Presentation + Paper
7 June 2024 The AI security pyramid of pain
Chris M. Ward, Josh Harguess, Julia Tao, Daniel Christman, Mike Tan, Paul Spicer
Author Affiliations +
Abstract
With resource constraints being a reality for many organizations, effective security for Artificial Intelligence (AI) systems has become a critical focus. To address this challenge, we introduce the AI Security Pyramid of Pain, a framework that adapts the cybersecurity Pyramid of Pain to categorize and prioritize AI-specific threats. This framework provides a structured approach to understanding and addressing various levels of AI threats. Starting at the base, the pyramid emphasizes Data Integrity, which is essential for the accuracy and reliability of datasets and AI models, including their weights and parameters. Ensuring data integrity is crucial, as it underpins the effectiveness of all AI-driven decisions and operations. The next level, AI System Performance, focuses on MLOps-driven metrics such as model drift, accuracy, and false positive rates. These metrics are crucial for detecting potential security breaches, allowing for early intervention and maintenance of AI system integrity. Advancing further, the pyramid addresses the threat posed by Adversarial Tools, identifying and neutralizing tools used by adversaries to target AI systems. This layer is key to staying ahead of evolving attack methodologies. At the Adversarial Input layer, the framework addresses the detection and mitigation of inputs designed to deceive or exploit AI models. This includes techniques like adversarial patterns and prompt injection attacks, which are increasingly used in sophisticated attacks on AI systems. Data Provenance is the next critical layer, ensuring the authenticity and lineage of data and models. This layer is pivotal in preventing the use of compromised or biased data in AI systems. At the apex is the tactics, techniques, and procedures (TTPs) layer, dealing with the most complex and challenging aspects of AI security. This involves a deep understanding and strategic approach to counter advanced AI-targeted attacks, requiring comprehensive knowledge and planning. This paper provides an overview of each layer, discussing their significance and interplay within AI system security. The AI Security Pyramid of Pain offers a strategic approach for organizations to navigate and mitigate the multifaceted threats in AI security effectively.
Conference Presentation
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Chris M. Ward, Josh Harguess, Julia Tao, Daniel Christman, Mike Tan, and Paul Spicer "The AI security pyramid of pain", Proc. SPIE 13054, Assurance and Security for AI-enabled Systems, 1305408 (7 June 2024); https://doi.org/10.1117/12.3025025
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KEYWORDS
Artificial intelligence

Information security

Systems modeling

Data modeling

Computer security

Defense and security

Reliability

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