---
title: "Enterprise Context Graph | Clearly AI"
description: "Map applications, vendors, data, AI systems, and people, and how they connect. Clearly AI builds the graph from your reviews so evidence carries into the next one."
canonical: https://clearly-ai.com/platform/enterprise-context-graph
---

_Enterprise context graph_

# Understand the system before the review starts

The enterprise context graph maps your applications, vendors, data, AI systems, and people, plus any categories you define, and shows how they connect. Clearly AI builds it from the reviews you already run and suggests updates as your knowledge base and projects change, so the evidence behind one review carries into the next.

- [Contact Enterprise Sales](https://clearly-ai.com/demo)
- [Read the docs](https://docs.clearly-ai.com/docs/context-graph)

- HID Global
- Rivian
- Ericsson
- PayPal
- Modern Health
- Hagerty
- Bastion
- Infineon

## Why does every review start from scratch?

A new feature uses the same payments service you reviewed last quarter. It sends data to a vendor your third-party risk team already assessed, and that vendor relies on a subprocessor your privacy team flagged. None of that reaches the next review. The inventory sits in a spreadsheet, the vendor record in another system, and the data flows in someone's head.

So the security engineer reconstructs the system by hand. The business owner uploads the same SOC 2 report and answers the same questions again. And no one can say which products are exposed when a vendor's risk changes.

_Context graph: a CMDB record, a ServiceNow ticket and two completed reviews feed four accepted nodes. The project Acme Payments API links to two of them and inherits 3 documents, and a completed review proposes 2 new relationships._

## How the context graph works

1. Start from the work you've already done:
    Clearly AI reads your CMDB, project descriptions and documents, and completed reviews, plus historical tickets (from ServiceNow, for example) where your team already accepted guidance. It then proposes the applications, vendors, data assets, data stores, features, processing activities, and AI systems they describe, with the relationships between them. You don't build the graph from a blank canvas.

2. Approve what's right:
    Every suggestion shows the passage that supports it and a confidence level. Accept it, reject it, or turn on auto-accept above the confidence level you choose. Rejected suggestions aren't proposed again.

3. Link it to your reviews:
    Connect a project to the systems it touches, or let Clearly AI suggest them. Documents attached to a linked vendor, application, or data store carry into the review automatically, and you can see every source a review will read before it runs.

4. Keep it current as you work:
    New reviews and knowledge base updates surface new nodes and relationships for approval. When the evidence behind a relationship changes, Clearly AI prompts a refresh, and relationships the evidence no longer supports are closed with their history kept.

## See how everything connects

An inventory tells you what you have. The context graph shows you how it fits together.

- **Third-party and nth-party exposure:** See which applications depend on a vendor, and which vendors that vendor depends on. When a vendor's risk changes, you know which products are in scope.
- **Data flows between systems:** Trace which data assets move between applications, data stores, vendors, and AI systems, and which processing activities use them.
- **Ownership on every node:** Each system has named owners, so findings and follow-up questions go to the right person.
- **A map you can explore:** Open the full map, filter by type or severity, or zoom into one system and its neighbors one or two hops out.

Context graph map focused on the partner-portal application, two hops out. partner-portal stores data in partner-accounts-db, shares data with the vendor Brightwave Analytics, and signs in with keystone-identity. partner-accounts-db is hosted on Cirrus Cloud Hosting, a proposed relationship, and Brightwave Analytics uses Cirrus Cloud Hosting as a subprocessor.

_The Clearly AI context graph map showing an application, its vendor, the vendor's subprocessor, and a restricted data store._

- [See how Clearly AI assesses vendor and nth-party risk](https://clearly-ai.com/solutions/third-party-risk)

## Upload the evidence once. Use it in every review.

Attach a vendor's SOC 2 report, an architecture doc, or a data processing agreement to the node it describes. Reviews of connected projects inherit it, so the business isn't asked for the same evidence twice, and your team doesn't re-answer what's already documented.

- **You decide what carries context:** Admins choose which relationship types bring documents into a review, and how far: one hop or two.
- **Every review shows its sources:** Confirm the sources a review will read before it runs. After it completes, the review keeps a fixed record of everything it used, so the findings stay traceable in an audit.
- **Reviews add to the graph:** When a review surfaces a new dependency or data flow, it's proposed back to the graph for approval.

- [See how Clearly AI keeps every finding traceable to its source](https://clearly-ai.com/platform/quality)

## AI suggestions you can trust and trace

The graph only helps if your team trusts what's in it. Every suggestion is built to be checked.

- **Sources on every suggestion:** Each proposed node, relationship, or attribute shows the document or review passage behind it.
- **Confidence you can read:** High means explicit or supported by multiple sources. Medium means one document or ambiguous wording. Low means a passing mention. Suggestions based only on AI-generated review answers are capped at Medium and are never auto-accepted.
- **Fills gaps, never overwrites:** Clearly AI fills in blank attributes only. Values your team entered stay as they are.
- **Changes you can undo:** Auto-accepted changes are flagged in each node's activity log and can be undone for seven days.

## Different policies for different parts of the business

Large organizations rarely run on one rulebook. A medical device line, a consumer app, and an internal platform can each have their own policies, controls, and risk thresholds.

Create a knowledge base for a specific product, application, or business unit. Reviews of that product run against its own policies and controls, alongside the standards that apply company-wide.

## Built around your review process

The context graph ships with seven node types: applications, vendors, data assets, data stores, features, processing activities, and AI systems. Admins can rename them, add new types, and define up to 64 attributes per type, so the graph matches how your company already describes its systems.

The graph replaces Clearly AI inventories. Everything you tracked in inventory is still there, now connected.

## Coverage that builds on itself

- **~90%** Reduction in time per review
- **4x** Review capacity with the same headcount
- **From 10 to 100%** Product coverage

> “As soon as we started using Clearly AI, we saw an immediate reduction in the amount of time that not only my team was spending on the reviews, but that the business owners were actually spending having to give us the answers for the assessments.”
>
> — Ashley Haynes, Senior Director of Privacy, Trust and AI Governance, Ericsson Enterprise Wireless Solutions

[Read the Ericsson story](https://clearly-ai.com/customer-stories/ericsson)

_FAQ_

## Answers to your top questions

### What is an enterprise context graph?

An enterprise context graph maps the systems, vendors, data, AI systems, people, and custom categories in your organization as nodes, and links them through relationships like “depends on” or “processes data from.” In Clearly AI, the graph lets security, privacy, and risk teams reuse shared evidence across reviews instead of gathering it again for every project.

### How is the context graph different from an asset inventory?

An inventory lists what you have. The context graph also records how those things connect, including vendor dependencies, nth-party relationships, and data flows between systems. Clearly AI uses those connections to bring the right evidence into each review.

### How does the context graph compare to our CMDB?

It builds on your CMDB, which stays your system of record. Clearly AI uses the data in your CMDB, along with historical ServiceNow tickets where your team has already accepted guidance, to build the graph. Then it adds what a CMDB doesn't hold for a security review: vendor and nth-party dependencies, data flows, the evidence behind each system, and the review history that links them.

### Does Clearly AI do data mapping for privacy?

Yes. Data assets, data stores, and processing activities are built-in node types, so your privacy team can track what personal data you hold, where it's stored, which systems and vendors it moves between, and which processing activities use it. The same graph feeds your [privacy impact assessments](https://clearly-ai.com/solutions/privacy), so the data map and the PIA stay in step.

### Does the context graph replace Clearly AI inventories?

Yes. When the context graph is enabled, it replaces Inventory in Clearly AI. Your existing items stay available as nodes, with relationships between them.

### How does Clearly AI build the graph?

Clearly AI analyzes CMDB, project descriptions and documents, completed reviews, and historical tickets where your team accepted guidance, then proposes nodes, relationships, and missing attribute values. Each suggestion shows its source and a confidence level, and waits for approval unless you turn on auto-accept for high- or medium-confidence suggestions. Suggestions from the first discovery run always need manual approval.

### Can Clearly AI map nth-party vendors?

Yes. Vendors are nodes in the graph, so you can record that one vendor depends on another and see every application connected to either. When a vendor's risk changes, you can see which products and data are affected. [Learn more about third-party risk reviews](https://clearly-ai.com/solutions/third-party-risk).

### How does the graph show up in a review?

When a project is linked to a node, documents attached to that node can carry into the project's reviews. Admins decide which relationship types carry context and how many hops they reach. Before a review runs, you can confirm every source it will read, and after it completes, the review keeps a record of the sources it used.

### Can different business units use different policies?

Yes. You can create knowledge bases for a specific product, application, or business unit, so reviews of that product run against its own policies and controls alongside your company-wide standards.

### Is the context graph a continuous monitoring tool?

No. Clearly AI doesn't watch your systems or dashboards. The graph is updated from the reviews you run and the documents in your knowledge base. When that material changes, Clearly AI proposes updates for your team to approve.

### Who can edit the context graph?

Everyone in your organization can view the graph. Owners and admins create nodes and manage types, and members manage the nodes they own. Vendor portal users can't access the graph.

## See the graph behind every review

We'll show how Clearly AI builds a graph from completed reviews, and how one vendor's evidence carries into the next review.

- [Contact Enterprise Sales](https://clearly-ai.com/demo)
- [See integrations](https://clearly-ai.com/integrations)

## Learn more

- [Secure everything that ships](https://clearly-ai.com)
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- [Understand the system before the review starts](https://clearly-ai.com/platform/enterprise-context-graph)
- [Trust, security, and deployment](https://clearly-ai.com/platform/trust-security-and-deployment)
- [Every plan reviewed. Every risky change threat modeled.](https://clearly-ai.com/platform/ide-and-mcp)
- [Verify what actually shipped](https://clearly-ai.com/platform/control-and-remediation-verification)
- [A current threat model for everything that ships](https://clearly-ai.com/solutions/continuous-threat-modeling)
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- [Clearly AI Privacy Policy](https://clearly-ai.com/trust/privacy-policy)
- [Clearly AI Subprocessors](https://clearly-ai.com/trust/subprocessors)
- [AI Risk Reviews](https://clearly-ai.com/use-cases/ai-risk-reviews)
- [Automate Triage](https://clearly-ai.com/use-cases/automate-triage)
- [Privacy Impact Assessments](https://clearly-ai.com/use-cases/privacy-impact)
- [Vendor Risk Assessments](https://clearly-ai.com/use-cases/vendor-risk)
- [Threat Modeling](https://clearly-ai.com/use-cases/threat-modeling)
- [Contact](https://clearly-ai.com/contact)
- [Contact Enterprise Sales](https://clearly-ai.com/demo)
- [Latest from the Clearly AI blog](https://clearly-ai.com/blog)
- [In the news](https://clearly-ai.com/press)
- [How Ericsson Enterprise Wireless Solutions cut review times by up to 80%](https://clearly-ai.com/customer-stories/ericsson)
- [How HID Global reviews 300+ products for the Radio Equipment Directive with the team it already has](https://clearly-ai.com/customer-stories/hid-global)
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- [Skip the Marketing: Clearly AI for Technical People](https://clearly-ai.com/blog/clearly-ai-technical-explanation)
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- [Why We Started Clearly AI](https://clearly-ai.com/blog/why-we-started-clearly-ai)
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- [How to Automate Security and Privacy Reviews with Clearly AI](https://clearly-ai.com/blog/how-to-automate-security-and-privacy-reviews-with-clearly-ai)
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[Canonical page](https://clearly-ai.com/platform/enterprise-context-graph)