A Roadmap to Address Burnout in the Cybersecurity Profession: Outcomes from a Multifaceted Workshop

Ann Rangarajan; Calvin Nobles; Josiah Dykstra; Margaret Cunningham; Nikki Robinson; Tammie Hollis; Celeste Lyn Paul; Charles Gulotta

arXiv · 2502.10293

A Roadmap to Address Burnout in the Cybersecurity Profession: Outcomes from a Multifaceted Workshop

Ann Rangarajan·Calvin Nobles·Josiah Dykstra·Margaret Cunningham·Nikki Robinson·Tammie Hollis·Celeste Lyn Paul·Charles Gulotta

View original source ↗Read source document ↗Export metadata ↓

Abstract

This paper addresses the critical issue of burnout among cybersecurity professionals, a growing concern that threatens the effectiveness of digital defense systems. As the industry faces a significant attrition crisis, with nearly 46% of cybersecurity leaders contemplating departure from their roles, it is imperative to explore the causes and consequences of burnout through a socio-technical lens. These challenges were discussed by experts from academia and industry in a multi-disciplinary workshop at the 26th International Conference on Human-Computer Interaction to address broad antecedents of burnout, manifestation and its consequences among cybersecurity professionals, as well as programs to mitigate impacts from burnout. Central to the analysis is an empirical study of former National Security Agency (NSA) tactical cyber operators. This paper presents key insights in the following areas based on discussions in the workshop: lessons for public and private sectors from the NSA study, a comparative review of addressing burnout in the healthcare profession. It also outlines a roadmap for future collaborative research, thereby informing interdisciplinary studies.

Explore related subjects

cs.CR cs.CY

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Ann Rangarajan, Calvin Nobles, Josiah Dykstra, Margaret Cunningham, Nikki Robinson, Tammie Hollis, Celeste Lyn Paul, Charles Gulotta. 2025-02-14. A Roadmap to Address Burnout in the Cybersecurity Profession: Outcomes from a Multifaceted Workshop. https://arxiv.org/abs/2502.10293

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Starfish: Rebalancing Multi-Party Off-Chain Payment Channels

Blockchain technology has revolutionized the way transactions are executed, but scalability remains a major challenge. Payment Channel Network (PCN), as a Layer-2 scaling solution, has been proposed to address this issue. However, skewed payments can deplete the balance of one party within a channel, restricting the ability of PCNs to transact through a path and subsequently reducing the transaction success rate. To address this issue, the technology of rebalancing has been proposed. However, existing rebalancing strategies in PCNs are limited in their efficiency and applicability under realistic network topologies. Cycle-based approaches only enable rebalancing among nodes that form cycles, while non-cycle-based approaches incur high on-chain operation costs. In this study, we propose Starfish, a rebalancing approach designed for the locally star-like structures formed by high-degree routing hubs and their incident channels in real-world PCNs. By leveraging a hub-centric structure, Starfish reduces the on-chain overhead of rebalancing from quadratic to linear in the number of channels, achieving high rebalancing efficiency. We formally prove the security of Starfish and conduct comparative experiments against existing rebalancing techniques to demonstrate its effectiveness.

cs.CR↗

arXiv2026

Proof-of-Authorship for Diffusion-based AI Generated Content

Recent advancements in AI-generated content (AIGC) have introduced new challenges in intellectual property protection and the authentication of generated objects. We focus on scenarios in which an author seeks to assert authorship of an object generated using latent diffusion models (LDMs), in the presence of adversaries who attempt to falsely claim authorship of objects they did not create. While proof-of-ownership has been studied in the context of multimedia content through techniques such as time-stamping and watermarking, these approaches face notable limitations. In contrast to traditional content creation sources (e.g., cameras), the LDM generation process offers greater control to the author. Specifically, the random seed used during generation can be deliberately chosen. By binding the seed to the author's identity using cryptographic functions, the author can assert to be the creator of the object. We refer to this stronger guarantee as proof-of-authorship, since only the creator of the object can legitimately claim the object. This contrasts with proof-of-ownership via time-stamping or watermarking, where any entity could potentially claim ownership of an object by being the first to timestamp or embed the watermark. We propose a proof-of-authorship framework involving a probabilistic adjudicator who quantifies the probability that a claim is false. Furthermore, unlike prior approaches, the proposed framework does not involve any secret. We explore various attack scenarios and analyze design choices using Stable Diffusion 2.1 and XL as representative case studies.

cs.CR↗

arXiv2026

Impact Analysis of Speech Representation Learning Models for Acoustic Side-Channel Attack

Acoustic side-channel attacks (ASCA) on keyboards have gained increasing attention, yet impact of speech representation learning models in ASCA remains unexplored. Addressing this, we introduce KEYAC, a dataset designed to analyze representation generalization for ASCA under both standard and VoIP codec settings. On KEYAC, we evaluate six representation learning models under zero-shot and partial fine-tuning settings using fully connected and convolutional networks. Results show that while partial fine-tuning improves performance, models struggle to generalize across VoIP codecs. We hypothesize this limitation stems from inadequate modeling of nonlinear feature interactions in conventional fine-tuning architectures. To address this, we employ Kolmogorov-Arnold Networks (KAN) for fine-tuning. Empirical results show that KAN-based fine-tuning consistently outperforms the baselines and establishes a new state-of-the-art on KEYAC.

cs.CR↗