A community-driven open-source project that structures threat hunting workflows using MITRE ATT&CK, Jupyter notebooks, and AI-augmented planning.
The Threat Hunter Playbook is an open-source project that provides a structured framework for threat hunting, documenting adversary tradecraft and detection logic using MITRE ATT&CK and interactive Jupyter notebooks. It solves the problem of ad-hoc, inconsistent hunting by formalizing the planning, execution, and reporting stages with reusable workflows and AI-augmented tools. The project aims to make threat hunting more efficient, repeatable, and community-driven.
Security analysts, threat hunters, detection engineers, and cybersecurity professionals who need structured methodologies for hunting adversary behavior and developing detection logic. It's also valuable for teams building internal hunting playbooks or integrating AI into security workflows.
Developers choose this project because it provides a concrete, executable framework for threat hunting that combines community knowledge with modern tools like Jupyter notebooks and AI augmentation. Unlike generic guides, it offers structured workflows, alignment with MITRE ATT&CK, and the ability to validate hunts with real datasets, making detection development more efficient and collaborative.
A community-driven, open-source project to share detection logic, adversary tradecraft and resources to make detection development more efficient.
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Organizes hunts into Plan, Execute, and Report stages, emphasizing reasoning and intent over ad-hoc investigation, as outlined in the framework section to make hunting repeatable.
Documents post-compromise adversary behavior using the ATT&CK framework for consistent taxonomy and reference, ensuring hunts are grounded in standardized tradecraft.
Combines markdown, analytics, and datasets in Jupyter notebooks that can be run locally or via BinderHub, allowing hunts to be treated as testable, interactive documents.
Integrates Agent Skills to capture hunting knowledge as explicit workflows for planning and hypothesis generation, augmenting human expertise with structured guidance.
The project currently concentrates on formalizing the planning stage, with less developed support for execution and reporting, which may require additional tooling or effort.
Relies on Jupyter notebooks and BinderHub, necessitating familiarity with these tools and potential setup challenges for local environments without cloud access.
As a community-driven project, the quality and updates of hunts depend on contributions, leading to potential inconsistencies or gaps in coverage.