Risk Models as Mediating Artifacts: A Postphenomenological Analysis of the CIIM Framework in Cybersecurity Practice
- URL: http://arxiv.org/abs/2604.22866v1
- Date: Thu, 23 Apr 2026 15:33:10 GMT
- Title: Risk Models as Mediating Artifacts: A Postphenomenological Analysis of the CIIM Framework in Cybersecurity Practice
- Abstract summary: This article argues that formal risk models function as mediating artifacts that shape how security practitioners perceive, interpret, and act on threats.<n>Based on Don Ihde's taxonomy on human-technology relationships and Peter-Paul Verbeek's extended mediational framework, the Contextual and Multimodal Hazard Impact Index (CIIM) is analyzed.<n>We examine how CIIM's time projection (t+1) and its hybrid machine learning architecture, combining LSTM/GRU, XGBoost, and Reinforcement Learning, produce a new form of technological intentionality.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: This article applies postphenomenological theory to the field of cybersecurity risk management, arguing that formal risk models function as mediating artifacts that shape how security practitioners or analysts perceive, interpret, and act on threats. Based on Don Ihde's taxonomy on human-technology relationships and Peter-Paul Verbeek's extended mediational framework, the Contextual and Multimodal Hazard Impact Index (CIIM), an original dynamic risk model presented as an empirical case study, is analyzed. CIIM is formally defined as CIIM(t+1) = [A T(t) V(t) E(t)] / R(t) + {alpha} P(t), where the condition R(t) 0 is not treated as a computational artifact to be smoothed out, but as a genuine systemic collapse that signals singularity. This design choice constitutes a deliberate phenomenological move, allowing organizational fragility to be made visible in a way that previous CVSS-based and probabilistic models conceal. In addition, we examine how CIIM's time projection (t+1) and its hybrid machine learning architecture, combining LSTM/GRU, XGBoost, and Reinforcement Learning, produce a new form of technological intentionality that structures practitioner or analyst attention and ethical deliberation. The article concludes by establishing implications for the ethical design of cybersecurity instrumentation and for the post-phenomenological methodology itself, proposing the concept of 'phenomenology of collapse' as a contribution to the empirical philosophy of technology.
Related papers
- CDFM: Towards a General-Purpose Causal Discovery Foundation Model [62.32138962508216]
Causal discovery is the process of recovering underlying causal structures from observational data.<n>We formulate the Causal Discovery Foundation Model (CDFM) as a unified, general-purpose framework for zero-shot structural inference.<n>CDFM consistently outperforms traditional algorithms, driving a paradigm shift toward a general-purpose causal discovery foundation model.
arXiv Detail & Related papers (2026-07-13T12:58:28Z) - MAECO-Lite: Modular Ontology for Dynamic Malware Analysis [0.0]
MAEC and STIX provide widely adopted vocabularies for describing malware artifacts and observations.<n>They tend to conflate enduring malware artifacts with the events generated during execution.<n>We propose MAECO-Lite, a lightweight ontology designed to represent data and operationalize their processing for dynamic malware analysis.
arXiv Detail & Related papers (2026-05-29T12:08:07Z) - Provably Secure Agent Guardrail [89.79561918065122]
Existing defense architectures rely on empirical semantic guardrails and probabilistic large model adjudicators.<n>This paper proposes a new security paradigm for agents based on the fundamental limitations of logical reasoning.
arXiv Detail & Related papers (2026-05-28T02:12:41Z) - Information as Maximum-Caliber Deviation: A bridge between Integrated Information Theory and the Free Energy Principle [51.56484100374058]
This work proposes that information can be defined as the deviation $$ of realized dynamics from a constrained maximum-caliber (MaxCal) path ensemble over a finite time horizon.<n>Under this definition, each of the cause/effect repertoires central to IIT 3.0 emerge directly from MaxCal variational principles.<n>Information $$ is shown to be equivalent to prediction error under accompanying predictive coding models.
arXiv Detail & Related papers (2026-05-03T07:22:55Z) - Generative Modeling in Protein Design: Neural Representations, Conditional Generation, and Evaluation Standards [3.8025175740783026]
Generative modeling has become a central paradigm in protein research.<n>This survey aims to accelerate the transition from predictive modeling to reliable, function-driven protein engineering.
arXiv Detail & Related papers (2026-03-27T13:02:16Z) - Towards Worst-Case Guarantees with Scale-Aware Interpretability [58.519943565092724]
Neural networks organize information according to the hierarchical, multi-scale structure of natural data.<n>We propose a unifying research agenda -- emphscale-aware interpretability -- to develop formal machinery and interpretability tools.
arXiv Detail & Related papers (2026-02-05T01:22:31Z) - Opportunities in AI/ML for the Rubin LSST Dark Energy Science Collaboration [63.61423859450929]
This white paper surveys the current landscape of AI/ML across DESC's primary cosmological probes and cross-cutting analyses.<n>We identify key methodological research priorities, including Bayesian inference at scale, physics-informed methods, validation frameworks, and active learning for discovery.
arXiv Detail & Related papers (2026-01-20T18:46:42Z) - SIGMA: Scalable Spectral Insights for LLM Collapse [51.863164847253366]
We introduce SIGMA (Spectral Inequalities for Gram Matrix Analysis), a unified framework for model collapse.<n>By utilizing benchmarks that deriving and deterministic bounds on the matrix's spectrum, SIGMA provides a mathematically grounded metric to track the contraction of the representation space.<n>We demonstrate that SIGMA effectively captures the transition towards states, offering both theoretical insights into the mechanics of collapse.
arXiv Detail & Related papers (2026-01-06T19:47:11Z) - Introduction to Symbolic Regression in the Physical Sciences [0.41998444721319217]
Symbolic regression has emerged as a powerful method for uncovering interpretable mathematical relationships from data.<n>This article introduces the Special Issue on Symbolic Regression for the Physical Sciences, motivated by the Royal Society discussion meeting held in April 2025.
arXiv Detail & Related papers (2025-12-17T19:32:50Z) - Information Science Principles of Machine Learning: A Causal Chain Meta-Framework Based on Formalized Information Mapping [7.299890614172539]
This study addresses key challenges in machine learning, namely the absence of a unified formal theoretical framework and the lack of foundational theories for model interpretability and ethical safety.<n>We first construct a formal information model, explicitly defining the ontological states and carrier mappings of typical machine learning stages.<n>By introducing learnable and processable predicates, as well as learning and processing functions, we analyze the causal chain logic and constraint laws governing machine learning processes.
arXiv Detail & Related papers (2025-05-19T14:39:41Z) - Modeling Interdependent Cybersecurity Threats Using Bayesian Networks: A Case Study on In-Vehicle Infotainment Systems [0.0]
This paper reviews the application of Bayesian Networks (BNs) in cybersecurity risk modeling.<n>A case study is presented in which a STRIDE-based attack tree for an automotive In-Vehicle Infotainment (IVI) system is transformed into a BN.
arXiv Detail & Related papers (2025-05-14T01:04:45Z) - Generalized Factor Neural Network Model for High-dimensional Regression [50.554377879576066]
We tackle the challenges of modeling high-dimensional data sets with latent low-dimensional structures hidden within complex, non-linear, and noisy relationships.<n>Our approach enables a seamless integration of concepts from non-parametric regression, factor models, and neural networks for high-dimensional regression.
arXiv Detail & Related papers (2025-02-16T23:13:55Z) - A Novel Neural-symbolic System under Statistical Relational Learning [47.30190559449236]
We propose a neural-symbolic framework based on statistical relational learning, referred to as NSF-SRL.<n>Results of symbolic reasoning are utilized to refine and correct the predictions made by deep learning models, while deep learning models enhance the efficiency of the symbolic reasoning process.<n>We believe that this approach sets a new standard for neural-symbolic systems and will drive future research in the field of general artificial intelligence.
arXiv Detail & Related papers (2023-09-16T09:15:37Z)
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.