Head-to-head · updated 13 September 2026
DataShield vs Noma Security: breadth of coverage, or proof of what happened?
Noma Security is a serious rival. Pretending otherwise wastes your afternoon. They've raised $132M. They ship discovery and posture checks. Plus red teaming and runtime protection. All in one console. They added agent-level access control for MCP servers. That happened in June 2026. In July, they became a partner. They now link to Anthropic's Claude Compliance API. Your question might be this. What AI runs across my whole estate? Is any of it dangerous? They answer that question. We don't.
Our question is narrower. It starts later. An agent is already touching regulated data. Can you prove which calls were allowed? Can you get a masked value back? Legal may need a lawful re-identification. Can you pull an agent's access mid-job? We tokenize datasets at ingest. Agents query tokenized data over MCP. Detokenization is a special, checked step. We seal each checked call into a hash chain. Your auditor can check it. No help from us needed. Here's where the two differ, with sources.
The short version
Pick DataShield when
- An examiner will one day ask you to prove an agent's access log wasn't edited, and "it's searchable" isn't an answer. Ours is a hash chain with signed, chained checkpoints. Run the verifier.
- Masking one-way would break you. Your fraud team joins across datasets, and Legal occasionally has to re-identify a subject lawfully. Our tokens are deterministic, join-preserving and vault-reversible.
- You need an agent's authority pulled mid-session. The very next governed tool call should fail closed, rather than waiting for a policy sync. How Auth does it.
- You'd rather see a price than start a quarter-long procurement cycle.
Pick Noma when
- You don't yet know what AI is running. Their continuous discovery and shadow-AI inventory is real work we simply don't do. You can't govern agents you can't see.
- You want AI-SPM, supply-chain posture and red teaming in the same console as runtime protection. All three are out of our scope.
- Your estate is Copilot Studio, Agentforce, ServiceNow, Bedrock, Databricks. They cover those platforms; we cover MCP-native agents on regulated data.
- You need inline masking of PII before a model sees it. They sit in traffic and do that. We deliberately don't sit inline, and we won't claim we do.
Bottom line: Noma secures the AI estate. DataShield produces the evidence and the reversibility that a regulator, not a CISO dashboard, will ask for. If your exposure is a wide fleet of unknown AI, start with them. If it's a small number of agents touching data you're legally accountable for, start here.
Feature by feature
Competitor cells describe Noma's own claims. We pull them from their site. Also launch posts and newsroom. All as of the date above. If we got something wrong, email support@myorg.ai. We'll fix it, and credit you.
| What matters | DataShield | Noma Security | Edge |
|---|---|---|---|
| Audit evidence | SHA-256 hash chain. Checkpoints are Ed25519-signed. They chain to each other too. The check names the failure. Tampering, insertion, deletion, or truncation. Try the verifier. | A "complete audit trail," logged and searchable. We found no public crypto tamper-check. | ◆ |
| Agent authorization | Each checked tool call passes a scope ceiling. It also passes an access tier. And a revocation re-check, before dispatch. Revocation lands on the next call. | Agentic Access Control: each agent gets an ID. One per MCP server. States are tool-level: Approved, Requires-Review, Blocked. Those are policy assignments. Per-call revocation isn't described. | ◆ |
| Break-glass | Scoped, time-boxed emergency access for agents. It auto-revokes. It can't be quietly deleted from the log. | Nothing published. | ◆ |
| Data handling | We apply tokens at ingest. They're deterministic, join-preserving, and vault-reversible. We also generalize quasi-identifiers. Dates become year, decade, or age band. ZIPs become 3 or 4 digits. Partial phones, SSNs, and emails too. Each column gets a cardinality-reduction score. Detokenization is a special, logged step. | Real-time masking of PII, credentials, and API keys. It happens inline, before the model sees them. Masking is one-way. Joins don't survive it. Lawful re-identification doesn't either. | ◈ |
| Inline runtime protection | Agents query already-tokenized data over MCP. Raw values never enter the queryable surface. We don't proxy your LLM traffic. We do gate each value. Each one leaving a checked dataset for a prompt. A PII or PHI column with no treatment set. It gets redacted. A PHI dataset blocks any AI endpoint. Unless it's marked BAA or ZDR approved. That's a gate on our own data path. Not an inline product. | Yes. "Detect. Mask. Block." in the live path. That includes blocking poisoned MCP servers. And malicious tools. | ◇ |
| GDPR erasure | Crypto-shred wipes per-subject key material. Plus ISO 27560 consent receipts. We HMAC-commit actor identities. So the chain still checks out after erasure. | Compliance tracking against GDPR and other rules. We found no erasure story. | ◆ |
| Discovery and posture | An MCP services registry for our own control plane. No cross-estate discovery, no AI-SPM, no supply-chain scanning. | Continuous AI discovery and shadow-AI inventory. Posture checks too. Plus supply-chain checks for poisoned models and data. | ◇ |
| Red teaming | Not offered. | Automated AI red teaming as part of the platform. | ◇ |
| MCP and agents | Native MCP endpoints on Auth, Ontology and Lighthouse. MCP tool tokens carry scope ceilings. Per-call metering ties to the agent. | A dynamic agent-to-MCP-server registry. Tool-level policy states. No per-call metering described. No chargeback attribution either. | ◈ |
| Deployment | Self-hosted in your own cloud or data center. Or on a dedicated single-tenant server we run. Your keys. | SaaS, on-prem, or self-hosted. They claim no model, data, or event leaves the setup. Ever. AWS Marketplace listed. | ◈ |
| Ecosystem reach | Model-vendor neutral, by design. Our MCP tool tokens lean on no single vendor. Same for our audit chain. | Links to Anthropic's Claude Compliance API. It's a native guardrail in TrueFoundry too. Same in Amazon Kiro. Plus a Kong gateway partnership. | ◇ |
| Maturity signals | Auth, Guardian and Lighthouse are live in production. Guardian and Lighthouse since April 2026. SOC 2: not yet certified. We say so. | Founded 2023. Raised $132M total. Self-reported clients include UiPath, Best Buy, and Nielsen. The vendor reports its own growth figures. | ◇ |
| Pricing | Public model, scoped instant quote, no sales wall. | Quote-only. Priced per agent, MCP server, and integration. No figures public. | ◆ |
◆ DataShield leads◇ Noma Security leads◈ comparable
We draw Noma claims from noma.security. Also from their launch posts and 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 an AI security platform
Proof, not search
"Complete audit trail" and verifiable trail are different claims. Only one survives a hostile question. A searchable log is still a mutable log. That's true in anyone's store. Ours is a hash chain with signed checkpoints. The check names what went wrong. Tampering, insertion, deletion, or truncation. EU AI Act Article 12 wants that exact trait. So does HIPAA §164.312(b). Try breaking ours. No account needed.
Protection you can undo on purpose
Masking is destructive. A DSAR lands. Or a fraud analyst needs to join two datasets. Or Legal has a lawful reason to re-identify. At that point, a masked value is just gone. We tokenize at ingest instead. Tokens are deterministic and join-preserving. We treat detokenization as a special, checked vault step. How the data plane works.
The Friday-afternoon revocation
An analyst leaves at 4pm. Their agent is 20 minutes into a 40-minute job. A policy state that says Approved just stays Approved. Until something syncs it. We re-check access on each checked tool call. So the next call fails closed. Break-glass works the same way, in reverse. It's scoped and time-boxed. And impossible to quietly delete from the log. How Auth does it.
Where Noma is genuinely stronger
Almost everywhere, outside evidence and reversibility. Noma has roughly a hundred times our resources. Their platform spans discovery, posture, and supply chain. Plus red teaming and runtime. Named customers back it up. They launched Agentic Access Control in June 2026. Our old line is no longer new. We used to say this: we live in the MCP call path. That line is tired now. They deploy on-prem too, as well as SaaS. So our self-hosting case misses them. That case targets pure SaaS vendors. They're inline. We are not. They also cover Copilot Studio, Agentforce, and ServiceNow. We don't touch those. Say a board asks your CISO for the safe pick. For the AI estate, that's them. I'd say the same, in their seat.
Where I'd argue back: breadth stops where evidence begins. Their access control assigns a state. To an agent, and to a tool. We re-check access on the call itself. That's the gap between a policy and an enforcement point. Their protection is one-way. Fine, until a regulator wants the subject's data. Or erase it on demand. And nothing public claims a log you can crypto-check. Ask us both for that. It's a short conversation.
Questions worth asking both of us
Ask these on each call in this category. Ours included, and ours especially.
Can you prove, with math, that a log entry wasn't deleted?
DataShield: yes. You don't have to take our word for it. Each row commits to the row before it. Checkpoints are Ed25519-signed. They chain to one another too. The check reports the failure by name. Not a plain red cross. There's a chain at /verify you can attack. Noma: their runtime page describes a full, searchable trail. We found no public tamper-check. Ask them to show one. And ask who at their company could edit the store.
What happens to a revoked agent mid-session?
We re-read access at dispatch, on each checked tool call. The call right after your revocation fails. Not the call after the token expires. Noma's model assigns tool-level states to agents: Approved, Requires-Review, Blocked. How fast a state change hits a live session? Not public. The Asana integration incident ran 34 days too long. Past the point it should have been cut off. Ask us both for the number.
How does GDPR erasure interact with the audit trail?
Crypto-shred: the subject's key material goes. Their data becomes unreadable. An ISO 27560 receipt records it. Actor identities in the chain are HMAC commitments. So nothing in the log needs deleting. The erasure is still real. Noma tracks GDPR compliance as a posture item. We found no erasure or crypto-shred story. Ask if erasing a subject breaks their log. And ask if masked data can serve a DSAR.
Noma masks PII inline. Doesn't that beat tokenizing at ingest?
For prompts a human types into a chat window: yes. Inline masking catches things we never see. We don't sit in the traffic path. And we won't claim we do. The trade is reversibility. Their masking is one-way, by design. Joins break. Lawful re-identification becomes impossible. We tokenize datasets at ingest instead. Agents query tokenized data over MCP. Detokenization is a special, checked step. Different layer, different failure mode. Some buyers should run both.
They integrate with Anthropic's Claude Compliance API. What's your equivalent?
We don't have one. We're not planning one either. Our MCP tool tokens stay model-vendor neutral. So do our scope ceilings, and our audit chain. By design. The same evidence plane covers Claude. Also an in-house model. Also whatever you migrate to in 2028. Noma's integration is a real edge. That's true if your setup is Claude-centric today. It gives you a control plane. One that speaks that provider's compliance surface, natively. Weigh that against lock-in, on a three-year horizon.
Does DataShield have SOC 2?
No. Auth is live, with a public threat model. And a verifier anyone can run. Guardian and Lighthouse have been in production since April 2026. We also have no named clients to show you. Against a $132M competitor, that's a real gap. Not a quirk. Source escrow sits in the design-partner terms. So one small vendor isn't a single point of failure. Security page.
- Noma's hero: "Secure AI. Control Your Agents." Primary CTA is a demo request. - noma.security, 13 Sep 2026
- Evolution Equity Partners led a $100M Series B. Total raised: $132M. - SecurityWeek, Jul 2025
- Agentic Access Control gives each agent its own ID. One per MCP server. States are tool-level: Approved, Requires-Review, Blocked. - Noma launch post, Jun 2026
- Runtime protection is "Detect. Mask. Block." It includes a complete, searchable audit trail. - noma.security/platform/runtime-protection, 13 Sep 2026
- Visibility and access governance, for AI agents and MCP servers. Reported on its own, not just by Noma. - Help Net Security, 2 Jun 2026
- EDPB names pseudonymization as a GDPR risk fix. For AI models. One-way masking can't satisfy that need. Not for re-identification. - EDPB Opinion 28/2024
Other head-to-heads
DataShield vs Skyflow
A privacy vault with an inline gateway. Versus agent access and proof.
DirectDataShield vs Strac
Inline MCP interception versus ID, proof, and break-glass.
ComplementDataShield vs Zenity
Agent discovery above, enforcement and evidence below. Most buyers run both.
AllEach comparison
Eleven vendors, one honest scorecard each.
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