Search: “risk-prioritization”

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DomainAll Cyber AIWindow24h 3d 7d 30d AllIn the wild Important only Show duplicates

CategoryAll Ransomware 2 Industry 2 Vulnerability 2 Data breach 2 Malware 1 Model release 1 Threat actor 1

SeverityAll critical 7 high 2 medium 0 low 1 info 1

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How to build an exposure management program the business trusts: Lessons from Tenable’s CSO

Tenable's CSO describes an AI-driven exposure management program that consolidates tool sprawl and translates cyber risk for boards.

A Tenable blog post shares lessons from CSO Robert Huber on moving to an AI-driven exposure management program. It argues tool sprawl and data silos hinder holistic risk assessment and that exposure management unifies attack-surface data into business-level metrics for the C-suite.

Tenable BlogAug 27, 14:30 UTC · 20d agoIndustry

Toward an Empirical Probabilistic Risk Manifestation Model of Organizational Cybersecurity in SMEs

Empirical study of 22 SME security assessments builds a probabilistic risk model and shows assessments can be cut 24-45% while retaining most critical findings.

Researchers analyzed 281 validated security findings from 22 real-world SME cybersecurity assessments conducted over two years via a pro bono university clinic. They derived an empirical Risk Manifestation Model linking eight organizational security functions to two exposure conditions, five attack mechanisms, and six outcome categories, using probability propagation to identify dominant risk pathways. The dominant pathway runs from asset exposure to credential compromise to unauthorized access, stable under leave-one-organization-out analysis. Retaining six functions reduces assessment burden by 24% while preserving 97% of critical findings; five functions cut burden 45% while preserving 89% of critical findings.

arXiv cs.CRSep 14, 01:15 UTC · 2d agoResearch

From ‘High/Medium/Low’ to Dollars: Making Cyber Risk Legible to Your CFO

Cyble argues security teams should express cyber risk in financial terms for CFOs instead of high/medium/low ratings, citing its 2025 threat forecast results.

Cyble published guidance on cyber risk quantification, arguing qualitative high/medium/low ratings fail to convey financial exposure to executives. The piece notes that over 80% of its 2025 threat predictions, including AI-driven ransomware and supply-chain attacks, materialized as anticipated.

CybleAug 25, 09:45 UTC · 22d agoIndustry

Bag of Tricks or Bag of Myths? Reducing Modeling Complexity with Task Knowledge in Explainable Suicide Risk Assessment

Researchers audit 31 NLP techniques for clinician-annotated suicide risk prediction, finding only 5 of 31 comparisons yield reliable gains.

A study of 1,635 clinician-annotated social media posts ran roughly 300 controlled experiments across 7 methodological families, auditing techniques such as model scaling, synthetic data, ensembling, and threshold tuning under severe class imbalance. The proposed system reformulates risk factor prediction as entailment between posts and codebook definitions, using architecturally diverse ensembles with class-balanced training and deployment-consistent calibration. It scores 0.8203 for risk, 0.7953 for evidence, and 0.7045 macro-F1 for factors, ranking third among 53 teams.

arXiv cs.AI / cs.LG / cs.CLSep 7, 17:14 UTC · 9d agoAI research

Model-Aware Schedules Improve Generation via Fiberwise Optimal Transport

Researchers introduce model-aware diffusion schedules via fiberwise optimal transport, cutting flow-matching FID on CIFAR-10 by 38.6% at 16 function evaluations.

The paper proposes constructing diffusion and flow-matching sampling schedules from a fiberwise prediction risk defined via optimal transport, combined with coefficient-path kinetic action, yielding a closed-form time allocation. Across DDPM and flow-matching experiments spanning targets, datasets, and architectures, the schedules beat model-agnostic baselines, including a 38.6% relative FID reduction for flow matching on CIFAR-10 at 16 function evaluations. Normalized fiberwise-risk profiles from independently trained models align closely, suggesting empirical universality, and a frozen analytic allocation template retains most of the gains.

arXiv cs.AI / cs.LG / cs.CLSep 10, 17:30 UTC · 6d agoAI research

AI's Vulnerability Surge May Be More Manageable Than First Feared

New research argues the coming surge of vulnerabilities will be manageable for enterprise security teams that adopt the right triage and prioritization strategies.

A Dark Reading write-up of new research suggests the anticipated explosion in vulnerability volume may be less overwhelming than feared. The analysis indicates enterprise security teams can cope if they apply appropriate strategies for handling vulnerability influx. The piece is framed as guidance rather than a disclosure of specific flaws or incidents.

Dark ReadingSep 2, 21:14 UTC · 13d agoResearch

What vulnerability prioritization looks like when KEV, EPSS, and CVSS disagree

Cohesity field CISO Joye Purser ranks KEV over EPSS over CVSS and urges 24-72 hour patching of exploited internet-facing systems.

In a Help Net Security interview, Cohesity Global Field CISO Joye Purser lays out a vulnerability prioritization framework that puts active exploitation (KEV) first, then exploit likelihood (EPSS), then technical severity (CVSS), adjusted for asset exposure, business criticality, and compensating controls. She endorses 24-72 hour remediation targets for critical exploited internet-facing vulnerabilities and describes the organizational tradeoffs and emergency procedures needed to hit them. The interview also covers honeypot failure modes when deception systems are over-connected or over-trusted, and budget guidance recommending OT/IT segmentation, phishing-resistant MFA, and tested recovery for a 400-person manufacturer.

Help Net SecurityAug 31, 00:00 UTC · 16d agoIndustry

Rapid7 Named Among Notable Vendors in Forrester MDR Landscape: Why the Future is Exposure-informed, Preemptive MDR

Rapid7 touts its listing in Forrester's Q3 2026 MDR Landscape, arguing MDR must converge with exposure management for measurable risk reduction.

Forrester's Managed Detection and Response Services Landscape, Q3 2026 names Rapid7 among notable providers and predicts MDR services will converge with exposure and posture improvement. Rapid7 pitches its exposure-informed, preemptive MDR built on its own SIEM, combining vulnerability findings and asset risk scoring with detection and response. The service includes unlimited incident response and a human-led, AI-enhanced investigation model. Forrester advises buyers to demand providers prove investigations rather than narrate dashboards.

Rapid7 BlogSep 14, 14:51 UTC · 2d agoIndustry

AI vulnerability discovery scores the highest impact of 20 emerging risks

Gartner survey of 316 organizations ranks AI-driven vulnerability discovery as the top emerging risk, with tangible impact expected within roughly two years.

Gartner's quarterly survey had 316 risk managers, auditors, and senior executives rank 20 emerging threats in April and May, with AI discovery of cyber vulnerabilities ranked first, up from outside the top five the prior quarter. Respondents scored the impact time frame at 1.92 on a scale where 1 means under a year and 2 means one to two years, and 76% placed it in their top ten, ranking first in all four regions and highest among banking and financial respondents at 78%. The analysis notes AI now finds unknown flaws at volumes patching teams cannot absorb and that exploit development time has collapsed, citing defensive efforts like Anthropic's Project Glasswing and OpenAI's Daybreak. Gartner recommends recalibrating cyber risk impact, revisiting risk appetite, demanding stronger vendor security validation, and moving toward faster automated remediation.

Help Net SecurityAug 26, 00:00 UTC · 21d agoIndustry

Why AI raises the stakes for exposure validation

Fal.Con 2026 commentary argues AI accelerates vulnerability discovery and exploitation, making evidence-based exposure validation essential for defender prioritization.

CSO Online reports on the exposure-validation theme at CrowdStrike's Fal.Con 2026 conference, where CEO George Kurtz described AI as the new cyber battlefield and emphasized AI red teaming and continuous security. The piece argues that as AI speeds up vulnerability discovery and exploitability analysis on both sides, teams must determine which exposures are actually exploitable in their environments—chained weaknesses, credential abuse, lateral movement, privilege escalation—rather than chasing theoretical risk. It points readers to Horizon3's conference perspective.

CSO OnlineSep 11, 21:49 UTC · 4d agoIndustry

Robust Policy Optimization via Adversarial Importance Sampling

Adversarial Importance Sampling estimates worst-case RL returns without extra interactions; authors also release the advrl PyTorch library.

The paper introduces Advis, which uses importance sampling over trajectories from standard training to estimate and optimize verifiable worst-case returns, requiring no additional environment interactions or auxiliary networks. It also releases advrl, a modular PyTorch library of single-file robustness methods and adversarial attacks for reproducible evaluation. The authors show adversarial hyperparameters do not transfer across agents, so they evaluate with 6-14x more attacker configurations than prior work. Effectiveness is demonstrated on continuous control environments.

arXiv cs.AI / cs.LG / cs.CLSep 11, 16:40 UTC · 5d agoAI research1

Necessary or Sufficient? Evaluating LLM Explanations With Behavioural Evidence

New framework tests whether LLM-cited explanation factors are necessary or sufficient, finding weak correlation across Claude, GPT, and Gemini models.

An arXiv paper introduces black-box intervention tests measuring whether factors LLMs cite in their explanations are necessary or sufficient for their outputs in agent oversight workflows. Across eight models from the Claude, GPT, and Gemini families, Spearman correlations between cited rankings and measured influence ranged from 0.349-0.354 (advisor recommendation) to 0.431-0.580 (prompt monitoring). Uncited factors scored above the lowest cited factor in up to 57.6% of advisor responses, showing cited top-three factors do not reliably identify the most influential inputs.

arXiv cs.AI / cs.LG / cs.CLSep 4, 17:37 UTC · 12d agoAI safety & security1

Perturbation Probing: A New Diagnostic for the Fragility of LLM Safety

Unit 42 research shows LLM safety refusals concentrate in a thin neural layer, motivating external, multi-layered AI security controls.

Palo Alto Networks Unit 42 introduces Perturbation Probing, a diagnostic technique for measuring the fragility of LLM safety mechanisms. The research finds that safety refusal behavior is localized within a thin neural layer, implying small perturbations can undermine built-in refusals. The authors argue this motivates external, multi-layered security defenses on top of model-internal safety training.

Palo Alto Unit 42Aug 28, 22:00 UTC · 18d agoAI safety & security

Competence-Gated Pooling of Language Models and Priors for Event Forecasting

Paper proposes a competence gate pooling language model forecasts with external priors, improving Brier score from 0.0771 to 0.0732 across 2,357 binary questions.

The paper defines a language model's relative competence as its marginal value beyond an available external forecast, and derives conditions under Brier loss where model disagreement improves that forecast. A competence gate estimates domain-level source weights from resolved outcomes, shrinks uncertain estimates toward a global weight, and recalibrates the pooled forecast. Across 2,357 resolved binary questions and five language models, it improves the external baseline from 0.0771 to 0.0732 Brier and beats global forecast combinations, though it defers to the market on ForecastBench. Across four Qwen models, verbal confidence failed to identify when the model outperformed the external forecast, while outcome-estimated competence supported better abstention.

Hugging Face daily papersSep 9, 20:00 UTC · 6d agoAI research

Ask Before You Optimize: Dynamic Pre-Formulation Clarification for Interactive Optimization

Researchers release OR-Clarify, a benchmark testing whether LLM agents ask clarifying questions before formulating optimization models from incomplete requests.

OR-Clarify evaluates pre-formulation clarification in operations research: each task gives a partial problem description, withholds structured hidden slots, and scores agents via bounded interaction with a simulated user, measuring slot recovery, stopping behavior, silent assumptions, and interaction cost. The authors also propose InterOPT, a two-stage framework that identifies formulation-critical gaps to decide when to ask or stop. In choice-based experiments InterOPT substantially outperforms all baselines in exact slot recovery and remains competitive in the open-ended setting.

Hugging Face daily papersSep 3, 20:00 UTC · 12d agoAI research1

Evaluating Time-Series Foundation Models and Multimodal Dietary Context for CGM Forecasting

Study finds zero-shot time-series foundation models underperform on CGM forecasting; fine-tuned Chronos-Bolt cuts RMSE up to 18.4% and dietary context adds signal.

The paper evaluates time-series foundation models for continuous glucose monitoring forecasting across eight public datasets covering Type 1 diabetes, Type 2 diabetes, and non-diabetes populations. Under a unified protocol, zero-shot foundation models did not consistently outperform baselines like Elastic Net and PatchTST, but lightweight fine-tuning did, with fine-tuned Chronos-Bolt reducing RMSE by 6.5%-18.4% in the T1D cohort and 8.6%-18.2% in the non-diabetes/T2D cohort. A residual-based fusion framework adding dietary context from CGMacros reduced overall RMSE by about 3% and postprandial RMSE by about 15% versus CGM-only baselines.

arXiv cs.AI / cs.LG / cs.CLSep 10, 17:43 UTC · 6d agoAI research

From Reweighting to Rewriting: Unlocking the Intervention Effects of Influential Samples in Training Data Attribution

Study shows rewriting responses of influence-selected training examples shifts LLM behavior more strongly than reweighting the same samples.

The paper examines training data attribution, arguing that influence functions identify high-leverage examples whose value goes unrealized under conventional weight-based reweighting interventions. It introduces influence-guided response rewriting, which replaces the responses of influence-selected examples with behavior-aligned or behavior-opposed supervision while keeping instructions fixed, tested across four open-weight LLMs using epistemic abstention as the primary testbed. Rewriting produces stronger, more persistent, and bidirectional behavioral shifts, including on safety refusal, while reweighting the same examples yields weak, inconsistent effects. The results motivate intervention-aware evaluation of TDA methods.

Hugging Face daily papersSep 1, 20:00 UTC · 14d agoAI research

The State of Cloud Risk 2026: Most Security Findings Aren’t Real Attacker Opportunities

Wiz Research's 2026 cloud risk report finds most high-severity findings lack a viable path to compromise, urging exposure-based prioritization.

Wiz Research telemetry underpins a State of Cloud Risk 2026 report arguing that the majority of high-severity security findings are not real attacker opportunities because they lack a path to compromise. The report pushes defenders to prioritize findings by exposure and exploitability rather than raw severity scores. No specific CVEs, victims, or incident counts are provided in the available text.

Wiz BlogAug 26, 15:01 UTC · 21d agoResearch

CISOs are feeling the security burden of accelerated AI use

Proofpoint's Voice of the CISO survey finds 85% of CISOs prioritizing AI security, with 80% managing AI risks without proportional resources.

Proofpoint's annual Voice of the CISO report, a Censuswide survey of 1,600 CISOs across 16 countries, found 85% rank securing AI assistants, copilots and automation among top priorities for the next two years. Eight in ten say they must manage AI-related security risk without a proportional increase in resources or expertise, and 78% now consider generative AI a major security risk, up 18% year over year. Board alignment improved to 85%, though nearly 80% still report excessive board pressure; 80% cite human behavior as the biggest cyber vulnerability.

Proofpoint Threat InsightSep 9, 21:18 UTC · 6d agoIndustry

Data Scarcity and Model Sparsity: Mixtures-of-Experts Overfit More to Repeated Data

Study finds Mixture-of-Experts models overfit faster than dense Transformers under repeated training data, with degradation tied to total parameter sparsity.

Across models from 80M to 1B active parameters (8.5B total), MoE architectures degrade more rapidly than dense models when training data is repeated, with the effect increasing with sparsity as dictated by total parameters. Dense 80M models tolerate 8x repetition with minimal loss while MoEs suffer at 4x and underperform dense models beyond 32x. Masking-based regularization such as dropout mitigates overfitting, letting MoEs beat dense models even at over 64x repetition, though no method matches all-unique training data. Routing stabilizes early and expert specialization correlates with overfitting to repeated data.

arXiv cs.AI / cs.LG / cs.CLSep 10, 17:57 UTC · 6d agoAI research

Building a risk-based vulnerability management program that scales

Asimily CEO Shankar Somasundaram outlines a risk-based vulnerability management approach using inventory, attack paths, KEV and EPSS data.

In a Help Net Security video, Asimily CEO Shankar Somasundaram argues patching everything is infeasible as AI-driven attacks inflate vulnerability counts, with one customer finding a thousand unknowns for each known one. He recommends building a full inventory of devices, applications, and data flows, mapping attack paths for reachability, and prioritizing with KEV, EPSS, and business impact. Mitigations include patching, virtual patching via NACs and firewalls, segmentation, and configuration snapshots to detect drift.

Help Net SecurityAug 24, 04:21 UTC · 23d agoIndustry

CTEM Is Not About the Stages. It’s About the Outcome.

Horizon3 argues CTEM programs should measure continuously reduced exposure rather than mapping technologies to Gartner's five stages.

Horizon3 contends that Continuous Threat Exposure Management should be judged by one outcome: continuously reducing attacker-reachable exposure, not by mapping a technology to each of Gartner's five stages. The post argues validation and verification, not visibility or closed tickets, provide evidence that attack paths are actually broken. It describes a Discover, Validate, Prioritize, Remediate, Verify, Repeat motion as its operationalization of CTEM.

Horizon3.aiSep 2, 13:11 UTC · 14d agoIndustry

Safe Meta-Reinforcement Learning via Information Space Reachability

Safe meta-RL framework reasons about safety in information space, learning a safety value function used for safety filtering and constrained policy optimization.

The paper proposes safe meta-RL that reasons about safety in information space, capturing both physical state and the agent's belief over the underlying task. A safety value function measures the probability of avoiding unsafe regions indefinitely and satisfies a self-consistency condition and Bellman equation, making it learnable via meta-RL. The resulting algorithm uses the learned function for safety filtering and constrained policy optimization, with effectiveness demonstrated on meta-RL benchmarks.

arXiv cs.AI / cs.LG / cs.CLSep 14, 17:27 UTC · 2d agoAI research

AI adoption brings new security headaches for already stretched CISOs

Proofpoint's 2026 Voice of the CISO report finds 79% of CISOs must manage AI-related risks without added resources or expertise.

Proofpoint's 2026 Voice of the CISO report says AI governance is expanding CISO responsibilities faster than resources, with 79% expected to manage AI-related risks without proportional support. Seventy-eight percent of CISOs consider GenAI a security risk, chiefly customer data loss through public AI platforms, and 61% expect a targeted attack within 12 months, down from 76% in 2025. Human risk remains the top vulnerability for 79% of respondents, and 93% of organizations with material data loss said departing employees played a role. Cloud account takeover now tops perceived threats, while email fraud, ransomware and malware declined in the rankings.

Help Net SecuritySep 10, 04:30 UTC · 6d agoIndustry

Operational Resilience: IT Security Risks with Reduced Staffing | Huntress

Huntress blog advises security teams on managing change, risk, and incident response during reduced-staffing holiday periods.

The article discusses how holiday-period staffing reductions change organizational risk profiles around change management, monitoring, and incident response capability. It argues against blanket change freezes when critical vulnerabilities with high exploitation probability demand patching, and stresses retaining decision-making authority, escalation paths, and recovery knowledge. It concludes by promoting Huntress Managed Response, which lets the Huntress SOC take predefined containment actions on confirmed threats without customer intervention.

HuntressSep 15, 14:00 UTC · 1d agoIndustry

Understanding the Impact of Model Pruning on Long-Tail Forgetting and Explanation Reliability in Medical Imaging

Systematic study finds model pruning causes frequency-dependent long-tail forgetting in medical imaging and that gradient-informed methods best preserve explanations.

Across two long-tailed medical imaging datasets, two CNN architectures, four pruning methods, and sparsity up to 95%, the study measures predictive performance, explanation stability, and faithfulness. Rare classes degrade earlier and more severely than frequent ones, while explanation reliability depends mainly on the pruning strategy, with gradient-informed methods degrading least. Mechanistic analysis ties explanation collapse to loss of class-discriminative gradients rather than vanishing feature activations, recommending class- and explanation-aware evaluation of compression.

arXiv cs.AI / cs.LG / cs.CLSep 7, 17:44 UTC · 9d agoAI research

DynSHAP: Towards Explainable Dynamic Survival Analysis

DynSHAP extends SHAP explainability to dynamic survival analysis, treating time-feature pairs as Shapley players for longitudinal clinical predictions.

DynSHAP adapts marginal SHAP estimators to dynamic survival analysis by treating time-feature pairs as players in the Shapley game, handling longitudinal irregular inputs and functional survival outputs. Temporal DynSHAP learns linear feature dependencies over time and addresses them with conditional sampling. On synthetic data with ground-truth attributions it recovers temporally dependent features more accurately than marginal estimators, and it produces faithful attributions on two real-world clinical datasets across two DSA architectures.

arXiv cs.AI / cs.LG / cs.CLSep 11, 16:39 UTC · 5d agoAI research

AI the Top Priority for New Spend as Cyber Budgets Flatline

IANS survey of 500 US CISOs finds AI is the top priority for net-new security budget even as overall cybersecurity budgets stay flat.

IANS's 2026 Security Budget Benchmark Report, based on interviews with 500 US security executives, found AI was the most popular focus for incremental budget, cited by 69% of respondents. Software now accounts for 35% of the security budget in 2026, up from 29% last year, nearly matching staff and compensation. Overall median budget growth remained flat, with 55% keeping budgets flat or making cuts, while 71% of VC-backed companies increased security budgets versus 52% of public companies. Most CISOs (69%) do not expect AI to cut headcount, and 81% anticipate it creating demand for new roles and skills.

Infosecurity MagazineSep 15, 13:00 UTC · 1d agoIndustry

Your Critical Vulnerabilities Might Not Be Your Biggest Risk

Autonomous penetration testing advocates prioritize exploitable attack paths over raw vulnerability severity for continuous security validation.

The article argues that scanner severity scores lack context: a critical flaw behind strong segmentation may be low priority, while a medium flaw on internet-facing systems can provide a foothold chained toward sensitive data. It positions autonomous penetration testing and attack path validation as the execution layer for continuous security validation, replacing point-in-time assessments. The piece is vendor-authored thought leadership rather than incident or vulnerability news.

The Hacker NewsSep 11, 11:30 UTC · 5d agoIndustry1

Bridging Control, Inference, Transport, and Thermodynamics: From Theory to Applications in Learning

Review connects control theory, optimal transport, probabilistic inference, thermodynamics, and machine learning via free-energy optimization under constraints.

The review unifies five fields: control theory, optimal transport, probabilistic inference, non-equilibrium thermodynamics, and machine learning. The common conceptual thread is optimization of free-energy-like functionals under dynamical or statistical constraints. Selected applications are presented in reinforcement learning, variational inference, and generative modeling. The tutorial-style text assumes no prior familiarity and begins from physics principles.

arXiv cs.AI / cs.LG / cs.CLSep 14, 17:16 UTC · 2d agoAI research