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. It has a sharp new story on top. Point it at Snowflake, Databricks, Oracle, SAP HANA, or an S3 bucket. It profiles the tables. It infers the checks. No one has to hand-write them. It scans on a schedule. It raises anomalies with a workflow behind them. In June 2026 they added field-level lineage. They call the result a data control layer. The CEO line is fair. If you only check in the pipeline, you are already too late. They have SOC 2 Type II. A $10M Series A. A real MCP server. It checks RBAC on each tool call. None of that is marketing fluff. 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 if this agent had permission to read it. It does not give you proof of what the agent did. DataShield does both. We tokenize datasets at ingest. Agents query tokenized data over MCP. Detokenization is a privileged, audited step. We check each governed tool call against the agent's current authority before dispatch. We seal each decision into a chain you can check yourself. You do not have to trust us. Below is the honest split, with 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. Maybe an examiner. Maybe an auditor. Maybe 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. Detokenization should be a privileged, logged step, not 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. Think 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 only covers PostgreSQL today.
- You want the quality signal pushed into the tools people now 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. The buying order comes down to one question: 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' site, press releases and user guide say. This is as of the date above. If we got something wrong, email support@myorg.ai. We will fix it, and credit you.
| What matters | DataShield | Qualytics | Edge |
|---|---|---|---|
| Data quality monitoring | Not our job. Saying we do would be a lie. Every dataset gets a 20-section analysis profile. It covers completeness, field stats, patterns, column semantics. Plus a relationship graph, quality metrics, lineage and compliance governance. On top of that sits a weighted composite trust score. It covers entity type, domain and estate. We recompute it hourly. We also run change and drift detection. This uses a diff engine and a schema-evolution log. What we do not have: anomaly detection, monitors on warehouse tables. No freshness SLAs, no 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. Also freshness, volumetrics, and a quality score with AI explanations. | ◇ |
| Audit evidence | SHA-256 hash chain with Ed25519-signed checkpoints. We chain the checkpoints too. The verdict has three values: clean, attested damage, or tampered. Verification names the failure mode. Re-tampering an attested chain un-attests it, automatically. Try the verifier. | An activity log you can filter, with a changes view, showing old and new values. There is also a separate audit of each unmasking event. It shows user, IP and fields touched. That second one is a nice piece of design. We found no hashing, no signing, and no proof either log can't be changed. | ◆ |
| Agent authorization | Each governed tool call runs a chain of checks. Auth gate, consented-tool allowlist step, scope ceiling, authority tier. Then a session recheck, then metering, then the handler. It fails closed. We delegate with RFC 8693 token exchange. The scope ceiling stays enforced on the exchanged token. | Their MCP server authenticates by bearer token. It states it runs RBAC on each tool call. It logs each call as a discrete action. That is a real per-call check, better than most of this market. We found no scope ceiling. No authority tier. No handoff model in the docs. | ◆ |
| Break-glass | Scoped, time-boxed emergency access for agents. It requires admin approval, an IP allowlist, and a step-up check. It auto-revokes, and no one can quietly remove it from the log. | Not described in their public docs. | ◆ |
| GDPR erasure | We crypto-shred the per-subject key material, citing ISO/IEC 27040 and GDPR Article 17. ISO 27560 consent receipts get issued too, signed at grant and at withdrawal. We HMAC-commit actor IDs in the chain. The audit still checks out once the subject is gone. | Not described. There is field masking, and an admin can reverse it. Plus 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 cover PII, PHI, financial data and secrets. This covers all 18 HIPAA Safe Harbor identifiers. Verdicts are reproducible, stamped by config digest and rule version. Tokens are deterministic and join-preserving, and a vault can reverse them. Quasi-identifier generalization turns dates into year, decade or age band. ZIPs turn into 3 or 4 digits. Each column gets a measured cardinality-reduction score. | Field masking replaces values with a placeholder, and an admin can unmask them. Format checks exist for credit card, email and SSN. 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. We meter per-call usage and tie it to the agent. We seal the decision into the chain. | AgentQ shipped in February 2026. The MCP server is written up. Streamable HTTP with SSE, bearer auth, the full tool catalog registered upfront. Read and write tools, with triggering jobs and creating tickets. Really shipped, really useful. | ◈ |
| What reaches the model | We do not proxy your LLM traffic. We do gate each value that leaves a governed dataset for a prompt. A PHI dataset blocks an endpoint without a BAA. Redaction is per column, and fails closed. | One admin-level switch for the whole setup. No Sharing, Metadata Shared, or Source Data Shared. Their docs promise the model never sees masked values, and never sees credentials. Clear and honest, but it is one lever, not a per-column rule. | ◆ |
| Lineage | A typed lineage graph with access gating at each hop. So an edge never reveals an asset you cannot read. We derive it from the pipelines that own the relationships. It is not a stored column-level graph. | Data Lineage shipped June 2026. It adds 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. We check a signed deploy manifest before rollout. Ed25519 audit-signing keys stay on your instance. They sit in a key directory you control. Kid-keyed rotation fails closed on an unknown key. KMS or HSM custody is a built seam in Auth. It is not a shipped connection. HMAC tokenization keys sit in your environment today, not in a KMS. We would rather say that than have you find out. | Managed single-tenant on AWS, Azure, Google or Oracle. Or self-hosted on any CNCF-conformant Kubernetes cluster, on-prem too. They list air-gapped setups as supported. Plus a Databricks-native mode. Their docs say they do not store raw client data at rest. Derivative data and picked 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 each page. | SOC 2 Type II since April 2024. A $10M Series A, led by BMW i Ventures, in June 2025. A Databricks technology partnership. A regulated client list, with 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. Also the product docs 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. 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 rechecks the session. It fails closed. Their 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. They are vault-reversible. A human with the right authority can get the value back. It happens 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. It covers all five trust services rules. We do not hold one at all. If your team scores that as a gate, not a signal, the talk stops there. 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. Compare that to our in-place catalog scan. It is production-ready for PostgreSQL. Everything else is honestly declared-but-unbuilt. Their field-level lineage, shipped June 2026, is deeper than our typed edge graph. And their engine is real work: inferred checks, freshness, volumetrics, anomaly fingerprinting. Plus a fix path into Jira, ServiceNow and PagerDuty. We have a 20-section profile and a trust score.
We do not have monitors. A page that claimed otherwise would fall apart in the first demo.
Here is the push-back. Their April 2026 launch renames their signal a control. A control is a thing that refuses. Read the docs, though, and the refusal is still advisory. They publish the signal. The agent should consult it, and should behave. The strongest real enforcement in their stack is one setup-wide switch. It decides whether the model sees metadata or actual values. Add RBAC on their own MCP server. Neither of those survives the question an examiner actually asks. That question is not "was the data good." It is "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 bad agent transactions through 2028 to be internal rule breaks, not attacks. That is exactly the case where a signal and good intent are weakest. That is the layer we sell. It sits on top of an engine like theirs perfectly happily.
Questions worth asking both of us
These are the questions we would ask, 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 Ed25519-signed, and are themselves chained. Verification tells apart deletion, truncation and tampering. Run it against a sample chain at /verify. Qualytics: their audit page is an activity list you can filter, with a changes view. Their masking audit log records who revealed what, from which IP. We found no hashing, no signing, and no proof it can't be changed. Ask them to show one.
What happens to a revoked agent mid-session?
DataShield rechecks session authority on each governed tool call. So a revocation lands on the next call. The context then drops to anonymous. Qualytics issues personal and service access tokens, and can revoke them. Their MCP server checks RBAC per call. That beats most of this market. What we could not find is the window. How long does a token stay honoured after you revoke it? Also, do you re-check an in-flight agent conversation? Ask for the number in seconds.
How does GDPR erasure interact with the audit trail?
DataShield crypto-shreds the per-subject key material. That destroys each ciphertext for that subject at once. We also issue an ISO 27560 consent receipt. We HMAC-commit actor IDs in the chain. 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. Their own docs note derivative data and picked values may sit in the setup. 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. There is no anomaly detection, no freshness SLAs and no incident workflow. We are not going to build them this year. What we do: profile and score the governed datasets agents read. We classify the sensitive fields, and tokenize them. Per call, we decide whether an agent may touch them. We keep proof. If the pain is a bad number in a dashboard, buy them. If it is an agent no one can account for, buy us. Plenty of teams will sensibly run both. Their signal feeds our rules.
Qualytics ships an MCP server and AgentQ too. What's different?
Theirs is a well-built console for their platform. Streamable HTTP, bearer auth, RBAC per tool call. Tools to search assets, read trend data, trigger jobs, tag things and open tickets. The agent is talking about your data. Ours is where agents query the governed data itself. The tool token carries a scope ceiling. We authorize the call before dispatch. Usage is metered and tied to the agent. The decision is sealed into a chain you can verify later. Different jobs. Want an agent that can ask how healthy a table is? Theirs is the better tool. Want an agent that reads regulated rows under a rule you can prove later? That is us.
Does DataShield have SOC 2?
Not yet, and we will not say we do. Qualytics does, since April 2024, and that is a fair point against us. What we offer instead: a public threat model. Also a verifier anyone can run without an account. Also 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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