組織が自信を持ってAIを管理、監視、スケールできるよう設計された、EnzaiのAIガバナンス製品のフルスイートをご覧ください。構造化されたインテークや一元化されたAIインベントリから、自動化されたアセスメントやリアルタイムの監視まで、Enzaiはイノベーションを遅らせることなく、日々のAIワークフローにガバナンスを直接組み込むためのビルディングブロックを提供します。

Enzai

AIガバナンス・プラットフォーム

データに基づいた比較

AIガバナンス・プラットフォーム

データに基づいた比較

Enzai Collibra

Collibra is a data catalog company, and AI Command Center governs AI from that foundation. Enzai does nothing but AI governance: six regulatory regimes written by lawyers, five discovery methods, and agent actions blocked before they run.

Enzaiと他の製品との違いは何でしょうか。Collibra何かご不明な点がございますか?

Collibra built the enterprise data catalog. Lineage, metadata, stewardship, business glossary, data quality. AI Command Center sits on that foundation, and its strongest claim is traceability from source dataset through model training, inference and deployment. In data lineage Collibra is very hard to beat.

Enzai does one thing. Finding AI systems, running them through intake, assessing them against compliance frameworks, and keeping agents inside the boundaries set for them. It was founded by lawyers who practiced in this area, and it ships configured, so a compliance officer runs it without learning a platform first.

The gap is what each one treats as the thing being governed. A catalog exists to describe assets and where they came from. A regulator asks a different question: what is this system used for, who is accountable, which obligations attach, and what stops it doing something it should not.

技術的な仕組み

機能ごとの詳細比較

技術的な仕組み

機能ごとの詳細比較

EnzaiとCollibra機能面においてどのように比較されますか?

Enzaiと競合他社のガバナンス機能の比較
CapabilityEnzaiCollibra
規制動向の先読みSix regimes ready on day one: EU AI Act, ISO 42001, NIST AI RMF, GDPR, Colorado SB 26-189 and Singapore AI Verify. Written and updated by qualified lawyers, so a change in the law is a library update rather than a project.Two published regulatory templates: EU AI Act and NIST AI RMF, alongside AI UC-1 for agentic assessments and custom frameworks you author. No ISO 42001, no GDPR, no US state regimes.
ポリシー・ライフサイクル管理Built around the AI system: purpose, owner, risk tier, autonomy level and the obligations that attach to it under each regime in scope.Built on the data catalog. The native objects are datasets, lineage and metadata, with AI use cases, models and agents registered against them. Regulators write about the use a system is put to, not where its training data came from.
リスクとコントロールのマッピングFive discovery methods run in parallel, covering sanctioned systems and shadow AI, including AI shipped inside tools nobody bought as AI and never registered by anyone.Collibra does not publish shadow AI discovery. AI enters the registry when someone registers it, either through an intake form or a developer CLI that generates manifests from code. Undeclared AI stays undeclared.
モデルインベントリのガバナンスOne register for systems, models, datasets, vendors and agents, with owner, risk tier and use case attached from the moment an entry lands.A unified registry for AI use cases, models and agents, platform-agnostic across AWS, Azure, Databricks, Google, SAP and MLflow. Strong coverage of registered assets in ML platforms.
監査に対応可能なエビデンス・トレイルRequests auto-tier on submission and route only to the reviewers that tier needs. Ships configured, and every step is editable when a business unit needs its own rules.Policy-driven governance with configurable compliance workflows and stakeholder routing, built in the platform. The tiering and thresholds are yours to design and yours to maintain.
部門横断的なワークフローEvidence captured once and reused across all six frameworks and every audit in scope.Lineage and evidence management for audits, across the two published templates. Evidence gathered for one regime does not carry to a regime Collibra has not shipped.
継続的なコンプライアンス監視A compliance score per system, per framework, recalculated as evidence lands. You can answer where a system stands against the EU AI Act today, separately from where it stands against ISO 42001.AI Trust Score, described by Collibra as one universal signal per system, aggregating documentation, data integrity, lifecycle status and regulatory signals into a single metric. One number, not a position per framework.
取締役会レベルのガバナンス報告Enforcement at the action layer, on any stack, in production today. Unsanctioned agent tool calls blocked at the boundary before they execute, autonomy tiered per agent, recursion capped across handoffs.Operational Trust for AI Agents turns production signals into a Trust Score and flags trust degradation. Collibra publishes it as available in preview with Databricks Agent Bricks, the first of its planned platform integrations. Scoring an agent is not stopping one.

The discovery row is the one to test rather than read. Collibra's registry fills when somebody registers something, through an intake form or a developer CLI. That works for the AI your governance team already knows about, which is not the AI a regulator will ask about. We wrote up how that usually goes in our guide to shadow AI discovery.

Their data lineage is deeper than ours, which counts if what you need is provenance from source dataset to model output.

戦略的意義

Enzaiが選ばれる理由

Enzaiがより適しているケース

Collibra traces the data your AI runs on. Enzai governs the AI system itself, and the obligations that attach to it.

A register only holds what someone registered

Collibra fills its AI registry two ways: an intake form, or a developer CLI that generates manifests from code. Both require somebody to declare the system. The AI that creates regulatory exposure is the AI nobody declared, and a catalog has no way to see it.

Five methods, running in parallel

Shadow AI without the blame

Bought AI counts too

顧客サポートチケット分類

ドラフトユースケース

5 つのリクエストされた AI ソリューション

リクエスト日: 2026年11月7日

リクエストされた者: Enzai

レビュアー:

自動契約リスクレビュー

ドラフトユースケース

5 つのリクエストされた AI ソリューション

リクエスト日: 2026年7月7日

リクエストされた者: Enzai

レビュアー:

販売予測と需要予測

ドラフトユースケース

5 つのリクエストされた AI ソリューション

リクエスト日: 2026年8月18日

リクエストされた者: Enzai

レビュアー:

従業員履歴書選別アシスタント

ドラフトユースケース

5 つのリクエストされた AI ソリューション

リクエスト日: 2026年6月19日

リクエストされた者: Enzai

レビュアー:

マイクロソフト

表示

3製品

One number is not a compliance position

Collibra describes its AI Trust Score as one universal signal per system, aggregating documentation, data integrity, lifecycle status and regulatory signals into a single metric. That is useful for a dashboard. It is not what an auditor asks for.

A score per framework

Recalculated as evidence lands

Traceable to the obligation

What keeps a program running once the inventory exists

Discovery and agent control get a program started. What keeps it going is more ordinary. Regulations move. The business has to route AI through governance rather than around it. Evidence has to appear without a fire drill. Enzai covers the EU AI Act, ISO 42001, NIST AI RMF, GDPR, the US state regimes and Singapore AI Verify, and ships configured, so the first program runs on defaults rather than a build project.

1

Live compliance posture

A score per system, per framework, recalculated as evidence lands. You can see where a system stands today without running an assessment cycle first.

1

Live compliance posture

A score per system, per framework, recalculated as evidence lands. You can see where a system stands today without running an assessment cycle first.

2

Risk-calibrated intake

Requests are tiered on submission by use case, data sensitivity and autonomy, then routed only to the reviewers that tier needs. Low-risk use cases stop queueing behind high-risk ones.

2

Risk-calibrated intake

Requests are tiered on submission by use case, data sensitivity and autonomy, then routed only to the reviewers that tier needs. Low-risk use cases stop queueing behind high-risk ones.

3

Evidence captured once

One assessment covers several frameworks. The trail is available on demand for an assessor, auditor or regulator.

3

Evidence captured once

One assessment covers several frameworks. The trail is available on demand for an assessor, auditor or regulator.

戦略的意義

Enzaiが選ばれる理由

Enzaiがより適しているケース

Collibra traces the data your AI runs on. Enzai governs the AI system itself, and the obligations that attach to it.

A register only holds what someone registered

Collibra fills its AI registry two ways: an intake form, or a developer CLI that generates manifests from code. Both require somebody to declare the system. The AI that creates regulatory exposure is the AI nobody declared, and a catalog has no way to see it.

Five methods, running in parallel

Shadow AI without the blame

Bought AI counts too

顧客サポートチケット分類

ドラフトユースケース

5 つのリクエストされた AI ソリューション

リクエスト日: 2026年11月7日

リクエストされた者: Enzai

レビュアー:

自動契約リスクレビュー

ドラフトユースケース

5 つのリクエストされた AI ソリューション

リクエスト日: 2026年7月7日

リクエストされた者: Enzai

レビュアー:

販売予測と需要予測

ドラフトユースケース

5 つのリクエストされた AI ソリューション

リクエスト日: 2026年8月18日

リクエストされた者: Enzai

レビュアー:

従業員履歴書選別アシスタント

ドラフトユースケース

5 つのリクエストされた AI ソリューション

リクエスト日: 2026年6月19日

リクエストされた者: Enzai

レビュアー:

マイクロソフト

表示

3製品

One number is not a compliance position

Collibra describes its AI Trust Score as one universal signal per system, aggregating documentation, data integrity, lifecycle status and regulatory signals into a single metric. That is useful for a dashboard. It is not what an auditor asks for.

A score per framework

Recalculated as evidence lands

Traceable to the obligation

What keeps a program running once the inventory exists

Discovery and agent control get a program started. What keeps it going is more ordinary. Regulations move. The business has to route AI through governance rather than around it. Evidence has to appear without a fire drill. Enzai covers the EU AI Act, ISO 42001, NIST AI RMF, GDPR, the US state regimes and Singapore AI Verify, and ships configured, so the first program runs on defaults rather than a build project.

1

Live compliance posture

A score per system, per framework, recalculated as evidence lands. You can see where a system stands today without running an assessment cycle first.

2

Risk-calibrated intake

Requests are tiered on submission by use case, data sensitivity and autonomy, then routed only to the reviewers that tier needs. Low-risk use cases stop queueing behind high-risk ones.

3

Evidence captured once

One assessment covers several frameworks. The trail is available on demand for an assessor, auditor or regulator.

信頼できるAIガバナンスを構築する準備はできていますか?

Enzaiがどのようにして、AIのインベントリ、リスク、コンプライアンス、およびエビデンスを管理する単一のオペレーティングレイヤーをチームに提供するのかをご覧ください。

AIガバナンスの承認ワークフローを表現した、温かみのあるフロストガラス(曇りガラス)調のインターフェース・ビジュアル。

AIガバナンス・レビュー

審査中

レビュー完了

0%

証拠を収集済み

コントロールをマッピング済み

レビュー準備完了

コントロールのマッピングが完了しました

0 / 8

承認待ちキュー

法務

準備完了

リスク

確認中

公正な評価

代替ソリューションが最適となるケース

公正な評価

代替ソリューションが最適となるケース

〜の際、Collibraのほうが、より優れた選択肢です。

Your requirement is data lineage, not AI governance

If the question in front of you is which datasets trained which model and where that output traveled, Collibra has been building exactly that for years and does it better than we do. That is a data provenance problem with AI attached rather than an AI governance problem.

You are all-in on Databricks

Collibra is Databricks' Governance Partner of the Year with bi-directional Unity Catalog integration, and its agent trust scoring previews there first. Where Databricks is the whole estate, that integration is real and it is ahead of everyone else.

ビジネスの現実

コスト、スコープ、および価値

ビジネスの現実

コスト、スコープ、および価値

Enzaiの最適な対象読者

You answer to more than the EU AI Act and NIST

Collibra publishes templates for two regulations. Programs get difficult at the third and fourth regime, when ISO 42001 certification comes into scope because a customer asked for it, when GDPR interacts with the AI Act, when Colorado SB 26-189 lands in January 2027, or when a Singapore deployment brings AI Verify into scope. Enzai ships all of those and reuses the same evidence base across them.

Most of your AI estate was bought, not built

A registry filled by intake forms and code manifests assumes AI arrives through engineering. Most of it does not. It arrives through procurement, inside SaaS tools, switched on by a business unit that never filed anything. Enzai finds it and governs it on the same footing as anything built in house.

Agents are acting, not just being scored

Enzai enforces at the action layer on any stack, in production today: unsanctioned tool calls blocked before they execute, autonomy tiered per agent, recursion capped across handoffs, every blocked attempt logged against the agent and its owner. We set out what agentic AI governance has to cover.

The program has to survive contact with the business

Governance tools usually fail on adoption, not capability. Enzai ships with the workflows already configured, so a first program runs on what comes out of the box. Underneath that, forms, approval routing, risk templates and framework logic are all configurable, so a global estate with a dozen business units and conflicting sign-off rules bends the platform to fit instead of the other way round. Enzai includes unlimited users at every tier, and has held ISO 27001 since 2023, audited annually by NQA.

移行へのステップ

移行に関する留意事項

移行へのステップ

移行に関する留意事項

~からの移行Collibra

Most organizations moving AI governance off Collibra keep Collibra. The catalog, lineage, data quality and glossary all stay where they are, because that is what it was built for. One workload moves.

The work is exporting the AI use case, model and agent registry and rebuilding assessments against the frameworks in scope. Expect the register to grow substantially on arrival, because discovery finds the AI that was never registered, and in most estates that is the larger half. Data lineage stays in Collibra and links across, so you are not choosing between provenance and governance. SSO through Microsoft Entra and SCIM provisioning keep access tied to the identity groups you already maintain.

One thing worth asking every vendor on your list (including us) is what an export actually contains. Assessment history and supporting evidence, or only the current state of each record? Our compliance frameworks library covers what a register needs to hold.

研究の根拠

情報源と検証

研究の根拠

情報源と検証

情報源と検証

本比較における主張を裏付ける、参考文献および検証日の一覧です。

Claims about Collibra come from Collibra's published product pages and documentation on the dates above. Where this page says Collibra does not publish something, that means it was not documented publicly on that date, not that it does not exist. Preview and roadmap items move, so anything described here as in preview should be checked with Collibra directly. Neither company publishes list pricing and this page makes no claim about either. Corrections welcome.

最終更新日:

競合他社情報の最終確認日:

安全なAIガバナンスの情報フローを表現した、抽象的な琥珀色のガラスのテクスチャ。

確実なAIガバナンスの意思決定を導く、明確な解答を。

Collibra比較に関するよくある質問

Collibra does not publish shadow AI discovery. Its AI registry is populated when someone registers a system, either through an intake form or through a developer CLI that generates manifests from code and syncs them into the registry. Both routes require the AI to be declared by someone who knows it exists. Enzai runs five discovery methods in parallel inside the core platform, covering sanctioned systems and shadow AI, including AI shipped inside tools that were never bought as AI.

Collibra publishes assessment templates for the EU AI Act and NIST AI RMF, alongside AI UC-1 templates for agentic assessments and custom frameworks you author yourself. ISO 42001, GDPR and the US state AI regimes are not among the published templates. Enzai ships six regimes ready to use, maintained in house by qualified lawyers, with evidence captured once and reused across all of them.

Collibra's Operational Trust for AI Agents converts live production signals into a dynamic AI Trust Score per agent and flags trust degradation. As at August 2026 Collibra publishes it as available in preview with Databricks Agent Bricks, described as the first of its planned platform integrations. Enzai enforces at the action layer across any stack in production today: unsanctioned tool calls are blocked at the boundary before they execute, each agent is tiered by permitted autonomy, and recursion is capped across agent-to-agent handoffs.

The market splits in two. Enterprise platforms extending existing infrastructure into AI, including Collibra, IBM watsonx.governance, ServiceNow and OneTrust, suit organizations already running the underlying system. Dedicated AI governance platforms, including Enzai, are built only for AI and go deeper on regulatory coverage, discovery and agent enforcement. The deciding question is usually whether AI governance is a workflow you are adding to a platform you already own or the problem you are solving.

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AI

AI

インフラストラクチャ

インフラストラクチャ

信頼を構築するために設計されています。

信頼を構築するために設計されています。

組織がAIを採用し、管理し、監視する能力を、企業レベルの信頼性で強化します。規模で運営する規制対象の組織向けに構築されています。

既存のシステム、ポリシー、AIワークフローを、すべて1つの統合プラットフォームでシームレスに接続します。

既存のシステム、ポリシー、AIワークフローを、すべて1つの統合プラットフォームでシームレスに接続します。