A Multilayered Defense Strategy Against Adversarial AI Attack

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Presented: December 16, 2024 12:00 pm (ET)
Presented by: Ash Banerjee, Roopa Chowbey

In the digital era, the cybersecurity landscape faces an unprecedented challenge from adversarial attacks, which manipulate data and machine-learning (ML) algorithms to undermine security systems. These sophisticated threats pose significant risks across various sectors and challenge traditional defense mechanisms. This study introduces a multilayered defense strategy designed to counter these advanced threats, focusing on the strategy’s practical implementation and assessing its effectiveness in fortifying cybersecurity against the continuously evolving landscape of artificial intelligence (AI)-driven cyber threats.

The proposed strategy responds to the increasing complexity of cyberattacks that conventional security measures often fail to address. By outlining a comprehensive approach that integrates multiple defensive layers, from input validation to continuous system monitoring, this study sheds light on a methodologically sound and adaptable solution. It aims to provide a blueprint for effectively mitigating the risks associated with adversarial AI attacks, ensuring a robust and resilient cybersecurity infrastructure.

The following topics will be covered:

  • Using AI within the U.S. Department of  Defense (DoD) for mission-critical objectives, including cyber-related missions
  • Adversarial AI and how it relates to cybersecurity
  • Impacts of adversarial AI on data, models, and security and examples of real-world scenarios in the civilian and defense sectors
  • Detecting adversarial AI when it targets national security
  • Prevention and remediation techniques
  • Implementation considerations
  • How the DoD handles adversarial AI

Focus Areas

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