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Cross-Cloud Behavioral Detections

Overview

This repository contains a practical, architect-level collection of behavioral security detections for public cloud environments.

The focus is on identity abuse, privilege escalation, and attacker tradecraft, rather than isolated events or generic alerting.

The detections are designed to operate on native cloud audit logs and to be adaptable across SIEMs and detection platforms.


Why Behavioral Detections

Most cloud security incidents do not begin with malware. They begin with valid credentials used in invalid ways.

Traditional detections focus on:

  • Individual API calls
  • Known bad indicators
  • Static thresholds

Attackers, however, operate through:

  • Legitimate identities
  • Expected APIs
  • Sequences of actions that only become suspicious when viewed together

This repository focuses on behavior over events.


Design Principles

Identity as the Primary Control Plane

In modern cloud environments, identity and access management is the true perimeter.

Each detection prioritizes:

  • Role assumptions
  • Service account usage
  • Privilege changes
  • Identity context (time, source, frequency)

Sequence-Based Detection

Single events are weak signals.

Detections in this repository are built around:

  • Action sequences
  • Temporal proximity
  • Changes in behavior relative to a baseline

Cloud-Native and Portable

All detections are based on:

  • AWS CloudTrail
  • Azure Entra ID and Activity Logs
  • GCP Audit Logs

They are intentionally written to remain:

  • Understandable without vendor-specific abstractions
  • Portable across detection engines

Operational Reality

Each detection includes:

  • Rationale
  • Expected false positives
  • Practical tuning guidance

A detection that cannot be tuned is operational debt.


Repository Structure

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Cross-cloud behavioral security detections focused on identity abuse, privilege escalation, and attacker tradecraft in AWS, Azure, and GCP.

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