> For the complete documentation index, see [llms.txt](https://cortex-docs.paloaltonetworks.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://cortex-docs.paloaltonetworks.com/data-security-documentation/advanced-tools/graph-search.md).
# Graph search
Graph Search provides an interactive, visual way to explore your assets and findings by their relationships. Instead of working through raw data alone, you get a graphical view of how assets, security findings, and their context connect, making it easier to spot attack paths and hidden risks and act on them faster.
{% hint style="info" icon="note-sticky" %}
**Note:** For more information, see the [Cortex Cloud Graph Search documentation.](/cortex-cloud-runtime-security/graph-search/what-is-graph-search.md)
{% endhint %}
The following sections provide examples of how you can use Graph Search across the main capabilities of Cortex Data Security.
## Data Security Posture Management (DSPM)
DSPM searches help identify where sensitive data resides and the risks associated with it, such as public exposure or the presence of malware on sensitive assets. See the following examples as starting points for building your queries:
AWS Buckets Containing Sensitive Data
Surfaces AWS storage buckets that have a data finding for sensitive content, helping you locate where regulated or confidential data resides across your AWS estate so you can prioritize protection.
```
FIND Bucket WHERE Provider = AWS
THAT Has Data Finding WHERE Finding Name = Sensitive data found
```
GCP Buckets Containing Sensitive Data
Surfaces GCP storage buckets that have a data finding for sensitive content, giving you the same sensitive-data visibility across Google Cloud. Change the `Provider` value to pivot the same search to other cloud storage assets.
```
FIND Bucket WHERE Provider = GCP
THAT Has Data Finding WHERE Finding Name = Sensitive data found
```
## AI Security Posture Management (AISPM)
AISPM helps organizations monitor their AI footprint and the security posture of AI assets, ensuring models are accounted for and protected from common LLM vulnerabilities. See the following examples as starting points for building your queries:
AI Models Deployed on Vulnerable Virtual Machines
Traces the deployment path of your AI models to the underlying compute, surfacing models that run on virtual machines carrying vulnerability findings. This highlights AI workloads exposed to infrastructure-level risk, so you can prioritize patching the hosts that put your models at risk.
```
FIND AI Model
THAT Deployed On Virtual Machine
THAT Has Vulnerability Finding
```
AI Models Trained On Datasets
Maps the lineage between your AI models and the datasets they were trained on, giving you visibility into which data sources feed each model. This is a starting point for governing training data and can be extended with additional relationships (for example, to check whether those datasets contain sensitive data).
```
FIND AI Model
THAT Trained On Dataset
```
## Data Access Governance
Data access governance queries use identity context to surface which identities can reach your sensitive data stores and where that access is risky—helping you enforce least privilege and govern who can access your data. See the following examples as starting points for building your queries:
Identities with Excessive Permissions
Surfaces cloud identities that carry overly broad or unneeded access—such as AWS policies with full wildcard permissions—so you can enforce least privilege and remediate risky access to your resources. Adjust the `Finding Name` value in the dropdown to look for other excessive assignments, such as `Excessive AWS policy with service level wildcard...` or `Excessive Azure Role Assignment found`.
```
FIND Identity
THAT Has Identity Finding WHERE Finding Name = Excessive AWS policy with full wildcard...
```
---
# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.
## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.
Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter:
```
GET https://cortex-docs.paloaltonetworks.com/data-security-documentation/advanced-tools/graph-search.md?ask=&goal=
```
`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.
Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.