Head-to-head · updated 13 September 2026
DataShield vs Lightup: the table is healthy, but may the agent read it?
Lightup sells enterprise data observability, and their hero says it plainly: "Enterprise Data Observability at the Speed of Light." Point them at a warehouse and they will learn what normal looks like, then page you when it stops. Three anomaly algorithms, seasonality handling, no thresholds to hand-tune, and a one-click Recommendations feature that closes coverage gaps for you. AMD, Skechers and McDonald's run it. It is a good product and this page is not going to pretend otherwise.
We are not a data observability tool. DataShield has no monitors, no alerts, no freshness SLAs and no incident queue, and we will say that again further down because it matters. What we do is govern the datasets an agent actually reads. 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 current authority, and the decision is sealed into a hash chain you can verify without trusting us. Here is the honest split, rows Lightup wins included.
The short version
Pick DataShield when
- An agent is reading customer records and somebody will later ask you to prove the access log was not edited. 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.
- You want the columns labelled as PII or PHI before anything touches them, and tokenized at ingest rather than masked in a dashboard.
- The whole thing has to run on your own infrastructure, on keys you hold, and you would like a price before you book a call.
Pick Lightup when
- Your problem is a pipeline that breaks quietly. Their anomaly models learn seasonality and trend from history, and you do not have to write the rules.
- You need alerts that reach a human. Slack, PagerDuty, Teams, email, with incidents and backtesting. We have none of that and are not building it this year.
- Your stack is Databricks, Snowflake, Dremio or ksqlDB and you want quality checks in place this quarter. Their connector and partner work is real.
- You want quality signals published back into a catalog. They shipped a Collibra integration in 2023 and an Alation partnership since.
Bottom line: Lightup tells you whether a table is trustworthy. DataShield decides whether an agent may read it and keeps proof of the decision. These are different jobs, and most teams who buy us have already bought something like them.
Feature by feature
Competitor cells describe what Lightup's public site and blog say as of the date above. If we have mischaracterised something, email support@myorg.ai and we will correct it, credited.
| What matters | DataShield | Lightup | Edge |
|---|---|---|---|
| Anomaly detection and monitoring | We have none. No statistical anomaly detector, no monitors on warehouse tables, no freshness SLAs. We profile and score datasets, which is a different thing, and we would rather say so than fudge it. | The core of the product, and it is good. Three algorithms cover values outside expectation, sharp change and slow-burn drift, with seasonality baked in, plus backtesting on history and feedback to tune accuracy. | ◇ |
| Incidents and alerting | Nothing to speak of. Guardian watches our own platform health, not your pipelines. | Incidents, Slack, PagerDuty, Teams and email routing, dashboards you can push into your BI tool. | ◇ |
| Profiling and quality scoring | Every dataset gets a 20-section analysis profile: completeness, field statistics, patterns, column semantics, relationship graph, quality metrics, lineage and compliance governance. A weighted composite trust score rolls up per entity type, domain and whole estate, with per-axis breakdown and trend, recomputed hourly. | Metrics, sliced metrics, custom quality indicators and dashboards. Strong on the time series, quieter on a single deep profile of one dataset. | ◈ |
| PII and PHI classification | 129 field classes covering PII, PHI, financial data and secrets, including all 18 HIPAA Safe Harbor identifiers. Deterministic, no model in the loop, and every verdict is stamped so you can re-derive it and diff it later. | We found no classification or sensitive-data labelling in their public material. Ask them what happens when a monitored column holds a national ID. | ◆ |
| 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. Catalog sample policy defaults to omit, so no column values are copied into the catalogue. | Not their vocabulary. Metrics are computed over your data; sensitive values are handled by whatever your warehouse already does. | ◆ |
| Audit evidence | SHA-256 hash chain with Ed25519-signed checkpoints that are themselves chained. Verification names the failure: tampering, insertion, deletion or truncation. Try it. | Langfuse tracing gives visibility into every AI session. Useful for debugging. We found no tamper-evidence mechanism. | ◆ |
| Agent authorization | Every governed tool call passes a scope ceiling, an authority tier and a revocation re-check before dispatch. It fails closed. | Lightup Agentic authenticates with "your existing Lightup API token, so there is no separate authentication to manage." One token, the reach of your account. We found no per-call decision point. | ◆ |
| MCP and agents | More than 200 MCP tools across Ontology, Auth, Corpus and Lighthouse. Tool tokens carry scope ceilings and every call is metered and attributed to the agent. | Lightup Agentic is a real MCP server, in beta, for Claude Code, Claude Desktop, Gemini CLI and other MCP clients. It drives their platform in natural language: create metrics, investigate incidents, onboard sources. | ◈ |
| Break-glass | Scoped, time-boxed emergency access for agents. It auto-revokes and 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. The audit chain still verifies afterwards. | A published DPA, and metric history with 30-day or 6-plus-month retention by tier. Subject-level erasure is not described. | ◆ |
| Deployment | Self-hosted in your own cloud or data center, or a dedicated single-tenant server we operate. Ed25519 audit-signing keys can live in your KMS or HSM. HMAC tokenization keys sit in your environment or derive from your machine key today, not in a KMS. | Cloud tier is cloud only. Enterprise adds "Cloud, Hybrid" on single-node or Kubernetes. What hybrid includes is not itemised publicly. | ◈ |
| Maturity signals | Live in production, with Guardian and Lighthouse there since April 2026. SOC 2 not yet certified, and we say so. | Founded 2019, roughly $9M disclosed Series A in 2023, CB Insights AI 100 in 2024, CIOReview award, named enterprise logos. Longer track record than ours, smaller balance sheet than their category rivals. | ◇ |
| Pricing | Published model, scoped instant quote, no sales wall. | Two tiers, both behind a Get Quote button. No figures published at either level. | ◆ |
◆ DataShield leads◇ Lightup leads◈ comparable
Lightup claims are drawn from lightup.ai and Lightup's own blog posts, 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 that can be silently 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 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 re-checked against current authority and fails closed. A monitoring layer sends you an alert about it on Monday. How Auth does it.
The columns get labelled before the agent reads them
Quality scores tell you a column is 98% populated. They do not tell you it is full of national IDs. We classify against 129 field classes first, then tokenize, then let the agent query. What the catalog holds.
Where Lightup is genuinely stronger
Start with the thing we cannot do. Lightup learns what a metric normally does, accounts for seasonality and trend, and flags the outlier without anyone writing a rule. You can backtest the rule against history before you trust it, and feed corrections back in. On top of that sit incidents, alert routing to Slack and PagerDuty, reconciliation between two sources, remediation actions, lineage for root-cause work, and since July 2025 quality checks on unstructured data. Their Recommendations feature, shipped in August 2025, finds your coverage gaps and applies the fix in one click. That is six years of focused work and we have none of it.
The push-back is about what their agent story actually governs. Lightup Agentic is a genuine MCP server, and the page is refreshingly concrete: Claude Code, Gemini CLI, SSE or Streamable HTTP. But the credential is "your existing Lightup API token, so there is no separate authentication to manage," and the accountability story is Langfuse session tracing. That is a control plane for their product, driven by an agent with your whole account in its hands. It is not a data plane where each read of a governed dataset is authorized on its own merits and written into evidence somebody else can check. If your agents are only tuning monitors, theirs is fine. If your agents are reading the records those monitors watch, that is our seam.
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. Lightup: their agentic page offers Langfuse tracing over AI sessions, which is observability, not tamper evidence. Ask them to show you a mechanism.
What happens to a revoked agent mid-session?
DataShield re-checks authority on every governed tool call, so revocation lands on the next call. Lightup Agentic runs on your existing Lightup API token. We could find no mid-session revocation or scope ceiling in their public material. Ask how long a leaked token keeps working, and what it can reach while it does.
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. Lightup publishes a DPA and sets metric retention by tier, 30 days on Cloud and 6-plus months on Enterprise. Per-subject erasure is a different question. Ask for the mechanism, not the retention setting.
Is DataShield a data observability tool? Do we drop Lightup?
No, and no. We have no anomaly detection, no monitors on your warehouse tables, no freshness SLAs and no incident management. If a silent pipeline break is your pain, buy a monitoring tool. Run us over the datasets agents read, where the obligation is classification, authorization and evidence. Plenty of teams will sensibly run both, and Lightup's own Collibra and Alation work shows they expect to sit next to other things.
Lightup ships an MCP server too. What's different?
Theirs drives their platform: create a metric, investigate an incident, onboard a source, all in plain English. Nice work, and we would use it. Ours is where the governed data itself is queried, so the tool token carries a scope ceiling, the call is authorized before dispatch, and the decision is sealed into a chain. One controls a product. One controls access to records. Ask both of us which one your agent is holding.
Does DataShield have SOC 2?
Not yet, and we will not imply otherwise. Auth is live 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. Lightup publishes no certification badge we could find either, so ask us both.
- Lightup's positioning: "Enterprise Data Observability at the Speed of Light", "powered by AI anomaly detection and agentic interfaces." — lightup.ai, 13 Sep 2026
- Lightup Agentic is "a Model Context Protocol (MCP) server that connects your Lightup instance to AI coding assistants like Claude and Gemini", authenticated with "your existing Lightup API token". — lightup.ai, 13 Sep 2026
- Three anomaly algorithms (values outside expectations, sharp change, slow-burn trend change) with seasonality and backtesting on history. — lightup.ai, 13 Sep 2026
- Genie, a GenAI copilot assistant, launched in beta preview "Powered by OpenAI" with a bring-your-own-model option. — Lightup blog, 17 Dec 2024
- Lightup Recommendations: "intelligent suggestions for the next best action" that close data quality coverage gaps in one click. — Lightup blog, 27 Aug 2025
- Two tiers, Cloud and Enterprise, both gated behind Get Quote. Cloud is cloud-only with 30-day retention; Enterprise offers "Cloud, Hybrid" with 6+ months. — lightup.ai pricing, 13 Sep 2026
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Unsupervised quality checks, and the evidence they don't produce.
AllEvery comparison
One honest scorecard per vendor.
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