Head-to-head · updated 13 September 2026
DataShield vs Bigeye: your monitors saw the data. Who saw the agent?
Bigeye is a good data observability platform. We won't pretend otherwise. Their machine learning watches freshness, volume, schema and distribution across large warehouse estates. It's been tuned on real production data since 2019. The lineage they bought in 2023 reaches back into legacy systems that most modern tools quietly skip. They now call themselves "The Enterprise AI Trust Platform." They hold SOC 2 Type II and ISO 27001. We don't.
We do a different job. DataShield governs the datasets agents read. It keeps proof of what each agent was allowed to do. We tokenize datasets at ingest. Agents query tokenized data over MCP. Detokenization is a gated, audited run. We re-check each checked tool call against current rights before it runs, and seal each decision into a hash chain you can verify yourself. We ship no anomaly detection, no freshness alerts, and no on-call rotation. Here's the honest split, rows they win included.
The short version
Pick DataShield when
- Someone is going to ask you to prove an agent's access log wasn't edited. An auditor, a regulator, 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. The very next tool call should fail. Not the next token refresh. The next call.
- The data, the policy engine and the proof all have to sit on your own hardware, on keys you hold. Bigeye is a managed cloud service, with an on-prem collector.
- You want a price before you book a call.
Pick Bigeye when
- Your real pain is broken pipelines. Tables that go stale, row counts that halve overnight, a schema change nobody announced. Their anomaly detection is the product. It's good at it.
- You need lineage across a messy estate, from the old transactional database, through ETL, to the BI tool. They bought Data Advantage Group in 2023 for exactly this.
- Procurement wants a certified vendor now. SOC 2 Type II, ISO 27001, pen-test reports on request, marketplace private offers. We can't match that paperwork today.
- You want agent token spend tracked per agent, per user, per workflow, with a daily cost baseline. We don't do FinOps at all.
Bottom line: Bigeye tells you the data is fit to use. DataShield decides whether this agent may touch it. It keeps proof of the decision, too. Those are two different obligations. Most teams end up owing both.
Feature by feature
Competitor cells describe what Bigeye's own site, docs and press releases say, as of the date above. Did we get something wrong? Email support@myorg.ai. We'll correct it, and credit you.
| What matters | DataShield | Bigeye | Edge |
|---|---|---|---|
| Anomaly detection and monitoring | None. We have no statistical anomaly detector. No freshness or volume monitors. No alert routing, no incident management. We say this plainly. Buying us for that would be a mistake. | The core of the product since 2019. Machine learning on freshness, volume, schema and distribution. Monitors, alerting and issue triage, across large warehouse estates. | ◇ |
| Data quality profiling and scoring | Each dataset gets a 20-section analysis profile. Completeness, field statistics, patterns, column semantics, relationship graph. Also quality metrics, transformation lineage, work rules and source fingerprint. A weighted trust score rolls up per entity type, domain and estate. It's recomputed hourly, with a per-axis breakdown and a trend. | Automated table and column profiling, feeding their monitors. Plus quality signals, surfaced to agents. Through Agent Trust Hub. | ◈ |
| Sensitive data classification | 129 built-in field classes cover PII, PHI, money data and secrets. All 18 HIPAA Safe Harbor IDs are in there too. Regex plus checksum validation runs the checks: Luhn, NPI, Verhoeff, ABA, IBAN, GTIN. Plus column-name lexicons, and anti-pattern suppressors. No model, so verdicts stay deterministic, and reproducible from a config digest. We scan PostgreSQL sources in place, with no rows leaving them. That's PostgreSQL today, not your whole estate. | A Data Sensitivity module that finds PII, PHI, PCI and PFI. Classifier bundles for HIPAA, PCI and GDPR. Described as AI detection, not pattern matching, with lineage context attached. Far broader source coverage than ours. | ◈ |
| Audit evidence | SHA-256 hash chain over each record, with Ed25519-signed checkpoints that are themselves chained. Verification returns a three-way verdict. It names the failure: tamper, insertion, delete or cut-short. Public demo. | AI Guardian's monitoring mode produces what they call a complete audit trail. We found no open tamper-evidence method. There's no way for you to verify a log without trusting their database. | ◆ |
| Agent authorization | Each checked call passes three gates first. A scope ceiling. A rights tier. A live pull-back re-check. Then it gets metered. It gets tied to the agent. The call fails closed. | AI Guardian checks requests against work rules. It can steer or block. The rule is about whether the data is fit for the use case. Not whether the caller is entitled to it. | ◆ |
| Mid-session revocation | We revalidate rights on each checked tool call. A revoked agent gets downgraded on its next call, not its next login. | Not shown in their public material. Agent Trust Hub is a registry, and a signal feed. Ask them how long a pulled agent keeps working. | ◆ |
| Break-glass | Scoped, time-boxed emergency access for agents. Gated by admin role, IP allowlist and step-up. Fully audited. It expires on its own. | Not shown in their public material. | ◆ |
| GDPR erasure | Crypto-shred of per-subject keys, plus ISO 27560 consent receipts. Actor identities in the chain are HMAC-committed. The proof still verifies after the subject is gone. | They mention workflows for privacy deletes and masking. The underlying erasure method isn't shown. Neither is what happens to the audit history. | ◆ |
| Tokenization and data handling | Deterministic, join-preserving, vault-reversible tokens, applied at ingest. Plus quasi-ID generalization: dates to year, decade or age band. ZIPs to 3 or 4 digits. Partial phones, SSNs and emails. Each column gets a measured cardinality-reduction score. Detokenization is admin-tier, tenant-scoped, and refused if the audit write fails. | They store aggregated statistics. They say they never keep raw source data, using read-only service accounts. Sensible for a monitoring tool. Tokenization isn't vocabulary they use. | ◆ |
| Lineage | Typed lineage traversal, with access gating at each hop. Derived from the pipelines that own the relationships, not stored as a separate graph. Useful inside our own platform. It doesn't map your ETL estate. | Cross-source lineage, bought with Data Advantage Group in 2023 and extended in 2024. Spans transactional databases, ETL, lakes, warehouses and BI tools. Years of connector work we can't match. | ◇ |
| MCP and agents | More than 200 MCP tools across Ontology, Auth, Corpus and Lighthouse. Agents ingest, classify, transform, snapshot and query checked data. Auth issues MCP tool tokens with scope ceilings, and meters each call. | A live MCP server lets an assistant check quality issues. It can trace lineage. It can search the catalog. It can also profile tables and manage monitors. It logs in with an API key and a workspace id. Good coverage of their own metadata. | ◆ |
| Agent cost and usage | Per-call metering, with agent attribution and quota enforcement. That's a control, not a spend dashboard. No FinOps reporting. | Token tracking per agent, user and workflow. Plus Cost Anomaly Detection, rolled out in August 2026. It flags spend spikes and silent failures against a daily baseline. | ◇ |
| Deployment | Self-hosted, in your own cloud or data center. Or a single-tenant server we run for you. Docker images for Auth, Ontology, Corpus and Lighthouse. Ed25519 signing keys stay on your instance, in a key folder you control. Key rotation fails closed on an unknown key. KMS or HSM custody is a built seam in Auth. It is not a shipped link yet. HMAC tokenization keys sit in your setup today, or come from your machine key. Not in a KMS. | A managed cloud service, agentless across 20 or more sources. An on-prem agent needs no inbound connections, plus AWS PrivateLink. The control plane is theirs. | ◈ |
| Maturity signals | Auth, Guardian and Lighthouse are live in production (Guardian and Lighthouse since April 2026). SOC 2 not yet certified. We say so on each page. | Founded 2019. Roughly $70M raised. Alteryx Ventures backed them. Also a Gartner Market Guide listing, SOC 2 Type II and ISO 27001. A logo wall too, with Centene, USAA, Cisco and Zoom on it. | ◇ |
| Pricing | Open model and a scoped instant quote with no sales wall. | Quote only. Their pricing page is gone. Packages sell as enterprise private offers. Agent Trust Hub has a 30-day free trial, no card, no call. That's the one self-serve door they have. | ◆ |
◆ DataShield leads◇ Bigeye leads◈ comparable
We draw Bigeye claims from bigeye.com, docs.bigeye.com, and Bigeye's own press releases. Last checked 13 September 2026. We link them below rather than work from memory.
Three things you get here that you won't get from a data observability platform
Proof that survives an audit
A log an admin can quietly edit proves nothing. Ours is a hash chain with signed checkpoints. The checker tells you what broke, not just that something did. That's the property EU AI Act Article 12 and HIPAA §164.312(b) reviewers ask about. Try it in your browser, no signup.
Authority that can change mid-flight
An analyst leaves on a Friday. Their agent is 20 minutes into a 40-minute job. With DataShield, we check the next checked tool call against current rights. It fails closed. A registry and a dashboard would tell you about it on Monday. How Auth does it.
Tokens the agent can still join on
Redaction breaks your joins. Our tokens repeat the same way each time. The same client id matches across datasets. The raw value stays in the vault. Turning a token back is a gated call. It gets refused if the audit write fails. How the data plane works.
Where Bigeye is genuinely stronger
We'd rather you heard this from us. Bigeye has spent seven years on a hard problem. Knowing a table is wrong before the rest of the firm does. That takes tuned models, years of false-positive pain, and connector work nobody enjoys. Their clients report detection times dropping from three days to under a day. Their lineage reaches into legacy systems. They bought a company that had done the boring part. That work was already done. They hold SOC 2 Type II and ISO 27001. They publish a real security page. They'll hand your procurement team a pen-test report. We ship none of that. A page that said different would be lying to you.
Here's the push-back, and it's narrow. Their AI Guardian gates on whether the data is fit for the use case. Good idea. It isn't the same as gating on whether this agent, right now, is entitled to the data. Gartner expects most not allowed agent transactions through 2028 to be internal rule violations, not attacks. A fitness check waves those straight through, because the data was fine. The agent was the problem. Agent Trust Hub is honest about this. Its own page calls itself a foundation for enforcement. Foundations help. They're just not the floor you stand on when an examiner asks for March.
Questions worth asking both of us
These are the questions we would want answered if we were the ones buying. Ask them on each call, ours included.
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. Verification tells delete apart from cut-short, and from tamper. Run it on a sample chain at /verify. Bigeye: AI Guardian's monitoring mode gives you an audit trail. We found no open tamper-evidence method. Ask them what stops a workspace admin from removing a row.
What happens to a revoked agent mid-session?
DataShield re-checks rights on each checked tool call. Pull-back lands on the next call. Bigeye registers agents in Agent Trust Hub and maps what data they touch. We couldn't find a mid-session pull-back path in their public docs. 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 the per-subject key. That makes each record for that subject unreadable at once. It issues an ISO 27560 consent receipt, too. The chain still verifies afterwards. Bigeye mentions privacy delete workflows. What happens underneath, and to the audit history, isn't spelled out. Ask for the method, not the workflow.
We already run Bigeye. Is this a rip and replace?
No, and we'd argue against it. Keep your monitors. If your tables break at 3am, that's their job, not ours. We sit under the datasets your agents read: classification, tokenization, per-call rights and proof. Most teams who buy us already own something that watches pipelines. The two layers don't fight.
Bigeye's AI Guardian can block a query. Isn't that the same thing?
Close, but aimed elsewhere. Their steering mode blocks access to data that fails a fitness rule. That's a data-quality judgement. Ours blocks a call for a different reason. The caller's scope, tier or live rights says no. That's an access judgement. Both are useful. Only one answers "who was allowed to see this?" And yes, they named their tier AI Guardian. Our fleet-health service is called Guardian too. Sorry about that. Nobody rang.
Does DataShield have SOC 2?
Not yet. We won't imply otherwise. Bigeye does, and on that row they beat us cleanly. What we offer instead: an open threat model, a checker anyone can run. Also source escrow in design-partner terms. That way a small vendor isn't a single point of failure. Auth is live. Guardian and Lighthouse have been in production since April 2026. Details on the security page.
- Bigeye's current positioning: "The Enterprise AI Trust Platform," with the line "AI is only as good as the data it runs on." Quoted in full. - bigeye.com, 13 Sep 2026
- AI Guardian is pitched as a control plane. It checks AI requests against work rules. It runs in monitoring, advising or steering mode. It blocks access to data that is not fit for use. - EIN Presswire, 10 Dec 2025
- Agent Trust Hub registers agents from Snowflake Intelligence, Claude Code, Databricks Genie, Microsoft Copilot and Salesforce Agentforce. It calls itself the foundation for enforcement. It has a 30-day free trial. - EIN Presswire, 2 Jun 2026
- Cost Anomaly Detection began rolling out on 18 August 2026. It flags agent spend spikes and drops to zero against a daily baseline. - EIN Presswire, 20 Aug 2026
- Bigeye states it is "a fully managed SOC 2 and ISO 27001 compliant application." It runs agentless across 20 or more sources. An on-prem agent and AWS PrivateLink are optional. - bigeye.com/security, 13 Sep 2026
- The Bigeye MCP server lets an AI assistant do a lot. Check data quality issues. Trace lineage. Search the catalog. Profile tables and manage monitors. It runs on an API key header and workspace id. - docs.bigeye.com, 13 Sep 2026
- Bigeye acquired Data Advantage Group in June 2023 to build automated cross-source data mapping and lineage. - Datanami, 27 Jun 2023
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