The AI Security Pyramid of Pain
- URL: http://arxiv.org/abs/2402.11082v1
- Date: Fri, 16 Feb 2024 21:14:11 GMT
- Title: The AI Security Pyramid of Pain
- Authors: Chris M. Ward, Josh Harguess, Julia Tao, Daniel Christman, Paul
Spicer, Mike Tan
- Abstract summary: 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.
- Score: 0.18820558426635298
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: 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.
Related papers
- Towards Guaranteed Safe AI: A Framework for Ensuring Robust and Reliable AI Systems [88.80306881112313]
We will introduce and define a family of approaches to AI safety, which we will refer to as guaranteed safe (GS) AI.
The core feature of these approaches is that they aim to produce AI systems which are equipped with high-assurance quantitative safety guarantees.
We outline a number of approaches for creating each of these three core components, describe the main technical challenges, and suggest a number of potential solutions to them.
arXiv Detail & Related papers (2024-05-10T17:38:32Z) - Artificial Intelligence as the New Hacker: Developing Agents for Offensive Security [0.0]
This paper explores the integration of Artificial Intelligence (AI) into offensive cybersecurity.
It develops an autonomous AI agent, ReaperAI, designed to simulate and execute cyberattacks.
ReaperAI demonstrates the potential to identify, exploit, and analyze security vulnerabilities autonomously.
arXiv Detail & Related papers (2024-05-09T18:15:12Z) - Towards an AI-Enhanced Cyber Threat Intelligence Processing Pipeline [0.0]
This paper explores the potential of integrating Artificial Intelligence (AI) into Cyber Threat Intelligence (CTI)
We provide a blueprint of an AI-enhanced CTI processing pipeline, and detail its components and functionalities.
We discuss ethical dilemmas, potential biases, and the imperative for transparency in AI-driven decisions.
arXiv Detail & Related papers (2024-03-05T19:03:56Z) - Pangu-Agent: A Fine-Tunable Generalist Agent with Structured Reasoning [50.47568731994238]
Key method for creating Artificial Intelligence (AI) agents is Reinforcement Learning (RL)
This paper presents a general framework model for integrating and learning structured reasoning into AI agents' policies.
arXiv Detail & Related papers (2023-12-22T17:57:57Z) - AI Hazard Management: A framework for the systematic management of root
causes for AI risks [0.0]
This paper introduces the AI Hazard Management (AIHM) framework.
It provides a structured process to systematically identify, assess, and treat AI hazards.
It builds upon an AI hazard list from a comprehensive state-of-the-art analysis.
arXiv Detail & Related papers (2023-10-25T15:55:50Z) - AI Maintenance: A Robustness Perspective [91.28724422822003]
We introduce highlighted robustness challenges in the AI lifecycle and motivate AI maintenance by making analogies to car maintenance.
We propose an AI model inspection framework to detect and mitigate robustness risks.
Our proposal for AI maintenance facilitates robustness assessment, status tracking, risk scanning, model hardening, and regulation throughout the AI lifecycle.
arXiv Detail & Related papers (2023-01-08T15:02:38Z) - Towards Automated Classification of Attackers' TTPs by combining NLP
with ML Techniques [77.34726150561087]
We evaluate and compare different Natural Language Processing (NLP) and machine learning techniques used for security information extraction in research.
Based on our investigations we propose a data processing pipeline that automatically classifies unstructured text according to attackers' tactics and techniques.
arXiv Detail & Related papers (2022-07-18T09:59:21Z) - SoK: On the Semantic AI Security in Autonomous Driving [42.15658768948801]
Autonomous Driving systems rely on AI components to make safety and correct driving decisions.
For such AI component-level vulnerabilities to be semantically impactful at the system level, it needs to address non-trivial semantic gaps.
In this paper, we define such research space as semantic AI security as opposed to generic AI security.
arXiv Detail & Related papers (2022-03-10T12:00:34Z) - The Feasibility and Inevitability of Stealth Attacks [63.14766152741211]
We study new adversarial perturbations that enable an attacker to gain control over decisions in generic Artificial Intelligence systems.
In contrast to adversarial data modification, the attack mechanism we consider here involves alterations to the AI system itself.
arXiv Detail & Related papers (2021-06-26T10:50:07Z) - Vulnerabilities of Connectionist AI Applications: Evaluation and Defence [0.0]
This article deals with the IT security of connectionist artificial intelligence (AI) applications, focusing on threats to integrity.
A comprehensive list of threats and possible mitigations is presented by reviewing the state-of-the-art literature.
The discussion of mitigations is likewise not restricted to the level of the AI system itself but rather advocates viewing AI systems in the context of their supply chains.
arXiv Detail & Related papers (2020-03-18T12:33:59Z) - Adversarial vs behavioural-based defensive AI with joint, continual and
active learning: automated evaluation of robustness to deception, poisoning
and concept drift [62.997667081978825]
Recent advancements in Artificial Intelligence (AI) have brought new capabilities to behavioural analysis (UEBA) for cyber-security.
In this paper, we present a solution to effectively mitigate this attack by improving the detection process and efficiently leveraging human expertise.
arXiv Detail & Related papers (2020-01-13T13:54:36Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.