Head-to-head · updated 13 September 2026
DataShield vs Qualytics: the data is trusted, but is the agent allowed?
Qualytics is a good data quality engine with a sharp new story on top. Point it at Snowflake, Databricks, Oracle, SAP HANA or an S3 bucket and it profiles the tables, infers the checks so nobody has to hand-write them, scans on a schedule, and raises anomalies with a workflow behind them. In June 2026 they added field-level lineage. They call the result a data control layer, and the CEO line is a fair one: if validation only happens in the pipeline, you are already too late. They have SOC 2 Type II, a $10M Series A, and a real MCP server that checks RBAC on every tool call. None of that is marketing fluff, and we are not going to pretend it is.
We answer the next question. Qualytics tells you the data is good enough to act on. It does not tell you whether this agent was allowed to read it, or give you an artefact that proves what it did. DataShield does. Datasets are tokenized at ingest; agents query tokenized data over MCP; detokenization is a privileged, audited operation. Every governed tool call is checked against the agent's current authority before dispatch, and every decision is sealed into a hash chain you can verify without trusting us. Below is the honest split, including the rows Qualytics wins outright.
The short version
Pick DataShield when
- Someone will eventually ask you to prove an agent's access log was not edited. An examiner, an auditor, or Article 12 of the EU AI Act. Our chain answers with math instead of a screenshot. Run the verifier.
- You need to pull an agent's authority mid-session and have the very next tool call fail. Not the next login. The next call.
- The data an agent reads has to be tokenized before the model sees it, with detokenization as a privileged, logged step rather than a checkbox.
- A GDPR erasure has to land without breaking the audit trail an auditor asked you to keep.
Pick Qualytics when
- Your real problem is bad data reaching a decision. Inferred checks, scheduled scans, anomaly workflow, freshness, volumetrics. We do none of that and we are not going to start.
- You need coverage across a wide estate: Snowflake, Databricks, BigQuery, Redshift, Synapse, Teradata, SAP HANA, Oracle, Trino, plus S3, ADLS and GCS. Our in-place catalog scan is PostgreSQL today.
- You want the quality signal pushed into the tools people already use: Atlan, Alation, Collibra, Purview, DataHub, plus Slack, PagerDuty, Jira and ServiceNow.
- Procurement wants a SOC 2 Type II report in the data room this quarter. They have one, dated April 2024. We do not.
Bottom line: Qualytics scores the data. DataShield governs the agent that reads it and keeps the proof. Most teams who need both should run both, and the buying order comes down to which alarm is louder this quarter: a bad number reaching a decision, or an unproven agent reaching regulated data.
Feature by feature
Competitor cells describe what Qualytics' public site, press releases and user guide say as of the date above. If we have mischaracterised something, email support@myorg.ai and we will correct it, credited.
| What matters | DataShield | Qualytics | Edge |
|---|---|---|---|
| Data quality monitoring | Not our job, and saying otherwise would be a lie. Every dataset gets a 20-section analysis profile: completeness, field statistics, patterns, column semantics, relationship graph, quality metrics, lineage and compliance governance. On top of that sits a weighted composite trust score per entity type, domain and estate, recomputed hourly, plus change and drift detection with a diff engine and a schema-evolution log. What we do not have: anomaly detection, monitors on warehouse tables, freshness SLAs, or incident management. | The core product, and the best part of it. 50+ rule types, ML-inferred checks, scheduled scans, anomaly detection with fingerprinting and status workflow, freshness, volumetrics, and a quality score with AI explanations. | ◇ |
| Audit evidence | SHA-256 hash chain with Ed25519-signed checkpoints that are themselves chained. The verdict is three-valued: clean, attested damage, or tampered. Verification names the failure mode, and re-tampering an attested chain un-attests it automatically. Try the verifier. | A filterable activity log with a changes view showing old and new values, plus a separate audit of every unmasking event including user, IP and fields touched. That second one is a nice piece of design. We found no hashing, signing, or immutability property documented for either. | ◆ |
| Agent authorization | Every governed tool call runs an auth gate, a consented-tool allowlist narrowing step, a scope ceiling, an authority tier, and a session revalidation, then metering, then the handler. It fails closed. Delegation is RFC 8693 token exchange with the scope ceiling enforced on the exchanged token. | Their MCP server authenticates by bearer token and states it enforces RBAC on every tool call, with each call logged as a discrete action. That is a real per-call check and better than most of this market. We found no scope ceiling, no authority tier, and no delegation model in the docs. | ◆ |
| Break-glass | Scoped, time-boxed emergency access for agents, admin plus IP-allowlist plus step-up gated. It auto-revokes and cannot be quietly removed from the log. | Not described in their public material. | ◆ |
| GDPR erasure | Crypto-shred of per-subject key material, cited to ISO/IEC 27040 and GDPR Article 17, plus ISO 27560 consent receipts signed at grant and at withdrawal. Actor identities in the chain are HMAC-committed, so the audit still verifies after the subject is gone. | Not described. There is field masking, which is reversible by an admin, and a reveal audit log. Masking is not erasure. Ask them what happens to a subject's values inside profiles, anomaly records and enrichment stores. | ◆ |
| Tokenization and sensitive data | 129 deterministic field classes covering PII, PHI, financial data and secrets, including all 18 HIPAA Safe Harbor identifiers, with reproducible verdicts stamped by config digest and rule version. Tokens are deterministic, join-preserving and vault-reversible. Quasi-identifier generalization turns dates into year, decade or age band and ZIPs into 3 or 4 digits, with a measured cardinality-reduction score per column. | Field masking replaces values with a placeholder and an admin can unmask. Format checks exist for credit card, email and SSN, but those are quality rules, not a classification library. No tokenization vocabulary anywhere in their docs. | ◆ |
| MCP and agents | More than 200 MCP tools across Ontology, Auth, Corpus and Lighthouse. Tool tokens carry scope ceilings, per-call usage is metered and attributed to the agent, and the decision is sealed into the chain. | AgentQ shipped February 2026 and the MCP server is documented: streamable HTTP with SSE, bearer auth, the full tool catalog registered upfront, read and write tools including triggering operations and creating tickets. Genuinely shipped, genuinely useful. | ◈ |
| What reaches the model | We do not proxy your LLM traffic. We do gate every value that leaves a governed dataset for a prompt, and a PHI dataset refuses an endpoint without a BAA. Redaction is per column and fails closed. | One administrator-level switch for the whole deployment: No Sharing, Metadata Shared, or Source Data Shared. Their docs promise masked values are never sent to the model and credentials never are. Clear and honest, but it is one lever, not a per-column policy. | ◆ |
| Lineage | A typed lineage graph with access gating at every hop, so an edge never reveals an asset you cannot read. It is derived from the pipelines that own the relationships, not a stored column-level graph. | Data Lineage shipped June 2026 with field-level lineage, a graph view, and anomalies visible on the graph. Deeper than ours on the dimension most people mean by the word. | ◇ |
| Deployment | Self-hosted in your own cloud or data center, or a dedicated single-tenant server we operate. Docker images ship for Auth, Ontology, Corpus and Lighthouse, and a signed deploy manifest is verified before rollout. Ed25519 audit-signing keys can live in your own KMS or HSM. HMAC tokenization keys sit in your environment today, not in a KMS, and we would rather say that than have you find out. | Managed single-tenant on AWS, Azure, Google or Oracle, self-hosted on any CNCF-conformant Kubernetes cluster including on-prem, air-gapped listed as supported, plus a Databricks-native mode. Their docs note raw customer data is not stored at rest, though derivative data and selected values may be. | ◇ |
| 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. | SOC 2 Type II since April 2024, $10M Series A led by BMW i Ventures in June 2025, Databricks technology partnership, and a regulated customer list including AES, MAPFRE, Brookfield and Wellstar Health System. | ◇ |
| Pricing | Published model, scoped instant quote, no sales wall. | Quote only. A demo and a free trial, no rate card. | ◆ |
◆ DataShield leads◇ Qualytics leads◈ comparable
Qualytics claims are drawn from qualytics.ai, their company and product news pages, and the product documentation at userguide.qualytics.io, last checked 13 September 2026. We link the sources below rather than work from memory.
Three things you get here that you won't get from an augmented data quality platform
Proof that survives an audit
A log you can silently edit proves nothing. Ours is a hash chain with signed checkpoints, and the verifier tells you what broke: tampering, insertion, deletion or truncation. That is 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 quits on a Friday. Their agent is 20 minutes into a 40-minute job. With DataShield the next governed tool call revalidates the session and fails closed. A quality platform writes you a lovely report about it on Monday. How Auth does it.
Tokens, not just masks
Masking hides a value from a screen. Tokenization changes what the agent holds. Our tokens are deterministic, so joins still work, and vault-reversible, so a human with the right authority can get the value back under an audited call. What Ontology does at ingest.
Where Qualytics is genuinely stronger
Start with the bit that stings. They hold a SOC 2 Type II report from April 2024 covering all five trust services criteria. We do not hold one at all. If your procurement team scores that as a gate rather than a signal, the conversation ends there and we would rather you knew on day one. Their connector list is also in a different weight class: twenty-odd JDBC sources plus S3, ADLS and GCS, against our in-place catalog scan that is production-ready for PostgreSQL and honestly declared-but-unbuilt for everything else. Their field-level lineage, shipped June 2026, is deeper than our typed edge graph. And the monitoring engine is real work: inferred checks, freshness, volumetrics, anomaly fingerprinting, and a remediation path into Jira, ServiceNow and PagerDuty. We have a 20-section profile and a trust score. We do not have monitors, and a page that claimed otherwise would fall apart in the first demo.
Here is the push-back. Their April 2026 launch renames the quality signal a control, and a control is a thing that refuses. Read the docs and the refusal is still advisory: the signal is published, the agent is expected to consult it, and the agent is expected to behave. The strongest real enforcement in their stack is one deployment-wide switch deciding whether the model sees metadata or actual values, and RBAC on their own MCP server. Neither of those survives the question an examiner actually asks, which is not "was the data good" but "show me that this agent was allowed to read this, on this date, and show me the log has not been touched since". Gartner expects most unauthorized agent transactions through 2028 to be internal policy violations rather than attacks, which is exactly the case where a published signal and a good intention are the weakest possible answer. That is the layer we sell, and it sits on top of a quality engine like theirs perfectly happily.
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 Ed25519-signed and themselves chained, and verification separates deletion from truncation and from tampering. Run it against a sample chain at /verify. Qualytics: their audit page is a filterable activity list with a changes view, and their masking audit log records who revealed what, from which IP. We found no hashing, signing, or immutability claim in the documentation. Ask them to show one.
What happens to a revoked agent mid-session?
DataShield revalidates session authority on every governed tool call, so a revocation lands on the next call and the context drops to anonymous. Qualytics issues personal and service access tokens and can revoke them, and their MCP server checks RBAC per call, which is better than most of this market. What we could not find is the window: how long a token stays honoured after you revoke it, and whether an in-flight agent conversation is re-checked. Ask for the number in seconds.
How does GDPR erasure interact with the audit trail?
DataShield crypto-shreds the per-subject key material, which destroys every ciphertext for that subject at once, and issues an ISO 27560 consent receipt. Actor identities in the chain are HMAC-committed, so the evidence still verifies once the subject is gone. Qualytics does not describe an erasure mechanism. They do have field masking, which an admin can reverse, and their own docs note that derivative data and selected values may be stored in the deployment. Ask what happens to a subject's values inside profiles, anomaly records and enrichment datastores.
Is DataShield a data quality platform? Do we drop Qualytics?
No, and probably not. We do not monitor your warehouse, we have no anomaly detection, no freshness SLAs and no incident workflow, and we are not going to build them this year. What we do is profile and score the governed datasets agents read, classify the sensitive fields, tokenize them, decide per call whether an agent may touch them, and keep proof. If the pain is a bad number in a dashboard, buy them. If it is an agent nobody can account for, buy us. Plenty of teams will sensibly run both, with their quality signal feeding our policy.
Qualytics ships an MCP server and AgentQ too. What's different?
Theirs is a well-built console for the quality platform: streamable HTTP, bearer auth, RBAC per tool call, tools to search assets, read quality trends, trigger operations, tag things and open tickets. The agent is talking about your data. Ours is where the governed data itself is queried, so the tool token carries a scope ceiling, the call is authorized before dispatch, usage is metered and attributed to the agent, and the decision is sealed into a chain you can verify later. Different jobs. If you want an agent that can ask how healthy a table is, theirs is the better tool. If you want an agent that reads regulated rows under a policy you can prove afterwards, that is us.
Does DataShield have SOC 2?
Not yet, and we will not imply otherwise. Qualytics does, since April 2024, and that is a fair point against us. What we offer instead is a public threat model, a verifier anyone can run without an account, and a stack you can host yourself so the trust boundary is one you control. Guardian and Lighthouse have been in production since April 2026. Design-partner terms include source escrow, so a small vendor is not a single point of failure. Details on the security page.
- Qualytics' current hero: "Control the context behind every decision." The sub-head calls it "data quality as a shared control layer where AI maintains coverage and your teams govern what trusted means." — qualytics.ai, 13 Sep 2026
- "Qualytics Launches Data Control Layer to Govern Context for AI Systems." CEO Gorkem Sevinc: "The control point for AI has shifted. If validation only happens in data pipelines, you're already too late." — Qualytics company news, 13 Apr 2026
- $10M Series A led by BMW i Ventures, citing 5x revenue growth and a top-3 US financial institution as a new customer. — Qualytics company news, 23 Jun 2025
- SOC 2 Type II certified across security, availability, confidentiality, processing integrity and privacy, audit window November 2023 to January 2024. — Qualytics company news, 25 Apr 2024
- Deployment: a managed single-tenant environment on AWS, Azure, Google or Oracle; self-hosted on a customer-managed CNCF-conformant Kubernetes cluster; and a Databricks-native mode. "Raw customer data is not stored at rest, but derivative data and selected values may be stored in the deployment." — Qualytics user guide, 13 Sep 2026
- The Qualytics MCP server runs over streamable HTTP with bearer-token auth, registers its full tool catalog upfront, and "enforces authentication and RBAC on every tool call." — Qualytics user guide, 13 Sep 2026
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