# Helping CNAs Generate CVSS Scores Faster and More Confidently Using XAI
## 7 Discussion
### Enhancing Collaboration between Security and ML Teams  
The existing literature predominantly emphasizes the development of ML models, often delivered to analysts as a black-box service. However, the realm of cybersecurity demands a thorough approach, one that does not merely follow blindly but asserts control over the intricate processes involved. Incorporating subject matter experts into the ML development process becomes imperative in ensuring the alignment of AI solutions with the specific needs and complexities of security contexts. Additionally, the literature has already succeeded in yielding high-performance models, so focusing on achieving collaboration between ML and security teams should be the next step forward. More specifically, the focus should be shifted towards optimizing the usability of AI solutions for security professionals. It is crucial to consider the analysts' roles and the real-world conditions:  
* Difference in experience: Analyst teams typically are a mix of seniors and juniors. While seniors guide and mentor juniors, unexpected circumstances such as the unavailability of seniors due to a high volume of work or the departure of a senior before completing knowledge transfer can hinder juniors' ability to quickly grasp and identify trends.
* High volume of work: Analysts are not solely dedicated to analysis tasks. They also participate in meetings, prepare and deliver presentations, engage in continuous learning, and pursue certifications. With multiple responsibilities demanding their attention, analysts cannot dedicate 100% of their time towards analysis. This increased workload raises the possibility of errors due to distraction or oversight.
* Different learning styles: Analysts are human individuals with diverse cognitive styles. Some prefer abstract, descriptive, and visual explanations, while others prefer concise, direct, and text-based explanations.
* Diverse backgrounds: Each analyst brings a unique professional background to their role. As a result, certain trends, technicalities, abbreviations, or underlying issues may elude an analyst who lacks familiarity with specific domains, potentially impacting their analysis.
* Need for continuous learning: Given the escalating number and severity of vulnerabilities, as illustrated in Figures 1 and 2, analysts face the challenge of staying up-to-date. This becomes increasingly challenging when confronted with stringent deadlines for their other responsibilities.  
Our work represents a progressive step in this direction, acknowledging the need for continuous refinement and exploration of avenues where security teams can seamlessly contribute their insights and expertise to enhance the efficacy of ML models. The emphasis should not solely be on achieving better collaboration but on making the collaborative process more accessible and user-friendly for security practitioners. Moving forward, it is imperative to explore novel approaches and strategies that empower security teams to share their findings and knowledge effectively, fostering an environment of collaborative improvement of AI-driven security solutions.