The AI Security Pyramid of Pain
<|reference_start|>The AI Security Pyramid of Pain: 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.<|reference_end|>
@article{ward2024the,
    title={The AI Security Pyramid of Pain},
    author={Chris M. Ward, Josh Harguess, Julia Tao, Daniel Christman, Paul
  Spicer, Mike Tan},
    journal={arXiv preprint arXiv:2402.11082},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.11082},
    primaryClass={cs.CR cs.AI}
}