tuesday. Our log is a hash chain with signed checkpoints, and the verifier names 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. Their agent is still running a 40-minute job. With DataShield the next governed tool call is re-checked against current authority and fails closed. Red teaming would have found that the agent could make the call. It can't stop it. How Auth does it.

An erasure you can defend

GDPR says delete. Your auditor says keep the log. Crypto-shred resolves that: the subject's key material is destroyed, the data becomes unreadable, and the chain still verifies. See the diagram.

Where Protect AI is genuinely stronger

We'd rather you hear this from us. Protect AI did the unglamorous work first. Pickle files can execute code, almost nobody was checking, and Guardian checked. They built huntr and paid researchers to break AI systems in public, which produced real threat intel rather than a marketing deck. Recon turned that into automated red teaming, and inside Palo Alto it now runs 50+ techniques mapped to OWASP and NIST at a scale a startup can't fund. If your risk is the model you just downloaded, buy that. We have nothing to sell you there.

The push-back is about what a finding does next. Red teaming ends with a report saying an agent can be talked into calling a tool it shouldn't. Somebody still has to make that call fail, and somebody still has to prove, months later, which calls were allowed. Gartner expects most unauthorized agent transactions through 2028 to be internal policy violations rather than attacks, and a policy violation looks perfectly normal to a scanner. The other thing worth naming: the version of Protect AI you liked is not for sale any more. protectai.com redirects to a platform page with an early-access button, and LLM Guard was archived in July 2026. If you came here searching for a Protect AI alternative, that's probably why.

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. Each record commits to the one before it, checkpoints are signed and chained, and verification tells deletion from truncation from tampering. Run it on a sample chain at /verify. Protect AI: their lineage is scanning and testing, and we found no published tamper-evidence mechanism for runtime logs. Ask Palo Alto to show one.

What happens to a revoked agent mid-session?

DataShield re-checks authority on every governed tool call, so revocation lands on the next call. Protect AI's heritage products don't sit in the call path, so the answer there depends on whatever Prisma AIRS agent security does today. Ask how long a compromised agent keeps working after you pull its access.

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 are HMAC-committed, so the evidence stays verifiable after the subject is gone. Protect AI doesn't hold subject data, so this question really goes to whoever stores your records.

Can I still buy Protect AI on its own?

Not as far as we can tell. protectai.com 301-redirects to Palo Alto's Prisma AIRS page, and the names Guardian, Recon and Layer no longer appear in the product line. The functions survive as AI Model Security and AI Red Teaming inside the platform. If a standalone SKU exists, ask your rep to put the line item in writing. Our page on Prisma AIRS covers the platform itself.

Is DataShield a replacement for model scanning?

No, and anyone who tells you otherwise is selling. We never look inside a model file. We govern the data an agent can reach and record the decision. Most teams shipping agents on regulated data need both a scanner at build time and an authorization layer at run time.

Does DataShield have SOC 2?

Not yet, and we won't imply otherwise. Auth is live a public threat model and a verifier anyone can run. Guardian and Lighthouse have been in production since April 2026. Design-partner terms include source escrow so a small vendor isn't a single point of failure. Details on the security page.

Other head-to-heads

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DataShield vs Prisma AIRS

The platform Protect AI was folded into, scored on its own terms.

 Adjacent

DataShield vs HiddenLayer

Model threat detection above, agent authorization underneath.

 Adjacent

DataShield vs Lakera

Guardrails catch the prompt. We decide the call and keep the proof.

 All

Every comparison

One honest scorecard per vendor, sources at the bottom.

See both mechanisms run in your browser: break a live audit chain, revoke an agent mid-session, then decide what your scanner still leaves open. Demo Center access is free with a work email.

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