task_categories:summarization task_categories:feature-extraction license:mit size_categories:n<1K format:json modality:tabular modality:text library:datasets library:pandas library:mlcroissant library:polars region:us
---
license: mit
task_categories:
- summarization
- feature-extraction
pretty_name: AI threat Hunter's playbook
size_categories:
- 1K<n<10K
---
# Dataset Card for Dataset Name

<!-- AI XDR playbook -->

This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).

## Dataset Details

### Dataset Description

<!-- AI xdr paper

XDR (Extended Detection and Response) is a security solution that combines multiple detection and response technologies to provide a more comprehensive view of an organization's security posture, making it easier to recognize and respond to potential threats[1]. AI/ML (Artificial Intelligence/Machine Learning) is a key component of XDR, as it enables advanced analytics techniques to identify potential threats and automate response actions[1][2]. Here are some ways in which AI enhances XDR platforms:

- **Advanced analytics**: XDR solutions use advanced analytics techniques supported by machine learning (ML) models to identify potential threats and automate response actions[1][5].

- **Automated response**: XDR solutions can automatically block or quarantine malicious files and alert security teams to potential incidents[1].

- **Single pane of glass view**: XDR solutions provide a unified view of all security events and incidents, making it easier for security teams to investigate and respond to threats[1].

- **Detecting unknown or zero-day threats**: AI-powered XDR solutions can detect unknown or zero-day threats, making them more effective than traditional detection and response technologies that rely on rule-based or signature-based detection methods[1][5].

- **Predicting future cyberattacks**: AI is able to predict future cyberattacks and identify their mechanisms to determine their origin, accelerating responses to attacks[5].

XDR platforms with AI can perform analyses on every layer of an organization's infrastructure, including those that were previously inaccessible to analysts[5]. AI analyzes logs and compares current activities on an organization's infrastructure to detect any unusual action on all its infrastructures, including servers, workstations, and networks[5]. Additionally, an AI-powered XDR with Next Generation Antivirus (NGAV) can detect unknown malicious files[5]. If an anomaly is detected, the sensors immediately send the information back to the XDR, which can automatically prioritize alerts so that security teams can immediately respond to potential threats[5].

Citations:
[1] Machine Learning and Artificial Intelligence (AI/ML): The Secret Sauce Behind XDR https://www.computer.org/publications/tech-news/trends/the-secret-sauce-behind-xdr/
[2] AI-Driven XDR: Defeating the Most Complex Attack Sequences - Cybereason https://www.cybereason.com/blog/ai-driven-xdr-defeating-the-most-complex-attack-sequences
[3] Harnessing the Power of AI-Driven XDR - Cybereason https://www.cybereason.com/blog/harnessing-the-power-of-ai-driven-xdr
[4] Explainable dimensionality reduction (XDR) to unbox AI 'black box' models: A study of AI perspectives on the ethnic styles of village dwellings - Nature https://www.nature.com/articles/s41599-023-01505-4
[5] How does AI enhance XDR platforms? - TEHTRIS https://tehtris.com/en/blog/how-does-ai-enhance-xdr-platforms
[6] XDR Should Be Viewed as An Open Architecture - Vectra AI https://www.vectra.ai/resources/research-reports/esg-xdr-open-architecture

By Perplexity at https://www.perplexity.ai/search/fd37ce22-dccf-4aa9-8478-d24cf6db23c4?s=m -->



- **Curated by:** [Edward Nyameri ]
- **Funded by [optional]:** [Nil funding but any interested POC is welcome]
- **Shared by [optional]:** [Edward Nyameri ]
- **Language(s) (NLP):** [LLM]
- **License:** [MIT]

### Dataset Sources [optional]

<!-- schooly-Computer Breaches -->

- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]

## Uses

<!-- Threat Hunting for AI cyber Security Tool Kit -->

### Direct Use

<!-- application platform analysis for Threat Hunters-->

[More Information Needed]

### Out-of-Scope Use

<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->

[More Information Needed]

## Dataset Structure

<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->

[More Information Needed]

## Dataset Creation

### Curation Rationale

<!-- Advancement of the Threat Hunt using Computational Intelligence to curb & contain comprising of information -->

[More Information Needed]

### Source Data

<!-- 🏫 Computer Breaches-->

#### Data Collection and Processing

<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->

[More Information Needed]

#### Who are the source data producers?

<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->

[More Information Needed]

### Annotations [optional]

<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->

#### Annotation process

<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->

[More Information Needed]

#### Who are the annotators?

<!-- This section describes the people or systems who created the annotations. -->

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#### Personal and Sensitive Information

<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->

[More Information Needed]

## Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->

[More Information Needed]

### Recommendations

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.

## Citation [optional]

<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**

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**APA:**

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## Glossary [optional]

<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->

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## More Information [optional]

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## Dataset Card Authors [optional]

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## Dataset Card Contact

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