Head-to-head · updated 13 September 2026
DataShield vs Atlan: the context layer explains the data, but who governs it?
Atlan is very good at one thing. It is a hard thing, too. It teaches your AI what your data means. Point it at Snowflake, Databricks, Looker, and SharePoint. It builds a shared context layer. Think glossary terms, lineage, owners, certification status. Their hero line is blunt. It names the gap they sell into. "Your AI doesn't know your business. Let's fix that." Gartner made them a Leader in January 2026. They were a Visionary the year before. That's earned. We won't argue with it.
Ours is not a catalog for your whole estate. We do have a real catalog, though. It scans a live PostgreSQL source in place. No rows ever leave it. That's PostgreSQL today, not your 80 connectors. What we add sits one floor down from Atlan. We tokenize datasets at ingest. Agents then query tokenized data over MCP. Turning a token back into real data: that's privileged. It is audited, always. Each checked tool call gets re-checked. Against the agent's access, right then. Each decision seals into a hash chain. You can verify it yourself, without trusting us. Here is the honest split. It includes the rows Atlan wins.
The short version
Pick DataShield when
- Someone will ask you to prove an agent's access log was not edited. An auditor, an examiner, or Article 12 of the EU AI Act. Our chain answers with math, not a rule PDF. Run the verifier.
- You need to pull an agent's access mid-session. The very next tool call should fail. Not the next token refresh. The next call.
- The catalog, the rule decisions, and the evidence all run on your own infrastructure. You hold the keys. Atlan has no self-hosted edition we could find.
- The values themselves need governing, not just the labels on them. Tokens at ingest, masked views, a privileged detokenize that writes an audit row.
Pick Atlan when
- You need one map of a wide estate. Warehouses, BI tools, SaaS apps, document stores. Their 80+ connectors are years of work. We won't match them.
- The data governance office is buying. Glossary, ownership, certification, stewardship workflow, and lineage across teams: that's their day job. The UX is built for business users.
- Your agents keep giving wrong answers because they lack business context. That is the exact problem their MCP server and Context Agents were built for.
- Procurement wants analyst cover. Gartner Leader in 2026, Forrester Leader in 2024 and 2025, and a logo list running from Mastercard to the NHS.
Bottom line: Atlan tells your agent what the data means. DataShield decides whether that agent may read it. It keeps proof of the decision, too. These are different floors of the same building. Running both is normal.
Feature by feature
Competitor cells describe what Atlan's public site says. Press releases too. We use the date above. If we got something wrong, email support@myorg.ai. We'll fix it and credit you.
| What matters | DataShield | Atlan | Edge |
|---|---|---|---|
| Catalog and connector coverage | Providers register over live connections. We scan and profile in place, no row egress. GA covers PostgreSQL only. Snowflake, BigQuery, and Databricks sit on the roadmap. So do S3, SQL Server, and Salesforce. No handler exists yet. Our own config file gates that honesty. | 80+ connectors span warehouses and lakehouses. Plus BI tools, SaaS apps, and document stores. Plus an Iceberg-based metadata lakehouse. This is the core of the product. It's the best part of it. | ◇ |
| Business glossary and lineage | Our glossary comes from entity types. It sits on an ISO/IEC 11179 data-element-concept table. Lineage traversal is typed, with access gating at each hop. It comes from the pipelines that own the relationships. Not its own graph store. | A mature glossary. Automated lineage across the estate. Bi-directional tag sync with Snowflake since 2023. Tag propagation downstream through lineage, too. Deeper and broader than ours. | ◇ |
| Field classification | 129 built-in field classes cover PII and PHI. Plus financial data and secrets. That includes all 18 HIPAA Safe Harbor identifiers. We use regex plus checksum validation. Luhn, NPI, Verhoeff, ABA, IBAN, GTIN. Plus column-name lexicons and anti-pattern suppressors. No model runs here. Verdicts reproduce from a config digest. It shipped out of shadow mode days ago. We'll say that out loud. | Classification tag management and propagation. Plus AI-generated descriptions from Context Agents. The tags are governance labels you apply and sync. We found no deterministic detector library described. | ◈ |
| Audit evidence | A SHA-256 hash chain, with Ed25519-signed checkpoints. Those checkpoints are themselves chained. Verification names the exact failure: tampering, insertion, deletion, or truncation. Try the verifier. | Structured audit events fire for each MCP tool call. Sensitive fields get masked before storage. AI edits get stamped "Updated using Atlan AI". That's good hygiene. We found no cryptographic tamper evidence. | ◆ |
| Agent authorization | Each checked tool call passes a scope ceiling. It also passes a consented-tool allowlist. Plus an access tier and a revocation re-check. That happens before dispatch. The call fails closed. | Atlan's roles, personas, and access rules apply. To each MCP tool call. No extra permissions need configuring. That governs the metadata surface, though. It doesn't stand between an agent and your rows. Not the ones in your warehouse. | ◆ |
| Break-glass | Scoped, time-boxed emergency access for agents. It's gated by admin, IP allowlist, and step-up auth. It auto-revokes on its own. It can't be quietly deleted from the log. | Not described in their public docs. | ◆ |
| GDPR erasure | Crypto-shred of per-subject key material. Plus ISO 27560 consent receipts. Actor identities in the chain carry HMAC commitments. The audit chain still verifies afterward. | Not described in their public docs. A catalog holds metadata, not subject records. The question lands differently here. It's still worth asking. | ◆ |
| Tokenization and data handling | We apply deterministic, join-preserving, vault-reversible tokens at ingest. We also generalize quasi-identifiers. Dates become year, decade, or age band. ZIPs become 3 or 4 digits. Phones, SSNs, and emails get partial masking. Each column gets a measured cardinality-reduction score. Masking and generalization are features you switch on, not defaults. Catalog scans store no column values by default. | By design, they index and annotate metadata. They don't move or store the underlying data. Tokenization isn't a word they use. | ◆ |
| MCP and agents | More than 200 MCP tools span Ontology, Auth, Corpus, and Lighthouse. Auth issues MCP tool tokens with scope ceilings. It meters each call against the agent. It seals the result, too. Agents query checked data itself, not just its description. | A real MCP server, and a good one. Reads use semantic_search and get_assets. Plus traverse_lineage and resolve_metadata. Writes cover description, tag, certification, and glossary. Back into the catalog. It serves context, not rows. | ◈ |
| Deployment | Self-hosted, in your own cloud or data center, your call. Or a dedicated single-tenant server we operate. Docker images cover Auth, Ontology, Corpus, and Lighthouse. A signed deploy manifest, checked by Guardian, comes with them. Ed25519 audit-signing keys stay on your instance. You control the key directory. Kid-keyed rotation fails closed. It fails on an unknown key. KMS or HSM custody is a built seam in Auth. It is not a shipped integration. HMAC tokenization keys sit in your environment today. Not in a KMS. We say so plainly. | SaaS, full stop. We found no self-hosted or on-prem edition on their site. Their trust pages didn't resolve at the URLs we tried. Ask them directly what leaves your environment. | ◆ |
| Maturity signals | Auth, Guardian, and Lighthouse are all live in production. Guardian and Lighthouse have run since April 2026. SOC 2 isn't certified yet, and we say so. No Gartner placement, either. | A $105M Series C in May 2024. A $750M valuation. $206M raised in total. Gartner Leader in 2026. Forrester Leader in 2024 and 2025. A long enterprise reference list, too. | ◇ |
| Pricing | Published model, scoped instant quote, no sales wall. | Quote only. Their pricing page is a contact form. It's headlined "An honest conversation about your AI context gap." Third-party estimates put the median near $50K a year. That's per year. Enterprise deals run well past $120K. Atlan publishes no figures itself. | ◆ |
◆ DataShield leads◇ Atlan leads◈ comparable
Atlan claims come from atlan.com. Plus Atlan's own press releases. Last checked on 13 September 2026. We link them below, not just a paraphrase from memory.
Three things you get here that you won't get from a context layer
Proof that survives an audit
A log that can be quietly edited proves nothing. Ours is a hash chain with signed checkpoints. The verifier tells you what broke. Not just that something did. That's the property EU AI Act Article 12 reviewers care about. Most of all. Same for HIPAA §164.312(b). A field that says "Updated using Atlan AI": that is a label. It's useful. But it isn't evidence. Try ours in your browser, no signup.
Authority that can change mid-flight
An analyst leaves on a Friday. Their agent is still 20 minutes into a 40-minute job. DataShield re-checks the next checked tool call. It checks against current access, right then. It fails closed. Inheriting roles at session start doesn't catch that. How Auth does it.
Governance on the values, not just the labels
A catalog can tell an agent a column holds PHI. It can't stop the agent from reading it, though. We tokenize at ingest. We serve masked views. Turning a token back into real data: that's privileged. It writes an audit row, each time. The label and the control live in the same place. See the catalog and the data plane.
Where Atlan is genuinely stronger
Let's be straight about this. Atlan has 80+ connectors. It has an Iceberg-backed metadata lakehouse, too. Its lineage stitches across estates we couldn't reach, for years. CME Group catalogued 18 million assets and 1,300 glossary terms with them. Their Context Agents have written more than 690,000 asset descriptions. That spans 50+ enterprise customers. 87% rated on par with a human. Some rated better. Gartner moved them from Visionary to Leader in twelve months. Their MCP server is a clean piece of design. Reads cover search, assets, lineage, and metadata. Writes cover descriptions, tags, certification, and glossary terms. Your existing roles apply to each call. No new permissions to set up. Does your problem come from agents not knowing your columns? What they mean? Buy Atlan. We would.
Here's the push-back, though. Their pitch says context makes AI trustworthy. Context is necessary, sure. But it isn't a control. Their own 2026 launch talks about defining governance policy once. Then enforcing it everywhere. Ask what "enforce" means. When does it trigger, exactly? In their docs, enforcement lands on the metadata tool call. That's the surface they own. The rows sit in Snowflake, though. Snowflake doesn't ask Atlan for permission. Gartner expects most unauthorized agent transactions through 2028. Most will be internal rule breaks. Not attacks. A label after the fact is the weakest answer. That's the exact case. That gap is the layer we sell. It sits under a catalog like theirs quite happily.
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, it can. Each record commits to the one right before it. Checkpoints get signed and chained. Verification tells deletion apart from truncation and from tampering. Run it against a sample chain at /verify. Atlan logs each MCP tool call. It logs it as a structured audit event. It masks sensitive fields before storage, which is good practice. We found no tamper-evidence method in their public docs. Ask them to show one.
What happens to a revoked agent mid-session?
DataShield re-checks access on each checked tool call. Revocation lands on the very next call. Atlan says your roles, personas, and access rules apply. To each tool call. No extra setup needed, they say. We couldn't tell whether that check runs per call. Or once per session. 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 per-subject key material. Then it issues an ISO 27560 consent receipt. Actor identities in the chain carry HMAC commitments. The evidence still verifies once the subject is gone. Atlan doesn't describe an erasure method in the docs we read. A catalog mostly holds metadata, so the blast radius is smaller. But descriptions and sample context can still carry personal data. Ask for the method, not the workflow.
Is DataShield an Atlan alternative, or do we run both?
Most teams run both. We won't catalog your whole estate. Our catalog covers a live PostgreSQL source today. No rows ever leave it. Need one map of Snowflake, Databricks, Looker, and SharePoint? Atlan is the better buy. Where we replace them is narrower. Picture a regulated team. It can't put a context layer over sensitive metadata. Not behind a third-party SaaS boundary. One that wants classification, tokenization, and agent access. Plus audit, in one self-hosted stack.
Atlan ships an MCP server too. What's actually different?
Theirs serves context. An agent can search assets, walk lineage, resolve metadata. Then it can write back a description. Or a tag, or a certification status. That's a strong catalog design. Their write-back story beats most. Ours is where the checked data itself gets queried. The tool token carries a scope ceiling. The call gets authorized before dispatch. It gets metered against the agent, too. The decision seals into the chain. Want an agent to ask what a column means? Use theirs. Want an agent to read the column under a rule? One you can later prove? Use ours.
Does DataShield have SOC 2?
Not yet. We won't imply otherwise, ever. Auth ships with a public threat model. Plus a verifier anyone can run. Guardian and Lighthouse have run since April 2026. Design-partner terms include source escrow. That way a small vendor isn't a single point of failure. Details on the security page.
- Atlan's current positioning: "Your AI doesn't know your business. Let's fix that." Plus 80+ connectors and 690K+ generated descriptions. Plus the CME Group 18-million-asset case study. - atlan.com, 13 Sep 2026
- Atlan MCP server: "Every tool your team uses. Every agent you deploy. One context layer." Reads: semantic_search, get_assets, traverse_lineage, and resolve_metadata. Writes: descriptions, classification tags, certification status, and glossary terms. Changes get stamped "Updated using Atlan AI". - atlan.com/mcp, 13 Sep 2026
- Named a Leader in the 2026 Gartner Magic Quadrant. That's for Data and Analytics Governance Platforms. Up from Visionary in 2025. The release names "agentic governance & policy orchestration". Plus an Iceberg-based metadata lakehouse. - atlan.com, 6 Jan 2026
- A $105M Series C. Led by GIC, with Meritech, Salesforce Ventures, and PeakXV. A $750M valuation, $206M raised in total. Positioned as "the control plane for the data and AI stack". - atlan.com, 8 May 2024
- Pricing is quote-only. The pricing page is a contact form. It's headlined "An honest conversation about your AI context gap." No tiers. No numbers, free tier, or open-source edition. - atlan.com/pricing, 13 Sep 2026
- Bi-directional tag sync with Snowflake. Launched as one of the first Snowflake data governance partnerships. One of its kind. - Humans of Data (Atlan), 28 Sep 2023
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