Core Concepts
Effective integration of AI-based vulnerability management solutions into industry workflows remains a significant challenge, requiring a collaborative effort between industry and academia to address key barriers related to model scope, customization flexibility, and financial implications.
Abstract
The paper explores the current state of security vulnerability management in the industry, highlighting the traditional workflows and the emerging opportunities presented by AI-based approaches. It then identifies three key barriers that prevent the industry from readily adopting academic AI models:
Inconsistent Scope and Priority: Industry favors specialized models that excel at addressing certain high-priority vulnerabilities, while academic research often focuses on one-for-all solutions with variable performance across different vulnerability types.
Limited Customization Flexibility: Industries require the ability to customize vulnerability management tools to accommodate diverse products and adhere to different security standards, which current academic models rarely address.
Unclear Financial Implications: The industry needs a clear understanding of the financial benefits and costs associated with integrating AI-based security vulnerability management solutions, but previous research works have inadequately discussed these aspects.
To bridge these gaps, the paper proposes three future research directions:
Emphasizing Specialized Model Research: Developing models that specialize in certain types of vulnerabilities to align with industry priorities and increase confidence in academic solutions.
Developing Flexible and Scalable Models: Ensuring that research models are well-documented, executable, and easily customizable to suit diverse industry environments and security standards.
Constructing Industry Reflective Evaluation Metrics, Datasets, and Resources: Designing evaluation scenarios and metrics that closely reflect real-world industry settings to provide a more accurate assessment of the practical and cost-effective use of academic models.
Additionally, the paper identifies two barriers that prevent the industry from effectively contributing to academic endeavors:
Shortage of Large-Scale and Diverse Datasets: Industry datasets hold invaluable insights into security vulnerabilities, but the sharing of these datasets is hindered by concerns over sensitive information disclosure.
Lack of First-Hand Industry Expertise: Academic researchers often lack direct access to industry practitioners' knowledge and experience, which is crucial for enhancing the effectiveness of research models in practice.
To address these barriers, the paper proposes two future collaboration directions:
Exploring Data Anonymization and Environment Simulation Techniques: Developing advanced methods to enable the secure sharing of industry datasets while preserving the original patterns and contexts of vulnerabilities.
Fostering Bidirectional Collaboration between Industry and Academia: Establishing robust knowledge exchange channels, such as internship programs and joint research initiatives, to leverage industry expertise and guide academic research towards practical solutions.
Overall, the paper highlights the need for a more collaborative and synergistic relationship between industry and academia to drive the effective adoption of AI-based vulnerability management solutions in real-world settings.