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ThreatModeling AppSec

Implementations & recommendations

  • 2 Topics
  • 4 Replies
2 Topics
K
khyatiraval
published in ThreatModeling AppSec
Generative AI for Threat Modeling

The increasing volume and complexity of cyberattacks requires organizations of all sizes to secure network architecture proactively. Threat modeling is essential in order to make the shift from reactive to proactive security. The threat modeling process involves identifying objectives and optimizing network security by discovering vulnerabilities and developing mitigations for the threats that may impact a particular system or network. Artificial intelligence can be a useful cybersecurity tool, particularly for detecting patterns and providing rapid insights using technologies like machine learning, deep learning and natural language processing. These capabilities make it especially valuable for under-resourced cyber professionals. How can AI be valuable for threat modeling? AI can help automate the process of manually mapping components (individual elements represented in a threat model) with potential threats and countermeasures to mitigate those threats. This helps cybersecurity pro

12570
K
1 year ago
Todd
Todd
posted in ThreatModeling AppSec
Threat Modeling Methodologies

What threat modeling methodology most aligns with your threat modeling beliefs?  STRIDE, PASTA, OCTAVE, VAST, other?For me, I think a hybrid approach that centers around the VAST methodology most closely aligns with how I like to approach threat modeling. I favor VAST because it focuses on the entire SDLC lifecycle and supports a scalable solution. The 3 pillars of VAST are automation, integration, and collaboration. Using VAST, you can create a holistic view of the entire attack surface, which enables enterprises to minimize their overall risk.What is your approach to threat modeling? 

4
catiecangialosi
1 year ago
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