Traditional cloud sourcing frameworks provide high-level lifecycle guidance, but they rarely address how financial governance should be embedded into day-to-day cloud operations. This paper presents a model-driven FinOps framework that integrates the Inform, Optimize, and Operate pillars into the Cloud Sourcing Life Cycle of a large pharmaceutical enterprise running on AWS. We first analyze the organization’s “governance vacuum,” characterized by reactive spend visibility, disconnected financial authorization, and ambiguous ownership and decommissioning responsibilities. We then introduce a structural metamodel that links technical resources, financial entities, and governance artifacts, and derive executable BPMN workflows that operationalize FinOps pillars across provisioning, optimization, and retirement. An illustrative case study instantiation explores how automated guardrails, standardized approval paths, and role-specific responsibilities can be used to control expenditure requests, proposing a method to close critical governance gaps. This conceptual approach suggests that cloud cost governance can become an operational default rather than an after-the-fact reporting activity.
Enterprise information systems increasingly depend on distributed actors that must coordinate under uncertainty, partial observability, and dynamic role demands. A key open question is whether explicit, lightweight role-based orchestration can improve cooperative learning without replacing reward-based signals. This paper proposes and evaluates two orchestration mechanisms layered on top of the Multi-Agent Posthumous Credit Assignment (POCA) algorithm in the DungeonEscape cooperative environment. The first mechanism dynamically assigns temporary task roles to agents based on environment state. The second extends this with urgency-weighted confidence signals per role. Experiments across three random seeds show that task-based orchestration achieves the highest peak group reward (0.811), while baseline POCA remains the most stable across seeds (0.746), and confidence-aware orchestration underperforms on average (0.691). The results suggest that selective, lightweight coordination guidance can improve cooperative specialization, but overly expressive orchestration signals introduce optimization difficulty. These findings offer practical guidance for designing adaptive coordination mechanisms in actor-based business process systems.
Passwords are as old as computers and are indispensable for daily operations in all aspects of digital life. Despite reinforcements such as 2FA and MFA, the use of passwords in authentication and authorization is still essential. At the same time, password compromise is still one of the leading causes of successful cyber attacks, regardless of scale, target, and motive. One of the commonly used avenues for password compromise is the interception of its hashed form and subsequent usage of “rainbow tables” to recover the original password. Overall, “rainbow tables” have been systematically proven to be orders of magnitude more effective than pure bruteforce enumeration and all-combination dictionaries. The development of “rainbow tables” datasets/toolsets and of associated research works have seen some intense and fruitful developments in the early 2000s–2010s. Nevertheless, a few challenges persisted – the efforts largely remained scattered, some of them even went offline without any meaningful preservation efforts, overall the datasets and toolsets remained under-characterized, and the underlying knowledge is not systematized nor aggregated. Overall, the expertise to approach the “rainbow tables” research field remains a “black magic” type of knowledge that prevents more researchers from contributing to the field. This paper aims to bridge the gap in the characterization and systematization of knowledge and resources pertaining to publicly researched “rainbow tables” and associated tools. We hope that this condensed overview provides a useful reference point for cybersecurity practitioners, regardless of whether their work involves applying existing rainbow tables or advancing methodological and research directions in this area.
Large language models are widely used for vulnerability lookup and explanation, but their reliability in security-critical workflows is unclear. We evaluate ChatGPT using structured prompts based on CERT/CC vulnerability notes, CVE entries, and additional tests targeting non-standard failure modes. Across vulnerability records, the model generally produced correct core descriptions, with the most frequent issues being low-severity omissions of supporting metadata such as affected versions, vendors, and weakness classifications. For older vulnerability notes, performance declined and included high-severity errors, such as incorrect mappings between vulnerability identifiers. Additional testing revealed further risks: silent substitution of identifiers, overly long and weakly curated product lists that appear comprehensive but lack verification, and context-driven mismatches between vulnerability notes and identifiers. In a follow-up months later with a newer model, the system first rejected fake CVEs but then produced confident, entirely fabricated details, revealing instability between rejection and hallucination. Taken together, these results show that while ChatGPT can assist in vulnerability lookup and explanation, its outputs require human verification and cross-checking against authoritative sources. Practical safeguards—such as single-item prompting, structured output constraints, explicit repetition of identifiers, and rejection checks—can help reduce error propagation in operational settings.
Last-mile delivery processes often suffer from discrepancies between predicted and actual delivery locations, leading to inefficiencies and increased operational costs. Existing approaches mainly focus on improving routing or geocoding accuracy, but limited attention has been given to proactively predicting such discrepancies. This paper proposes a data-driven framework that integrates geospatial analysis with machine learning to identify deliveries at risk of spatial deviation. The approach combines geospatial distance computation with feature engineering based on address structure and contextual attributes and applies a Gradient Boosting model for predictive classification. The results demonstrate that the proposed framework achieves reliable predictive performance (AUC = 0.736) and enables the identification of high-risk deliveries at scale. The main contribution of this study lies in combining geospatial discrepancy quantification with predictive modeling into a unified operational framework. However, the approach is limited by the use of static features and a fixed spatial threshold. Future work will explore dynamic contextual data and adaptive thresholding.
