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Threat model

As explained in Cyber Threat Hunting

Threat modeling is the systematic analysis of threats, attack methods, assets, and potential attack paths affecting a system or organization.

A threat model systematically identifies threats affecting an environment, then describes their relevance, potential effect, actors, targets, attack vectors, and indicators. Its results help prioritize threat-hunting work rather than treating every possible threat equally. section 5.1.1 section 5.3.1

Build a useful threat model around these operational elements:

  • Source and threat: state where the attack originates and what compromise or unauthorized action may occur. section 5.1.1
  • Actor and target: identify plausible adversaries and the containers, nodes, applications, or infrastructure they may affect. section 5.1.1
  • Attack vector and effect: describe how access is obtained and what control, persistence, or malicious activity could follow. section 5.1.1
  • Priority and preparation: select threats with the highest relevance and effect, formulate a hypothesis, define scope, and gather suitable event sources. section 5.1.1 section 5.3.1

Kubernetes container escape

A public-cloud Kubernetes threat model considers an attacker escaping a privileged container, reaching hosting nodes and other containers, querying the Kubernetes API server, and provisioning crypto-mining, botnet, or Tor resources. The scenario identifies malicious individuals, state-sponsored groups, or organized crime as actors; containers and nodes as targets; privileged access as the attack vector; and potential control of the cluster as the effect. Unexpected API calls, accounts, and locations become indicators for a prioritized hunt. section 5.1.1

Figure 5.5 Threat-hunting process: Preparation phase

Use the preparation workflow to connect a selected threat scenario with its hunting hypothesis, scope, infrastructure context, and available logs. section 5.3.1

How Cyber Threat Hunting covers it

How deep it goes
Breaks scenarios into actors, targets, vectors, effects, and indicators, then continues through hypothesis, scope, and data-source preparation.
Examples
Uses a Kubernetes container-escape scenario involving privileged access, API-server activity, malicious resources, and unexpected API calls.
What you can do after
Diagnose/debug with it by selecting a high-priority threat, forming a hunt hypothesis, defining scope, and choosing relevant logs.
How it connects
Connects threat hunting with Kubernetes, cloud infrastructure, attack vectors, audit logs, and indicators of compromise.

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This explainer was generated by Manning's knowledge graph from the text of Cyber Threat Hunting. The section links point to the parts of the book it draws on.