Not all threat modeling frameworks are created equal. In cloud and AI environments, choosing the wrong framework can lead to false confidence, missed attack paths, and security blind spots.
This article takes an advanced, analytical, and tutorial-driven approach to cloud threat modeling frameworks, focusing on real-world cloud usage in the US enterprise landscape.
We will break down:
When each framework works best
Where each framework fails in cloud-native systems
How to combine frameworks for stronger coverage
What Is a Threat Modeling Framework?
A threat modeling framework is a structured lens used to identify and categorize potential attacks against a system.
In cloud environments, frameworks help answer:
Which cloud components are most likely to be abused?
How identities, APIs, and data flows can be exploited
Which risks deserve immediate mitigation
Frameworks do not replace expertise — they amplify it.
STRIDE: The Most Common (and Most Misused) Framework
What STRIDE Stands For
STRIDE categorizes threats into six classes:
Spoofing identity
Tampering with data
Repudiation
Information disclosure
Denial of service
Elevation of privilege
Where STRIDE Works Well in the Cloud
STRIDE is highly effective for:
API threat modeling
Microservices architectures
Authentication and authorization design
Early-stage cloud architecture reviews
Example (AWS / Azure):
Spoofing → Compromised IAM credentials
Elevation of privilege → Over-permissioned IAM roles
Information disclosure → Public S3 or Blob storage
Where STRIDE Breaks Down
STRIDE struggles with:
Business logic abuse
Multi-stage attack chains
AI/ML-specific threats
Insider threat scenarios
US enterprise mistake: Using STRIDE alone and assuming full coverage.
PASTA: Risk-Centric Threat Modeling for Enterprises
What Is PASTA?
PASTA (Process for Attack Simulation and Threat Analysis) is a risk-driven framework designed for enterprise-scale systems.
PASTA focuses on:
Business impact
Threat actor capability
Attack simulation
Why PASTA Fits US Enterprises
PASTA aligns well with:
NIST Risk Ma