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 and we are not going to pretend otherwise. Their machine learning watches freshness, volume, schema and distribution across large warehouse estates, and it has 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 do not.
We do a different job. DataShield governs the datasets agents read and keeps proof of what each agent was allowed to do. Datasets are tokenized at ingest; agents query tokenized data over MCP; detokenization is a privileged, audited operation. Every governed tool call is re-checked against current authority before it runs, and every decision is sealed into a hash chain you can verify yourself. We ship no anomaly detection, no freshness alerts and no on-call rotation. Here is 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 was not 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 and have the very next tool call fail. Not the next token refresh. The next call.
- The data, the policy engine and the evidence 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, and it is 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 cannot match that paperwork today.
- You want agent token spend tracked per agent, per user, per workflow, with a daily cost baseline. We do not do FinOps at all.
Bottom line: Bigeye tells you the data is fit to use. DataShield decides whether this agent may touch it, and keeps proof of the decision. Those are two different obligations, and most teams will 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. If we have mischaracterised something, email support@myorg.ai and we will correct it, credited.
| What matters | DataShield | Bigeye | Edge |
|---|---|---|---|
| Anomaly detection and monitoring | None. We have no statistical anomaly detector, no freshness or volume monitors, no alert routing and no incident management. We say this plainly because buying us for that would be a mistake. | The core of the product since 2019. Machine learning on freshness, volume, schema and distribution, with monitors, alerting and issue triage across large warehouse estates. | ◇ |
| Data quality profiling and scoring | Every dataset gets a 20-section analysis profile: completeness, field statistics, patterns, column semantics, relationship graph, quality metrics, transformation lineage, business rules and source fingerprint. A weighted trust score rolls up per entity type, domain and estate, 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 covering PII, PHI, financial data and secrets, including all 18 HIPAA Safe Harbor identifiers. Regex plus checksum validation (Luhn, NPI, Verhoeff, ABA, IBAN, GTIN), column-name lexicons and anti-pattern suppressors. No model, so verdicts are deterministic and reproducible from a config digest. We scan PostgreSQL sources in place with no rows leaving them. That is PostgreSQL today, not your whole estate. | A Data Sensitivity module that finds PII, PHI, PCI and PFI with classifier bundles for HIPAA, PCI and GDPR, described as AI detection rather than pattern matching, with lineage context attached. Far broader source coverage than ours. | ◈ |
| Audit evidence | SHA-256 hash chain over every record, with Ed25519-signed checkpoints that are themselves chained. Verification returns a three-way verdict and names the failure: tampering, insertion, deletion or truncation. Public demo. | AI Guardian's monitoring mode produces what they call a complete audit trail. We found no published tamper-evidence mechanism, and no way for you to verify a log without trusting their database. | ◆ |
| Agent authorization | Every governed tool call passes a scope ceiling, an authority tier and a live revocation re-check before dispatch, then gets metered and attributed to the agent. The call fails closed. | AI Guardian evaluates requests against business policies and can steer or block. The policy is about whether the data is fit for the use case, not whether the caller is entitled to it. | ◆ |
| Mid-session revocation | Authority is revalidated on every governed tool call, so a revoked agent is downgraded on its next call, not its next login. | Not described 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, and fully audited. It expires on its own. | Not described in their public material. | ◆ |
| GDPR erasure | Crypto-shred of per-subject key material plus ISO 27560 consent receipts. Actor identities in the chain are HMAC-committed, so the evidence still verifies after the subject is gone. | They mention workflows for privacy deletes and masking. The underlying erasure mechanism is not described, and neither is what happens to the audit history. | ◆ |
| Tokenization and data handling | Deterministic, join-preserving, vault-reversible tokens applied at ingest, plus 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. Detokenization is admin-tier, tenant-scoped and refused if the audit write fails. | They store aggregated statistics and say they never keep raw source data, using read-only service accounts. Sensible for a monitoring tool. Tokenization is not vocabulary they use. | ◆ |
| Lineage | Typed lineage traversal with access gating at every hop, derived from the pipelines that own the relationships rather than stored as a separate graph. Useful inside our own platform. It does not map your ETL estate. | Cross-source lineage bought with Data Advantage Group in 2023 and extended in 2024, spanning transactional databases, ETL, lakes, warehouses and BI tools. Years of connector work we cannot match. | ◇ |
| MCP and agents | More than 200 MCP tools across Ontology, Auth, Corpus and Lighthouse. Agents ingest, classify, transform, snapshot and query governed data. Auth issues MCP tool tokens with scope ceilings and meters each call. | A shipping MCP server that lets an assistant check quality issues, trace lineage, search the catalog, profile tables and manage monitors. It authenticates with an API key header and a workspace id. Good coverage of their own metadata. | ◆ |
| Agent cost and usage | Per-call metering with agent attribution and quota enforcement, which is 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 that flags spend spikes and silent failures against a daily baseline. | ◇ |
| Deployment | Self-hosted in your own cloud or data center, or a dedicated single-tenant server we operate. Docker images for Auth, Ontology, Corpus and Lighthouse. Ed25519 audit-signing keys can live in your own KMS or HSM. HMAC tokenization keys sit in your environment or derive from your machine key today, not in a KMS. | A managed cloud service, agentless across 20 or more sources, with an on-prem agent that 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, and we say so on every page. | Founded 2019, roughly $70M raised, Alteryx Ventures investment, Gartner Market Guide listing, SOC 2 Type II and ISO 27001, and a logo wall with Centene, USAA, Cisco and Zoom on it. | ◇ |
| Pricing | Published model and a scoped instant quote with no sales wall. | Quote only. Their pricing page is gone, and packages are sold as enterprise private offers. Agent Trust Hub has a 30-day free trial with no card and no call, which is the one self-serve door they have. | ◆ |
◆ DataShield leads◇ Bigeye leads◈ comparable
Bigeye claims are drawn 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, and the verifier tells you what broke, not just that something did. That is 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 the next governed tool call is checked against current authority and 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 are deterministic, so the same customer id matches across datasets while the raw value stays in the vault. Detokenization is a privileged call, and it is refused if the audit write fails. How the data plane works.
Where Bigeye is genuinely stronger
We would rather you heard this from us. Bigeye has spent seven years on a hard problem: knowing that a table is wrong before the business does. That takes tuned models, years of false-positive pain, and connector work nobody enjoys. Their customers report detection times dropping from three days to under a day. Their lineage reaches into legacy systems because they bought a company that had already done the boring part. They hold SOC 2 Type II and ISO 27001, they publish a real security page, and they will hand your procurement team a pen-test report. We ship none of that, and a page that claimed otherwise would be lying to you.
Here is the push-back, and it is narrow. Their AI Guardian gates on whether the data is fit for the use case. Good idea. It is not the same as gating on whether this agent, right now, is entitled to the data at all. Gartner expects most unauthorized agent transactions through 2028 to be internal policy violations rather than attacks, and 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 are useful. They are 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 every 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, and verification tells deletion apart from truncation and from tampering. Run it on a sample chain at /verify. Bigeye: AI Guardian's monitoring mode gives you an audit trail. We found no published tamper-evidence mechanism. Ask them what stops a workspace admin from removing a row.
What happens to a revoked agent mid-session?
DataShield re-checks authority on every governed tool call, so revocation lands on the next call. Bigeye registers agents in Agent Trust Hub and maps what data they touch. We could not find a mid-session revocation 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, which makes every record for that subject unreadable at once, and issues an ISO 27560 consent receipt. The chain still verifies afterwards. Bigeye mentions privacy delete workflows. What happens underneath, and to the audit history, is not spelled out. Ask for the mechanism, not the workflow.
We already run Bigeye. Is this a rip and replace?
No, and we would argue against it. Keep your monitors. If your tables break at 3am, that is their job and not ours. We sit under the datasets your agents read: classification, tokenization, per-call authority and evidence. Most teams who buy us already own something that watches pipelines. The two layers do not 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 policy, which is a data-quality judgement. Ours blocks a call because the caller's scope, tier or live authority says no, which is an access judgement. Both are useful. Only one answers "who was allowed to see this?" And yes, they named their tier AI Guardian while our fleet-health service is called Guardian. Sorry about that. Nobody rang.
Does DataShield have SOC 2?
Not yet, and we will not imply otherwise. Bigeye does, and on that row they beat us cleanly. What we offer instead is a published threat model, a verifier anyone can run, and source escrow in design-partner terms so a small vendor is not 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." — bigeye.com, 13 Sep 2026
- AI Guardian is pitched as a control plane that evaluates AI requests against business policies, in monitoring, advising or steering mode, blocking 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, and calls itself the foundation for enforcement, with a 30-day free trial. — EIN Presswire, 2 Jun 2026
- Cost Anomaly Detection began rolling out on 18 August 2026, flagging 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," agentless across 20 or more sources with an optional on-prem agent and AWS PrivateLink. — bigeye.com/security, 13 Sep 2026
- The Bigeye MCP server lets an AI assistant check data quality issues, trace lineage, search the catalog, profile tables and manage monitors, authenticated by 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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