March 2026

By: Meredith Bradford Photo Courtesy of Tethral Your AI agent can turn on a light. It can check your calendar. It can set a timer. Each of those is a solved problem. But when you say “leaving in five minutes” and the house needs to arm security, step down lighting, adjust climate to away mode, kill the media, and queue a morning state for when you get back, no single agent call handles that. You need coordinated execution […]

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This paper presents DeepStage, a deep reinforcement learning (DRL) framework for adaptive, stage-aware defense against Advanced Persistent Threats (APTs). The enterprise environment is modeled as a partially observable Markov decision process (POMDP), where host provenance and network telemetry are fused into unified provenance graphs. Building on our prior work, StageFinder, a graph neural encoder and an LSTM-based stage estimator infer probabilistic attacker stages aligned with the MITRE ATT&CK framework. These stage beliefs, combined with graph embeddings, guide a […]

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Artificial intelligence is rapidly becoming the default answer to almost every security challenge. Boards are asking about it. CISOs are budgeting for it. Vendors are rebranding around it. But in identity and access management (IAM), AI is only as good as the data you feed it, and in most enterprises, that data is far from complete. IBM’s Cost of a Data Breach Report found that compromised credentials remain one of the most common initial attack vectors in breaches, […]

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Electronic control units (ECUs) embedded within modern vehicles generate a large number of asynchronous events known as diagnostic trouble codes (DTCs). These discrete events form complex temporal sequences that reflect the evolving health of the vehicle’s subsystems. In the automotive industry, domain experts manually group these codes into higher-level error patterns (EPs) using Boolean rules to characterize system faults and ensure safety. However, as vehicle complexity grows, this manual process becomes increasingly costly, error-prone, and difficult to scale. […]

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:::info Astounding Stories of Super-Science July, 2008, by Astounding Stories is part of HackerNoon’s Book Blog Post series. You can jump to any chapter in this book here. The Call of the Wild – The Toil of Trace and Trail Astounding Stories of Super-Science July 2008: The Call of the Wild – The Toil of Trace and Trail By Jack London ::: Thirty days from the time it left Dawson, the Salt Water Mail, with Buck and his mates […]

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Byline: Matthew Kayser Artificial intelligence now operates inside the core infrastructure of modern enterprises. Models influence logistics networks, financial systems, fraud detection pipelines, and automated decision platforms. As AI workloads expand across distributed environments, the security challenge is shifting. Protecting AI systems can no longer rely solely on reactive defense. Organizations must secure the full lifecycle of these systems. Cybersecurity researcher and adjunct professor Ashok Kumar Kanagala has focused much of his work on how security architecture must […]

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While Reinforcement Learning (RL) enhances Large Language Model reasoning, on-policy algorithms like GRPO are sample-inefficient as they discard past rollouts. Existing experience replay methods address this by reusing accurate samples for direct policy updates, but this often incurs high computational costs and causes mode collapse via overfitting. We argue that historical data should prioritize sustaining diversity rather than simply reinforcing accuracy. To this end, we propose Dynamic Jensen-Shannon Replay (DyJR), a simple yet effective regularization framework using a […]

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Hierarchical Instruction Following (HIF) refers to the problem of prompting large language models with a priority-ordered stack of instructions. Standard methods like RLHF and DPO typically fail in this problem since they mainly optimize for a single objective, failing to explicitly enforce system prompt compliance. Meanwhile, supervised fine-tuning relies on mimicking filtered, compliant data, which fails to establish the priority asymmetry at the algorithmic level. In this paper, we introduce textsc{HIPO}, a novel alignment framework that formulates HIF […]

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