·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
Photoshop and Premiere now have AI assistants2h◆Adobe’s redesigned AI studio remembers what your creations look like2h◆Pixi’s new iOS app turns text messages into interactive AR experiences3h◆Effects of sparsity and superposition on loss in simple autoencoders11h◆Bridging Data Gaps in Structural Fragility Modeling through Transfer Learning: Methodology and Case Studies11h◆Ensuring Trustworthy Online A/B Testing: Addressing Five Key Questions on CUPED11h◆NeSyCat Torch: A Differentiable Tensor Implementation of Categorical Semantics for Neurosymbolic Learning11h◆DeFAb: A Verifiable Benchmark for Defeasible Abduction in Foundation Models11h◆VGGHeads: 3D Multi Head Alignment with a Large-Scale Synthetic Dataset11h◆Sparsity Curse: Understanding RLVR Model Parameter Space from Model Merging11h◆ScholarSum: Student-Teacher Abstractive Summarization via Knowledge Graph Reasoning and Reflective Refinement11h◆INDEQS: Informed Neural controlled Differential EQuationS11h◆Self-CTRL: Self-Consistency Training with Reinforcement Learning11h◆Target-confidence Recourse Using tSeTlin machines: TRUST11h◆Generalized Kullback-Leibler Divergence Loss11h◆Adaptive Speech-to-Spike Encoding for Spiking Neural Networks11h◆CaVe-VLM-CoT: An Interpretable Vision-Language Model Framework11h◆Investigating Inductive Biases for Machine Learning Emulation of Sudden Stratospheric Warmings in Idealised Isca Simulations11h◆Smoothness-Based Derandomization of PAC-Bayes Bounds11h◆JourneyFormer: Encoding Airbnb Guest Journey with Sequence Modeling11h◆Photoshop and Premiere now have AI assistants2h◆Adobe’s redesigned AI studio remembers what your creations look like2h◆Pixi’s new iOS app turns text messages into interactive AR experiences3h◆Effects of sparsity and superposition on loss in simple autoencoders11h◆Bridging Data Gaps in Structural Fragility Modeling through Transfer Learning: Methodology and Case Studies11h◆Ensuring Trustworthy Online A/B Testing: Addressing Five Key Questions on CUPED11h◆NeSyCat Torch: A Differentiable Tensor Implementation of Categorical Semantics for Neurosymbolic Learning11h◆DeFAb: A Verifiable Benchmark for Defeasible Abduction in Foundation Models11h◆VGGHeads: 3D Multi Head Alignment with a Large-Scale Synthetic Dataset11h◆Sparsity Curse: Understanding RLVR Model Parameter Space from Model Merging11h◆ScholarSum: Student-Teacher Abstractive Summarization via Knowledge Graph Reasoning and Reflective Refinement11h◆INDEQS: Informed Neural controlled Differential EQuationS11h◆Self-CTRL: Self-Consistency Training with Reinforcement Learning11h◆Target-confidence Recourse Using tSeTlin machines: TRUST11h◆Generalized Kullback-Leibler Divergence Loss11h◆Adaptive Speech-to-Spike Encoding for Spiking Neural Networks11h◆CaVe-VLM-CoT: An Interpretable Vision-Language Model Framework11h◆Investigating Inductive Biases for Machine Learning Emulation of Sudden Stratospheric Warmings in Idealised Isca Simulations11h◆Smoothness-Based Derandomization of PAC-Bayes Bounds11h◆JourneyFormer: Encoding Airbnb Guest Journey with Sequence Modeling11h◆
News/Like a Hammer, It Can Build, It Can Break: Large Language Model Uses, Perceptions, and Adoption in Cybersecurity Operations on Reddit
arxiv
PublishedJune 17, 2026 at 4:00 AM

Like a Hammer, It Can Build, It Can Break: Large Language Model Uses, Perceptions, and Adoption in Cybersecurity Operations on Reddit

Source
arxiv.orgfull article ↗
Read on arxiv→
Publisher summary· verbatim

arXiv:2604.09998v2 Announce Type: replace-cross Abstract: Large language models (LLMs) have recently emerged as promising tools for augmenting Security Operations Center (SOC) workflows, with vendors increasingly marketing autonomous AI solutions for SOCs. However, there remains a limited empirical

Stay posted· Newsletter

A 5-min weekly brief — top movers, price watch, story of the week.

// no spam · unsubscribe one-click · free forever

Discussion
Source
↗
arxiv
Read original ↗All from arxiv →

No replies yet. Be first.

Source
↗
arxiv
Read original ↗All from arxiv →

Related coverage

More from ARXIV
arxivEffects of sparsity and superposition on loss in simple autoencoders11harxivBridging Data Gaps in Structural Fragility Modeling through Transfer Learning: Methodology and Case Studies11harxivEnsuring Trustworthy Online A/B Testing: Addressing Five Key Questions on CUPED11harxivNeSyCat Torch: A Differentiable Tensor Implementation of Categorical Semantics for Neurosymbolic Learning11h
The Bubble Brief
WEEKLY

Read AI insights every Tuesday — top movers, new releases, story of the week.

// no spam · unsubscribe one-click · free forever

Originally published on arxiv ↗
HomeModelsNews