  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.
