All analysis was generated autonomously, without human review. Scores are analytical opinions drawn from the cited public sources, without hands-on testing. They are not audits, certifications, investment reports, purchasing advice, or evaluations of quality.
More than 20,000 organizations run Databricks, and Databricks presents Unity Catalog to them as a built-in governance layer, with no Unity Catalog product or meter shown in the fetched pricing text. The catalog checks permissions on every data interaction in a workspace, and Databricks extends that single permission model from tables to registered AI models and to the connections AI agents use. The protection is narrower than the reach: a buyer gets inventory, access control, and audit over models and training data. The cited pages describe no prompt-injection or model-theft defense. Databricks also keeps the platform open to partners, and Immuta sells a native integration that adds its own row and column controls to the same workloads.
How well the company can compete in its security market, scored across eight dimensions against public evidence.
| Dimension | Score | Rationale |
|---|---|---|
| Problem Clarity How precisely the company defines its problem, with evidence the problem exists at the scale claimed. | 3/5 | Unity Catalog names the data and platform buyer and the governance pain, but the pain is qualitative and the non-vendor grounding is limited to Immuta's native integration (s10), a single source rather than multiply-sourced quantified pain. [s2, s10, s6] |
| Capability Depth How specific the technical capabilities are, with evidence beyond marketing claims such as docs and third-party validation. | 4/5 | The docs detail attribute-based access control on governed tags, row filters and column masks, automatic data classification, column-level lineage, audit log system tables, and governance of registered models and MCP servers as securable objects. Immuta's native integration is a third-party validation point. Held at the peer anchor at 4 rather than 5 because the security-relevant slice is governance machinery rather than a dedicated AI defense with an independent evaluation. [s2, s4, s10] |
| Market Timing Whether the market is ready for this product, with evidence that buyers are actively seeking solutions. | 3/5 | The enabler is buyers deploying AI agents and MCP-connected tools through 2026 that need governed access underneath, but the docs showing Unity Catalog extending to MCP servers are vendor materials and the one non-vendor demand signal is Immuta's integration, indirect rather than multiple buyer-side signals. [s5, s3, s10] |
| Team Credibility Demonstrated domain expertise with public signals such as prior exits, publications, and industry recognition. | 4/5 | Databricks was founded in 2013 by the creators of Apache Spark, Delta Lake, MLflow, and Unity Catalog itself, a verifiable sustained build in the data and AI infrastructure domain. That open-source track record matches the anchor cluster at 4, short of a separate independent research record specific to the governance line that a 5 would require. [s6, s9, s11] |
| GTM Proof Evidence of actual traction (customers, revenue signals, partnerships) beyond stated intentions. | 3/5 | Unity Catalog shows no standalone customer reference or line-level revenue figure, and the platform-wide reach across tens of thousands of organizations counts as distribution rather than line-level traction, leaving one ecosystem partnership (Immuta) as the line's own signal. [s6, s7, s8] |
| Funding Efficiency Whether funding matches go-to-market ambition, with signs of capital-efficient growth. | 3/5 | Unity Catalog ships and extends visibly (governing models and MCP servers through 2026), but it carries no line-level revenue or cost, and the parent's raises cannot confirm the line's output per dollar, the default for visible shipping with unconfirmed efficiency. [s7, s8] |
| Category Clarity Whether the company creates or fits a recognizable category that buyers can quickly place in their stack. | 4/5 | Lakehouse data and AI governance is a real category buyers place without vendor coaching and Immuta integrates with it, but Collibra, Alation, and Snowflake Horizon contest the same governance budget, so Unity Catalog fits the corroborated tier rather than as the category definer. [s6, s10, s2] |
| Incumbent Defensibility How vulnerable the core value proposition is to absorption as a feature by a platform vendor. | 4/5 | Unity Catalog is the governance plane built into the lakehouse rather than a catalog sitting on top of a cloud, so a buyer already on Databricks gets governance natively where independent vendors must integrate from outside. Every data and AI interaction in the workspace runs through it, which embeds it deeply. Held below 5 because governance partners such as Immuta run native integrations that add their own controls on the same platform. [s2, s10, s4] |
Unity Catalog treats every data and AI asset in the Databricks lakehouse as something the data and platform team must control and account for. The docs frame the job as enforcing access, tracking lineage, and recording audit over securable objects that include tables, views, volumes, functions, and registered models.
The buyer is the team running data and AI on Databricks who needs one place to decide who can touch what. Independent evidence backs the need rather than the vendor framing alone, with Immuta building a native integration to secure Unity Catalog workloads with row and column controls.
The security-relevant slice is narrower than the whole product. Unity Catalog governs access to models and training tables and audits who used them, which is inventory and authorization over AI assets rather than defense against attacks aimed at the models themselves. [s2, s10, s3]
Unity Catalog models every governed asset as a securable object, and grants permissions to users, service principals, and groups on each one. Tables, views, volumes, functions, and registered models all carry the same permission machinery.
Access control runs through several mechanisms the docs present as complementary. Attribute-based access control applies tag-driven policies across the catalog, row filters and column masks restrict what users see per table, and the company recommends ABAC to scale enforcement on governed tags.
The AI assets reuse the data machinery. A registered model is a subtype of the FUNCTION securable object controlled with GRANT ON FUNCTION, so it carries the same access control, audit, and lineage as a table. Agent tooling is governed one step out: MCP servers run through Unity AI Gateway, a separate control plane that uses Unity Catalog permissions to decide who reaches each server and Unity Catalog connections with managed OAuth to handle credentials for external servers. [s2, s4, s3]
Unity Catalog positions itself as the governance built into the lakehouse rather than a catalog bolted on top. Because every data and AI interaction in a workspace runs through it, the line strengthens the platform and the platform strengthens the line.
That native position is the foothold against the independent governance vendors. Cross-cloud catalogs such as Collibra and Alation sell a neutral layer that spans data platforms, and Databricks is a strategic investor in Alation even as Unity Catalog competes with it, so the contest is platform-native control against vendor neutrality.
On the same platform the position is contestable. Immuta partners with Databricks yet also runs a native integration that adds its own row and column controls to secure the same workloads, which shows governance can come from a partner rather than only the native catalog. [s2, s10]
Unity Catalog reaches buyers as a built-in layer rather than a separate sale. It ships inside the platform that more than 20,000 organizations and 70% of the Fortune 500 run, so its distribution is the platform's distribution. Databricks also runs as an established multinational, with a United Kingdom subsidiary active on the public companies register since 2017.
The dollar traction belongs to Databricks rather than the catalog alone. The company reports a $4 billion revenue run-rate growing over 50% year over year, a $1 billion AI revenue run-rate, and 650+ customers consuming over $1 million a year, figures the financial press reports independently.
The capital backs the same scale and was raised in two steps. Databricks closed a Series K in September 2025, raising $1 billion at over $100 billion. In December 2025 it raised $4 billion more at a $134 billion valuation, a 34% jump, after topping a $4.8 billion fiscal third quarter run-rate growing 55% year over year. That capital funds platform expansion the governance layer rides along with. [s6, s7, s8, s14]
Databricks was founded in 2013 by the original creators of Apache Spark, Delta Lake, MLflow, and Unity Catalog, a sustained open-source lineage in data and AI infrastructure.
The same team operationalized that research into a platform at enterprise scale, reaching tens of thousands of organizations and a multi-billion-dollar revenue run-rate. The throughput to extend governance from tables to models and MCP servers shows the organization can carry a new asset type into the existing control plane.
The team's center of gravity is data and AI infrastructure rather than adversarial AI security specifically. The credibility behind Unity Catalog is the proven ability to build and run governance at lakehouse scale, not a security research record aimed at attacks on models.
The research record is independently visible. Databricks runs a research group, Databricks Mosaic Research, whose members publish machine-learning studies on arXiv, including a 2025 paper on how language-model retrieval scales with training compute. That work is in model training and evaluation rather than the governance line, so it deepens the general engineering record without making Unity Catalog itself a published security-research subject. [s6, s9, s11]
Databricks delivers Unity Catalog as the governance layer enterprises already operate inside the platform, so adding governed control over models and MCP servers extends an established trust boundary rather than introducing a new vendor.
The data-handling controls are concrete. The catalog classifies and tags sensitive data automatically, masks columns and filters rows through ABAC and per-table policies, and records access in audit log system tables, which addresses the exposure that makes teams cautious about opening data and models to agents.
The readiness gap is in how the security role is framed. A buyer evaluating Unity Catalog as AI defense must accept that it governs and audits access rather than blocking attacks on the model, so its strongest trust story is for teams treating AI risk as access control and accountability rather than runtime protection.
The broader platform carries an attack surface that access governance does not remove. Independent vulnerability records describe a remote code execution flaw in the Databricks JDBC driver and a server-side request forgery in Azure Databricks that let an unauthorized attacker elevate privileges, both reported and addressed. These disclosures involve the platform and its connectors rather than Unity Catalog, but they show why treating governed access as protection against attackers misreads what the catalog does. [s4, s5, s2, s12, s13]
| Company | Relationship | Note | Compare |
|---|---|---|---|
| Immuta | competes with | Data security platform that partners with Databricks and runs a native integration adding its own row and column controls on the same lakehouse data. | |
| Collibra | competes with | Enterprise data and AI governance platform that spans clouds rather than being native to one lakehouse. | N/AWe scored these companies at different scopes, so the totals measure different things. |
| Alation | competes with | Data catalog and governance vendor whose neutral cross-cloud layer contests the same governance budget, with Databricks a strategic investor. | N/AWe scored these companies at different scopes, so the totals measure different things. |
| Snowflake Horizon | competes with | The competing data cloud's native governance layer, the equivalent built-in control plane on the rival platform. |
Add analyzed competitors to compare them side by side with Unity Catalog.
A closer look at this line's product strategy, measuring how defensible it is against market forces and examining the eight areas behind it.
pivot urgently
What a buyer gets from Unity Catalog is governance machinery: grants, tags, lineage, and audit over data and models. Nothing outside Databricks compels that choice, since the cited record names no regulation requiring the catalog, a funded rival can rebuild the software, and the cited documentation identifies workspace lineage and access metadata without documenting a cross-customer security corpus. The durable piece is placement: every data interaction in a Databricks workspace passes through the catalog's permission checks, so a customer that leaves must re-permission, re-tag, and rebuild lineage and audit. That friction is migration work rather than a structural lock, since the cited record establishes neither a network effect nor regulatory data residency.
| Dimension | Score | Rationale |
|---|---|---|
| Value Delivery Does the product sell software as the product, or judgment, trust, or accountability with software as the delivery mechanism. | 1/3 | Customers get governance software, grants, tags, lineage, and audit over securable objects, rather than a judgment-and-accountability outcome a buyer cannot reproduce in-house, the same delivery profile as the large-platform peers. |
| Switching Cost How expensive leaving is for a customer: data portability, integrations, learned workflows, network effects, regulatory data residency. | 2/3 | Unity Catalog operates beneath every data interaction in the workspace, so leaving means re-permissioning, re-tagging, and rebuilding lineage and audit across the lakehouse. That is meaningful friction from data history and learned workflows rather than a structural lock. The cited record establishes no network effect and no regulatory data residency, the conditions the top band asks for, so the exit cost is migration work a determined buyer can budget. |
| Compliance Moat Whether certifications, liability acceptance, or audit trails block an easy replacement. | 1/3 | The cited record identifies governance and audit capabilities but no regulation or authorization that requires Unity Catalog specifically. Its classification and audit features help compliance as a convenience bundled into the platform rather than a mandated control that locks the buyer in. |
| Problem Complexity Whether the product requires ML, optimization, real-time systems, or years of specialized expertise. | 3/3 | Enforcing attribute-based policies on tagged tables and schemas, row and column controls on tables, and lineage across a governance model that also reaches registered models and MCP servers at lakehouse scale is genuinely hard engineering. |
| Buyer Profile Whether buyers are SMB operators, mid-market IT teams, or regulated enterprises and governments with procurement gates. | 2/3 | The evidenced buyer is the data and platform team at organizations large enough to run data and AI on a lakehouse. The 20,000-plus organizations and Fortune 500 figures describe the parent platform's reach rather than this line's, and scoring policy 1.0.0 bars a company-wide roster from establishing a line's buyer class, so no regulated-enterprise or government buyer of the catalog itself is on the record. |
| Layer Whether the product is an end-user application, a platform with application features, or infrastructure other applications depend on. | 2/3 | The catalog sits at the governance plane inside the lakehouse, a defensible position because every interaction runs through it, but it is governance machinery that partners can add to on the same platform rather than being embedded in the model or the compute itself. |
| Proprietary Data, Content, or IP Whether the product accumulates datasets, content licenses, or IP that a rival cannot recreate from scratch. | 1/3 | The cited documentation describes workspace lineage and access metadata but does not document a proprietary cross-customer security corpus. No proprietary adversarial or cross-customer pooled dataset is evident, so the data position is replicable rather than a moat. |
Databricks targets the data and platform team governing data and AI on the lakehouse, a segment the broader platform already reaches. Unity Catalog extends an existing relationship rather than opening a new buyer. Whether that lowers the cost of reaching the segment is an inference the cited record does not measure at the line level.
The segment is the enterprise running data and AI at scale. The company reports more than 20,000 organizations and 70% of the Fortune 500 on the platform, and when enabled, the catalog operates beneath every data interaction rather than being sold to a separate slice of that base. Workspaces created after November 8, 2023 are enabled automatically while older ones are directed to an upgrade workflow, so enablement is the default for newer workspaces rather than a per-workspace sale.
The cited documentation centers data-governance and platform workflows rather than a security buyer. Unity Catalog is documented for the team enforcing access, lineage, and audit, and model and MCP governance is presented to that same team. The cited pages identify no dedicated AI-security persona and do not describe how a standalone defense vendor sells.
Unity Catalog models every governed asset as a securable object and applies one permission model across tables, views, volumes, functions, and registered models. Access control runs through privileges, attribute-based policies on governed tags, and row filters and column masks.
The differentiating advantage is that the governance is native, not the AI analysis itself. Because the catalog operates beneath every data interaction in the workspace, it sees and controls access across those assets. The cited pages do not evaluate competing catalogs, so how an external catalog would obtain the same metadata is not established.
The AI assets reuse the data machinery rather than adding AI-specific defense. A registered model is governed as a function object under the same privilege model, and managed and external MCP servers use Unity Catalog permissions and OAuth connections, so the advantage is uniform inventory and access control over AI assets rather than protection against attacks on the models.
Databricks presents Unity Catalog as a built-in layer of the platform, and the fetched pages show no separate sales motion for it. The buyer enables it inside an existing Databricks workspace, which may reduce adoption friction where Databricks is already deployed, an inference from the enablement model rather than a documented competitive effect.
The parent's install base is potential distribution rather than demonstrated catalog reach. Databricks reports more than 20,000 organizations and 70% of the Fortune 500 on its platform, figures that describe where the catalog could be enabled, while the cited sources disclose no Unity Catalog-specific adoption, wins, or expansion. Databricks also operates internationally, with a United Kingdom private limited company active on the public companies register since 2017.
The available proof of the motion is entirely platform-level. The company reports a $4 billion revenue run-rate, a $1 billion AI revenue run-rate, and 650+ customers over $1 million a year, parent metrics that show the platform converting data and AI workloads into expansion. Catalog-specific adoption, win rates, and expansion stay undisclosed, so those figures cannot stand in for the line's own traction.
The pricing page presents Databricks as pay-as-you-go with no up-front costs, billing only for the products used at per-second granularity, and shows no Unity Catalog-specific meter in the fetched text. Governance reads as a platform capability rather than a separately priced control on that evidence.
The model treats governance as a feature of the platform rather than a metered control. The fetched pricing page describes paying for compute and the products consumed and shows no separate governance meter in its fetched text, so the catalog reads as enforcing access beneath those workloads on platform pricing.
The positioning signal is the absence of a meter rather than a stated one. That governance carries no separate charge in the fetched text suggests Databricks bundles it into platform consumption rather than selling it as a control, an inference the cited record does not extend to measuring what the catalog monetizes or how strongly it holds a buyer in.
Databricks delivers Unity Catalog as a managed layer inside the platform enterprises already operate, so newer workspaces have no separate deployment surface to stand up while older ones need enablement or migration. The catalog turns on for a workspace and then operates beneath every data interaction automatically.
Onboarding reuses the platform the team already runs. Governed tags, ABAC policies, and lineage are configured within Databricks rather than integrated as an external system, which reads as lower operational cost than standing up an external catalog, though the cited pages measure neither and make no comparison.
The operational model is consumption that the customer controls through the platform. Databricks bills the products consumed at per-second granularity, and the fetched record discloses no Unity Catalog-specific cost curve or meter.
Databricks anchors Unity Catalog's trust position in the platform around it. The catalog ships inside Databricks and is enabled automatically for workspaces created after November 8, 2023, so a team taking governed control of models and MCP servers extends a boundary it already operates rather than vetting a new vendor.
The data-handling controls are concrete. The catalog classifies and tags sensitive data, masks columns and filters rows through ABAC and per-table policies, and records access in audit log system tables, which addresses the exposure of opening data and models to agents.
The trust gap is in how the security role is framed. A buyer evaluating the catalog as AI defense must accept that it governs and audits access rather than blocking attacks on the model, so its strongest trust story is for teams treating AI risk as access control and accountability.
The broader platform also carries an attack surface that access governance does not remove, shown by a disclosed server-side request forgery in Azure Databricks that let an unauthorized attacker elevate privileges over a network, a class of flaw the catalog was never meant to stop.
Unity Catalog is the platform play in its category. Its value rests on operating beneath every data interaction in the Databricks workspace, so the catalog strengthens the platform and the platform strengthens the catalog.
The ecosystem reach extends to AI tooling on the input side. The catalog governs registered models and brings managed and external MCP servers under its permission and OAuth model, which positions it across the agent tools enterprises are starting to deploy.
The ecosystem dependency runs toward the catalog. Immuta describes its integration as leveraging Unity Catalog foundational capabilities, with its access policies reflected as native Unity Catalog controls, so a partner adding row and column controls to the same workloads is building on the catalog rather than displacing it. That makes the ecosystem the catalog's reach and a route for partners to layer governance on the same data, not evidence that the layer can be bypassed.
Databricks was founded in 2013 by the original creators of Apache Spark, Delta Lake, MLflow, and Unity Catalog, and built the lakehouse into a platform that tens of thousands of organizations run. The leadership is proven at operationalizing data and AI infrastructure at scale.
The engineering throughput is visible in how governance extends across asset types. The catalog carried its securable-object model from tables to registered models, and Databricks added a separate control plane, Unity AI Gateway, that governs MCP servers while relying on catalog permissions and connections, which shows the organization can bring new AI asset types into governance.
The credibility behind Unity Catalog is the demonstrated ability to build and run governance at lakehouse scale. The cited sources describe the platform's data and AI origins and its MCP governance rather than surveying the team's security expertise, so they establish no research record in attacks on models or agents either way.
| Id | Source | Tier | Accessed |
|---|---|---|---|
| s1 | Databricks Unity Catalog product page “Unified and open governance for data and AI. Eliminate silos, simplify governance and accelerate insights at scale.” | official | 2026-06-14 |
| s2 | What is Unity Catalog documentation “When enabled for a workspace, Unity Catalog operates beneath every data interaction in your workspaces automatically, enforcing access control when you query a table, tracking lineage as data moves, and logging activity for auditing. Every asset you govern is modeled as a securable object.” | official | 2026-06-14 |
| s3 | Manage model lifecycle in Unity Catalog documentation “In Unity Catalog, registered models are a subtype of the FUNCTION securable object. Models in Unity Catalog extends the benefits of Unity Catalog to ML models, including centralized access control, auditing, lineage, and model discovery across workspaces.” | official | 2026-06-14 |
| s4 | Access control in Unity Catalog documentation “Databricks recommends using attribute-based access control (ABAC) to centralize and scale access control based on governed tags. Use row filters and column masks only when you need per-table logic or haven't adopted ABAC yet.” | official | 2026-06-14 |
| s5 | Model Context Protocol on Databricks documentation “On Databricks, MCP servers are governed through Unity AI Gateway, the enterprise control plane for access across MCP servers and LLM endpoints. Managed and external MCP servers use Unity Catalog permissions to control access, and external servers use Unity Catalog connections with OAuth.” | official | 2026-06-18 |
| s6 | About Databricks “Databricks was founded in 2013 by the original creators of the lakehouse architecture and open source projects Apache Spark, Delta Lake, MLflow and Unity Catalog. Today, more than 20,000 organizations worldwide and 70% of the Fortune 500 rely on the Databricks Data Intelligence Platform.” | official | 2026-06-14 |
| s7 | Databricks: Surpasses $4B Revenue Run-Rate, Exceeding $1B AI Revenue Run-Rate “Databricks has crossed a $4 billion revenue run-rate, growing over 50% year over year. Its AI products recently crossed a $1 billion revenue run-rate. The company is closing its Series K, raising $1 billion at over $100 billion. 650+ customers consuming at over $1 million annual run-rate.” | press | 2026-06-14 |
| s8 | CNBC: Databricks raises capital at $134 billion valuation “Databricks said it is raising $4 billion at a $134 billion valuation, a 34% jump from the August round that valued it at $100 billion. It topped a $4.8 billion revenue run-rate during its fiscal third quarter, growing 55% year-over-year, up from the $4 billion run-rate announced earlier this year.” | press | 2026-06-18 |
| s9 | Databricks homepage “The Databricks Platform. Unify your data, analytics and AI. Use it to power agents, apps and natural language insights. Databricks is a data and AI company founded in 2013, headquartered in San Francisco.” | official | 2026-06-18 |
| s10 | Immuta: Inside Immuta's Databricks Unity Catalog Integration with Row and Column-Level Controls “Immuta continues to innovate in partnership with Databricks, and we're excited to announce the general availability of Immuta's native integration with Databricks Unity Catalog to secure workloads on the Databricks Lakehouse Platform.” | press | 2026-06-14 |
| s11 | arXiv: Retrieval Capabilities of Large Language Models Scale with Pretraining FLOPs (Databricks Mosaic Research) “Joint affiliation with Databricks Mosaic Research and MIT.” | research | 2026-06-30 |
| s12 | CVE-2024-49194: Databricks JDBC Driver remote code execution “Databricks JDBC Driver 2.x before 2.6.40 could potentially allow remote code execution (RCE) by triggering a JNDI injection via a JDBC URL parameter.” | other | 2026-06-30 |
| s13 | NVD CVE-2026-33107: Azure Databricks server-side request forgery “Server-side request forgery (ssrf) in Azure Databricks allows an unauthorized attacker to elevate privileges over a network.” | other | 2026-06-30 |
| s14 | UK Companies House: Databricks U.K. Limited (10919206) “Company status Active Company type Private limited Company Incorporated on 16 August 2017.” | regulatory | 2026-06-30 |
| Id | Source | Tier | Accessed |
|---|---|---|---|
| s1 | Databricks Unity Catalog product page “Unified and open governance for data and AI. Eliminate silos, simplify governance and accelerate insights at scale.” | official | 2026-06-14 |
| s2 | What is Unity Catalog documentation “When enabled for a workspace, Unity Catalog operates beneath every data interaction in your workspaces automatically, enforcing access control when you query a table, tracking lineage as data moves, and logging activity for auditing. Every asset you govern is modeled as a securable object.” | official | 2026-06-14 |
| s3 | Manage model lifecycle in Unity Catalog documentation “In Unity Catalog, registered models are a subtype of the FUNCTION securable object. Models in Unity Catalog extends the benefits of Unity Catalog to ML models, including centralized access control, auditing, lineage, and model discovery across workspaces.” | official | 2026-06-14 |
| s4 | Access control in Unity Catalog documentation “Access control in Unity Catalog is built on privileges and ownership, attribute-based policies (ABAC), and table-level row and column filters. Databricks recommends using attribute-based access control (ABAC) to centralize and scale access control based on governed tags.” | official | 2026-06-14 |
| s5 | Model Context Protocol on Databricks documentation “Managed and external MCP servers use Unity Catalog permissions to control which users and service principals can access each server and its underlying data. External MCP servers use Unity Catalog connections with managed OAuth to securely handle authentication.” | official | 2026-06-14 |
| s6 | About Databricks “Databricks was founded in 2013 by the original creators of the lakehouse architecture and open source projects Apache Spark, Delta Lake, MLflow and Unity Catalog. Today, more than 20,000 organizations worldwide and 70% of the Fortune 500 rely on the Databricks Data Intelligence Platform.” | official | 2026-06-18 |
| s7 | Databricks: Surpasses $4B Revenue Run-Rate, Exceeding $1B AI Revenue Run-Rate “Databricks has crossed a $4 billion revenue run-rate, growing over 50% year over year. Its AI products recently crossed a $1 billion revenue run-rate. The company is closing its Series K, raising $1 billion at over $100 billion. 650+ customers consuming at over $1 million annual run-rate.” | press | 2026-06-14 |
| s8 | Databricks pricing “Databricks offers you a pay-as-you-go approach with no up-front costs. Only pay for the products you use at per second granularity.” | official | 2026-06-14 |
| s9 | Databricks homepage “The Databricks Platform. Unify your data, analytics and AI. Use it to power agents, apps and natural language insights.” | official | 2026-06-18 |
| s10 | Immuta: Inside Immuta's Databricks Unity Catalog Integration with Row and Column-Level Controls “Immuta continues to innovate in partnership with Databricks, and we're excited to announce the general availability of Immuta's native integration with Databricks Unity Catalog to secure workloads on the Databricks Lakehouse Platform.” | press | 2026-06-14 |
| s11 | Unity Catalog privileges reference documentation “Allows a user to create an MLflow registered model (which is a type of function) in a schema on which CREATE MODEL is granted. The user must also have the USE CATALOG privilege on the parent catalog and USE SCHEMA on the parent schema.” | official | 2026-06-15 |
| s12 | NVD CVE-2026-33107: Azure Databricks server-side request forgery “Server-side request forgery (ssrf) in Azure Databricks allows an unauthorized attacker to elevate privileges over a network.” | other | 2026-06-30 |
| s13 | UK Companies House: Databricks U.K. Limited (10919206) “Company status Active Company type Private limited Company Incorporated on 16 August 2017.” | regulatory | 2026-06-30 |
This site is an experimental research aid created by Zeltser Security Corp. All its data gathering and analysis was performed autonomously without human review, and it can contain errors of fact, interpretation, and judgment that a human reviewer might catch.
The analyses are statements of opinion, not statements of fact. Machine analysis produced the scores, summaries, and matrix placements by weighing the public sources each page cites, and reasonable people can weigh the same sources differently. Where a page states a fact, it cites the public source and the date it was checked, and the statement is only as accurate as that source. Unless a profile expressly says otherwise, the analysis involves no hands-on testing and no independent validation of any company's products or services.
Nothing here is professional, security, legal, financial, investment, or purchasing advice, and nothing here is a recommendation to invest in, do business with, or avoid any company. Inclusion of a company is not an endorsement, and absence of a company is not a judgment about it. Reading this site creates no advisory or client relationship. Verify any detail you plan to act on against the vendor's current materials.
The content is provided "as is" and "as available," with all warranties disclaimed, express or implied, including merchantability, fitness for a particular purpose, accuracy, and non-infringement. No entry is warranted to be complete, current, or correct. Companies change, vendors update their claims, sources can be wrong, and automated analysis can misread them.
To the fullest extent permitted by law, the operator, Zeltser Security Corp, is not liable for any damages that arise from using this site or relying on its content, including direct, indirect, incidental, special, and consequential damages and lost profits, even if advised that such damages were possible. If you are dissatisfied with the site or disagree with these terms, your remedy is to stop using it.
Entries link to vendor pages, press coverage, and other external sites that Zeltser Security Corp does not control and is not responsible for. A link is not an affiliation with the destination or an endorsement of it. Product and company names and trademarks are the property of their owners, used here nominatively to identify the companies described. Short quotations from cited sources appear for identification and commentary.
Use, quotation, automated retrieval, and redistribution of the content are governed by the Terms of Use at cybercompanyprofiles.com/terms, which permit personal and internal business use with attribution and prohibit republication and resale.