> 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/cortex-xsiam/detect-investigate-and-respond-to-threats/threat-management/analytics/analytics-issues-and-analytics-biocs.md).

# Analytics issues and Analytics BIOCs

Learn how Cortex XSIAM Analytics issues and BIOCs identify anomalous activity for investigation.

The Cortex XSIAM Analytics engine generates an issue when it detects suspicious activity, composed of multiple events, that deviates from the behavior baseline it establishes over time. To ensure the Analytics detectors generates issues efficiently and do not overcrowd your Issues table, Cortex XSIAM automatically disables issues from detectors that reach 5000 or more matches over a 24 hour period.

In addition to standard Analytics issues, there is another category of issues generated by Analytics behavioral indicators of compromise (ABIOCs). In contrast to standard Analytics issues, Analytics BIOCs (ABIOCs)—indicate a single event of suspicious behavior with an identified chain of causality. To identify the context and chain of causality, ABIOCs leverage user, endpoint, and network profiles. The profile is generated by the Analytics Engine and can be based on a simple statistical profile or a more complex machine-learning profile. Cortex XSIAM tailors each ABIOC to your specific environment after analyzing your logs and data sources and continually tunes and delivers new ABIOCs with content updates.

<br>


---

# 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 by asking a question.

Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters:

```
GET https://cortex-docs.paloaltonetworks.com/cortex-xsiam/detect-investigate-and-respond-to-threats/threat-management/analytics/analytics-issues-and-analytics-biocs.md?ask=<question>&goal=<user_goal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case.

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.