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, and it is a hard thing. It teaches your AI what your data means. Point it at Snowflake, Databricks, Looker, SharePoint and the rest, and it builds a shared context layer: glossary terms, lineage, owners, certification status. Their hero line is blunt about the gap they sell into. "Your AI doesn't know your business. Let's fix that." Gartner made them a Leader in January 2026, up from Visionary the year before. That is earned, and we are not going to argue with it.
We are not a catalog for your whole estate. We have a real catalog, but it scans a live PostgreSQL source in place, with no rows leaving it. That is PostgreSQL today, not your 80 connectors. What we add sits one floor down from Atlan. Datasets are tokenized at ingest; agents query tokenized data over MCP; detokenization is a privileged, audited operation. Every governed tool call gets checked against the agent's authority right then. Every decision is sealed into a hash chain you can verify without trusting us. Below is the honest split, rows Atlan wins included.
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 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 catalog, the policy decisions and the evidence all have to run on your own infrastructure, on keys you hold. 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 and we are not going to match them.
- The data governance office is buying. Glossary, ownership, certification, stewardship workflow and lineage across teams is their day job, and 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, and keeps proof of the decision. These are different floors of the same building, and running both is normal.
Feature by feature
Competitor cells describe what Atlan's public site 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 | Atlan | Edge |
|---|---|---|---|
| Catalog and connector coverage | Providers registered over live connections, scanned and profiled in place with no row egress. GA for PostgreSQL only. Snowflake, BigQuery, Databricks, S3, SQL Server and Salesforce are declared on the roadmap with no handler yet, and our own config file gates that honesty. | 80+ connectors across warehouses, lakehouses, BI tools, SaaS apps and document stores, plus an Iceberg-based metadata lakehouse. This is the core of the product and the best part of it. | ◇ |
| Business glossary and lineage | Glossary materialized from entity types on an ISO/IEC 11179 data-element-concept table. Typed lineage traversal with access gating at every hop, derived from the pipelines that own the relationships rather than stored as a separate graph. | Mature glossary, automated lineage across the estate, bi-directional tag sync with Snowflake since 2023, and tag propagation downstream through lineage. Deeper and broader than ours. | ◇ |
| Field 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 reproducible from a config digest. Shipped out of shadow mode days ago, and we will say that out loud. | Classification tag management and propagation, with AI-generated descriptions from Context Agents. The tags are governance labels you apply and sync, not a deterministic detector library we could find described. | ◈ |
| Audit evidence | SHA-256 hash chain with Ed25519-signed checkpoints that are themselves chained. Verification names the failure: tampering, insertion, deletion or truncation. Try the verifier. | Structured audit events for every MCP tool call, with sensitive fields masked before storage, and AI edits stamped "Updated using Atlan AI". That is good hygiene. We found no cryptographic tamper evidence. | ◆ |
| Agent authorization | Every governed tool call passes a scope ceiling, a consented-tool allowlist, an authority tier and a revocation re-check before dispatch. The call fails closed. | Atlan's roles, personas and access rules apply to every MCP tool call with no separate permissions to configure. That governs the metadata surface. It does not stand between an agent and the rows in your warehouse. | ◆ |
| Break-glass | Scoped, time-boxed emergency access for agents. Admin plus IP allowlist plus step-up gated, auto-revoking, and it cannot be quietly deleted from the log. | 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 audit chain still verifies afterwards. | Not described in their public material. A catalog holds metadata rather than subject records, so the question lands differently, but it is still worth asking. | ◆ |
| 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. 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 rather than move or store the underlying data. Tokenization is not vocabulary they use. | ◆ |
| MCP and agents | More than 200 MCP tools across Ontology, Auth, Corpus and Lighthouse. Auth issues MCP tool tokens with scope ceilings, meters every call against the agent, and seals the result. Agents query governed data itself, not only its description. | A real MCP server, and a good one. semantic_search, get_assets, traverse_lineage and resolve_metadata for reads; description, tag, certification and glossary writes back into the catalog. It serves context, not rows. | ◈ |
| 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, with a signed deploy manifest Guardian verifies. 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 say so. | SaaS. We could not find a self-hosted or on-prem edition on their site, and their trust pages did not resolve at the URLs we tried. Ask them directly what leaves your environment. | ◆ |
| 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. No Gartner placement. | $105M Series C in May 2024 at a $750M valuation, $206M raised in total, Gartner Leader in 2026, Forrester Leader in 2024 and 2025, and a long enterprise reference list. | ◇ |
| Pricing | Published model, scoped instant quote, no sales wall. | Quote only. Their pricing page is a contact form headlined "An honest conversation about your AI context gap." Third-party marketplace estimates put the median around $50K a year, with enterprise deals well past $120K, but Atlan publishes no figures. | ◆ |
◆ DataShield leads◇ Atlan leads◈ comparable
Atlan claims are drawn from atlan.com and Atlan'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 context layer
Proof that survives an audit
A log that can be quietly edited 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 care about. A field that says "Updated using Atlan AI" is a label. It is useful, but it is not 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. With DataShield the next governed tool call is re-checked against current authority, and it fails closed. Inheriting roles at session start does not catch that. How Auth does it.
Governance on the values, not just the labels
A catalog can tell an agent that a column holds PHI. It cannot stop the agent from reading it. We tokenize at ingest, serve masked views, and make detokenization a privileged step that writes an audit row. The label and the control live in the same place. See the catalog and the data plane.
Where Atlan is genuinely stronger
Let us be straight about this. Atlan has 80+ connectors, an Iceberg-backed metadata lakehouse, and lineage stitched across estates we could not 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 across 50+ enterprise customers, and 87% were rated on par with or better than a human. Gartner moved them from Visionary to Leader in twelve months. Their MCP server is a clean piece of design: reads for search, assets, lineage and metadata, writes for descriptions, tags, certification and glossary terms, with your existing roles applied to each call and no new permissions to set up. If your problem is that your agents do not know what your columns mean, buy Atlan. We would.
Here is the push-back. Their pitch is that context makes AI trustworthy, and context is necessary but it is not a control. Their own 2026 launch talks about defining governance policy once and enforcing it everywhere. Ask what "enforce" means at the moment an agent reads a customer table. In their material the enforcement lands on the metadata tool call, because that is the surface they own. The rows sit in Snowflake, and Snowflake does not ask Atlan for permission. Gartner expects most unauthorized agent transactions through 2028 to be internal policy violations rather than attacks, which is exactly the case where a label after the fact is the weakest possible answer. That gap is the layer we sell, and 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 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 against a sample chain at /verify. Atlan logs every MCP tool call as a structured audit event and masks sensitive fields before storage, which is good practice. We found no tamper-evidence mechanism in their public material. Ask them to show one.
What happens to a revoked agent mid-session?
DataShield re-checks authority on every governed tool call, so revocation lands on the next call. Atlan says your roles, personas and access rules apply to each tool call with no separate setup. We could not find out 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 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. Atlan does not describe an erasure mechanism in the material 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 mechanism, not the workflow.
Is DataShield an Atlan alternative, or do we run both?
Most teams run both. We are not going to catalog your whole estate. Our catalog covers a live PostgreSQL source today, with no rows leaving it. If you need one map of Snowflake, Databricks, Looker and SharePoint, Atlan is the better buy. Where we replace them is narrower: a regulated team that cannot put a context layer over sensitive metadata behind a third-party SaaS boundary, and that wants classification, tokenization, agent authority and 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 write back a description, a tag or a certification status. That is a strong catalog design and their write-back story is better than most. Ours is where the governed data itself is queried. The tool token carries a scope ceiling, the call is authorized before dispatch, the call is metered against the agent, and the decision is sealed into the chain. If you want an agent to ask what a column means, use theirs. If you want an agent to read the column under a policy you can later prove, use ours.
Does DataShield have SOC 2?
Not yet, and we will not imply otherwise. Auth ships with a public threat model and a verifier anyone can run. 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.
- Atlan's current positioning: "Your AI doesn't know your business. Let's fix that." plus 80+ connectors, 690K+ generated descriptions and 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 stamped "Updated using Atlan AI". — atlan.com/mcp, 13 Sep 2026
- Named a Leader in the 2026 Gartner Magic Quadrant for Data and Analytics Governance Platforms, up from Visionary in 2025, with "agentic governance & policy orchestration" and an Iceberg-based metadata lakehouse named in the release. — atlan.com, 6 Jan 2026
- $105M Series C led by GIC with Meritech, Salesforce Ventures and PeakXV, at a $750M valuation and $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 headlined "An honest conversation about your AI context gap", with no tiers, numbers, free tier or open-source edition. — atlan.com/pricing, 13 Sep 2026
- Bi-directional tag synchronization with Snowflake, launched as one of the first Snowflake data governance partnerships of its kind. — Humans of Data (Atlan), 28 Sep 2023
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