「siem」の検索結果

Deploy Microsoft Sentinel as a cloud-native SIEM/SOAR by configuring multi-cloud data connectors (AWS, Azure, GCP), writing KQL detection and hunting queries, and building automated Logic Apps response playbooks. Use when establishing a centralized SOC for multi-cloud environments, migrating from a legacy SIEM, or performing petabyte-scale threat hunting; not for AWS-only setups where Security Hub/GuardDuty suffice or for endpoint EDR needs.

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Security log parsing, anomaly detection, SIEM query building, Sigma rule creation, and correlation rule development across Splunk, Elastic, QRadar, and Microsoft Sentinel

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Engineer and audit SIEM detection rules — log source coverage, Sigma / KQL / SPL / Elastic query authoring, MITRE ATT&CK mapping, false-positive tuning, and detection-as-code workflows. Use when the user mentions 'SIEM,' 'detection engineering,' 'detection rules,' 'Sigma,' 'KQL,' 'SPL,' 'Splunk,' 'Sentinel,' 'Elastic,' 'Wazuh,' 'Chronicle,' 'detection-as-code,' 'MITRE ATT&CK mapping,' 'log coverage,' 'alert tuning,' 'use case development,' or needs help building or improving security detections.

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Implements technical breach detection capabilities including SIEM integration, DLP alert configuration, anomaly detection rules, and insider threat monitoring. Provides a breach classification taxonomy across confidentiality, integrity, and availability dimensions. Covers detection tool selection, alert tuning, and integration with privacy incident response workflows. Keywords: breach detection, SIEM, DLP, anomaly detection, insider threat, classification.

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Converts hunt findings into portable Sigma detection rules: choosing the right logsource, expressing robust selection/filter logic, setting level and ATT&CK tags, and validating before converting to a SIEM query. Activates for requests to write a Sigma rule, convert a hunt to a detection, or create portable SIEM detection logic.

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This skill covers deploying Microsoft Sentinel as a cloud-native SIEM and SOAR platform for centralized security operations. It details configuring data connectors for multi-cloud log ingestion, writing KQL detection queries, building automated response playbooks with Logic Apps, and leveraging the Sentinel data lake for petabyte-scale threat hunting across AWS, Azure, and GCP security telemetry.

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bu skill covers Dağıt:ing Microsoft Sentinel as a cloud-native SIEM and SOAR platform for centralized security operations. It details configuring data connectors for multi-cloud log ingestion, writing KQL Tespit queries, building automated response playbooks with Logic Apps, and leveraging the Sentinel data lake for petabyte-scale threat hunting across AWS, Azure, and GCP security telemetry.

日本語の概要は準備中です。原文の説明を表示しています。

This skill covers deploying Microsoft Sentinel as a cloud-native SIEM and SOAR platform for centralized security operations. It details configuring data connectors for multi-cloud log ingestion, writing KQL detection queries, building automated response playbooks with Logic Apps, and leveraging the Sentinel data lake for petabyte-scale threat hunting across AWS, Azure, and GCP security telemetry.

日本語の概要は準備中です。原文の説明を表示しています。

This skill covers deploying Microsoft Sentinel as a cloud-native SIEM and SOAR platform for centralized security operations. It details configuring data connectors for multi-cloud log ingestion, writing KQL detection queries, building automated response playbooks with Logic Apps, and leveraging the Sentinel data lake for petabyte-scale threat hunting across AWS, Azure, and GCP security telemetry.

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Hunt for suspicious PowerShell execution (T1059.001) such as encoded commands, download cradles, AMSI bypass, and constrained language mode evasion using EDR telemetry (CrowdStrike, Microsoft Defender for Endpoint), Sysmon, and SIEM queries (Splunk, Elastic). Use when proactively threat hunting, triaging EDR/SIEM alerts, or scoping an incident involving malicious PowerShell activity.

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Detect RDP brute force attacks by parsing Windows Security Event Logs (EVTX files, via python-evtx) for failed logon patterns (Event ID 4625, Logon Type 10/3), correlating with successful logons (Event ID 4624), and analyzing NLA failures and source IP frequency. Use when investigating exposed RDP endpoints, building SIEM detection rules for credential guessing, or confirming whether a compromised account followed a brute-force pattern.

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Identifies lateral movement techniques in enterprise networks by analyzing authentication logs, network flows, SMB traffic, and RDP sessions using Zeek, Velociraptor, and SIEM correlation rules to detect attackers moving between systems.

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Detect adversary lateral movement across networks using Splunk SPL queries against Windows authentication logs, SMB traffic, and remote service (WMI/PsExec/RDP) abuse. Use when hunting for MITRE ATT&CK TA0008 lateral movement activity or investigating suspected pivoting between hosts during an incident, with Splunk as the SIEM.

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Correlates security events in IBM QRadar SIEM using AQL (Ariel Query Language), custom rules, building blocks, and offense management to detect multi-stage attacks across network, endpoint, and application log sources. Use when SOC analysts need to investigate QRadar offenses, build correlation rules, or tune detection logic for reducing false positives.

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Builds vendor-agnostic detection rules using the Sigma rule format for threat detection across SIEM platforms including Splunk, Elastic, and Microsoft Sentinel. Use when creating portable detection logic from threat intelligence, mapping rules to MITRE ATT&CK techniques, or converting community Sigma rules into platform-specific queries using sigmac or pySigma backends.

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Deploy MISP via Docker and configure feeds from sources like abuse.ch, AlienVault OTX, and CIRCL to aggregate, correlate, and distribute threat intelligence, including automated feed synchronization and STIX/TAXII-based integration with Splunk, Elasticsearch, and SOAR platforms. Use when standing up centralized IOC management or wiring multi-source threat feeds into a SIEM.

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Builds automated threat intelligence feed integration pipelines connecting STIX/TAXII feeds, open-source threat intel, and commercial TI platforms into SIEM and security tools for real-time IOC matching and alerting. Use when SOC teams need to operationalize threat intelligence by automating feed ingestion, normalization, scoring, and distribution to detection systems.

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Leverages Splunk Enterprise Security and SPL (Search Processing Language) to investigate security incidents through log correlation, timeline reconstruction, and anomaly detection. Covers Windows event logs, firewall logs, proxy logs, and authentication data analysis. Activates for requests involving Splunk investigation, SPL queries, SIEM log analysis, security event correlation, or log-based incident investigation.

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Use this skill when the user is deploying or operating the DOCA Argus Service — the packaged BlueField-side runtime-security container that watches the BlueField and attached host for suspicious activity, integrity violations, and operational anomalies, and forwards findings to a SIEM (Splunk / ELK / Sentinel / syslog). Covers the four-axis config (detection policy, forwarding, sampling, host coverage), running the NGC container on BlueField Arm, and wiring the forwarder. Trigger even without "DOCA Argus" by name — typical implicit phrasings: "container green but no findings arrive", "false-positive flood in Splunk", or "runtime security on a fleet of BlueField-3s". Refuse and route elsewhere for installing DOCA, SIEM-side ingest stanzas, pre-baked detection-rule packs, and metrics observability (DOCA Telemetry). Argus is NVIDIA's currently- promoted runtime-security framework, superseding the older App Shield library; name it first for new runtime-security work.

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Configure security information and event management (SIEM) systems for threat detection, log aggregation, and compliance. Use when implementing centralized security logging, writing detection rules, or meeting audit requirements across cloud and on-premise infrastructure.

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供应链与生产运营 (供应链与生产运营 (Supply Chain & Production Operations) — 面向制造业与消费品实体供应链的『计划-制造-交付』运营判断,不是采购谈价技巧,也不是跨境电商发货物流。覆盖: (a) 第一性张力 — 这行的核心世界观分歧: **流动 > 局部效率(用有效产出/流动时间看系统,而不是用设备利用率/人均产出看工位;非瓶颈上的效率是幻觉) ⇄ 精益拉动 vs 推式预测(TPS/JIT 按下游消耗拉动、小批量平准化 vs MRP 按预测展开净需求推动;DDMRP 解耦缓冲是第三条路) ⇄ 精益/丰田 TPS 派 vs 约束理论 TOC 派(消除七大浪费、全局改善 vs 五步聚焦瓶颈、鼓-缓冲-绳 DBR 排产;两派互相指责对方浪费时间/只治标) ⇄ 变异是敌人(Deming/六西格玛/Factory Physics 的 VUT 排队论: 利用率越靠近满载排队时间越爆炸,交期长不是产能不够而是变异+高负荷) ⇄ 库存是负债 vs 库存是缓冲(会计把库存记成资产、精益视其为掩盖问题的水位、TOC 与韧性派视其为吸收变异的必要缓冲;真问题是缓冲放在哪一层而不是有没有) ⇄ 精益 JIT vs 韧性冗余(2020-2023 断链后 just-in-case、多源、近岸/友岸的回摆;精益派反驳崩的是『假精益』——单一来源+砍库存不砍变异) ⇄ 服务水平 vs 库存成本(安全库存/报童模型/ABC-XYZ 的经典取舍,牛鞭效应放大上游波动) ⇄ 成本导向压价 vs 供应商共生开发(Kraljic 分级、丰田供应商协会式长期共生 vs 年降指标与竞价) ⇄ 一个数 vs 各部门各算各的(S&OP/IBP 的本质是跨职能对齐到一套数字与一份产销计划))**; (b) 工具栈(此行的工具是『系统+方法』的混合) — ERP/MRP(SAP S/4HANA PP·MM、Oracle、用友·金蝶、Odoo、NetSuite)、APS 高级计划排程(Kinaxis RapidResponse、o9、Blue Yonder、SAP IBP、OMP、Siemens Opcenter APS)、MES/车间执行(Siemens Opcenter、Rockwell FactoryTalk、黑湖智造)、WMS/TMS、需求预测(统计模型 + Nixtla StatsForecast/Prophet、FVA 预测增值分析)、运筹优化与仿真(OR-Tools、Gurobi、Pyomo、AnyLogic、FlexSim、SimPy)、供应链风险与多级可视(Resilinc、Everstream、Interos)、分析层(SQL/Python pandas/Power BI)、以及纸笔工具(价值流图 VSM、A3、看板、标准作业票)—— 选型决策树的关键分岔是 MTS/MTO/ATO/ETO 履行模式与需求变异度,而非厂商功能清单; (c) 心智模型层 — 利特尔法则(在制品 = 产出率 × 流动时间)、瓶颈与非瓶颈、缓冲三态(库存/时间/产能缓冲)、解耦点(客户订单渗透点 CODP)、牛鞭效应、takt time 节拍与产线平衡、OEE 与六大损失、muda·mura·muri 三种浪费、自働化 jidoka 与安灯、防错 poka-yoke、SMED 快速换模换出小批量、平准化 heijunka、PDCA 与丰田改善型 Toyota Kata、DMAIC 与 Cpk 过程能力、报童模型与安全库存公式、ABC-XYZ 分级; (d) 工作流/SOP — 跑一轮月度 S&OP(需求评审→供应评审→预 S&OP→执行 S&OP)、做需求预测与预测误差复盘(MAPE/WMAPE/bias/FVA)、设定库存分级与安全库存、MRP 跑批与例外消息处理(缺料齐套与 MRP 神经质)、瓶颈识别与 DBR 排产、价值流图现状图→未来图→改善计划、新品导入 NPI 与多级 BOM/ECN 工程变更、供应商选择与开发(RFQ→审厂→APQP/PPAP→量产→年度绩效)、断料应急与交期承诺(ATP/CTP)、循环盘点与库存准确率; (e) 知识正典(en/ja 一手极厚,中译齐全) — 《目标 The Goal》Goldratt(TOC 小说体奠基)、《丰田生产方式》大野耐一、《改变世界的机器》Womack·Jones·Roos(MIT IMVP 研究)、《精益思想 Lean Thinking》Womack & Jones、《丰田模式 The Toyota Way》Liker、《工厂物理学 Factory Physics》Hopp & Spearman(排队论视角的运营科学)、《学习观察 Learning to See》Rother & Shook(VSM)、《丰田套路 Toyota Kata》Rother、Spear & Bowen 的 HBR 1999《Decoding the DNA of the Toyota Production System》、Lee/Padmanabhan/Whang 1997《The Bullwhip Effect in Supply Chains》、Ptak & Smith 的 DDMRP、Orlicky 的 MRP、Chopra & Meindl 的供应链管理教材、Deming《转危为安 Out of the Crisis》、Sheffi《The Resilient Enterprise》、ASCM/APICS 的 SCOR 参考模型与 CPIM/CSCP 知识体系; (f) 行业大佬/figures — 大野耐一(TPS 立法者)、新乡重夫(SMED/防错)、Eliyahu Goldratt(TOC 创立者)、W. Edwards Deming(变异与系统观)、James Womack & Daniel Jones(把 TPS 译成西方可学的『精益』)、Jeffrey Liker(丰田模式学术化)、Mike Rother(VSM 与改善型)、Wallace Hopp & Mark Spearman(工厂物理学)、Hau Lee(斯坦福,牛鞭效应与 Triple-A 供应链)、Yossi Sheffi(MIT,韧性)、Carol Ptak & Chad Smith(DDMRP)、David Simchi-Levi(MIT,风险敞口指数)、Steven Spear(TPS 的 DNA)、以及中文圈的供应链实务作者(刘宝红等); (g) 争议/批判(必须保留不洗白) — JIT 是不是 2021 全球断链的元凶(精益派反驳:崩的是单一来源+砍库存不砍变异的『假精益』);西方学精益的高失败率(只搬工具不搬思维,精益在很多工厂沦为裁员的代号);六西格玛黑带官僚化与扼杀创新之争;TOC 派与精益派互相攻击(Goldratt 讽刺精益是等浪费自己浮现);预测无用论(需求驱动派说预测必然错、要靠缓冲吸收)vs 统计预测派;控制塔/数字孪生/AI 预测的过度承诺与高项目失败率;中国制造的年降与内卷式压价 vs 供应商共生;近岸·友岸外包是真实产能转移还是政治叙事; (h) 流派/思想谱系 — **精益/TPS 派(丰田血统:大野-新乡→Womack/Jones/Liker/Rother,信全局消除浪费与人的改善能力)** vs **约束理论 TOC 派(Goldratt:系统产出只由瓶颈决定,聚焦五步、DBR、缓冲管理)** vs **统计质量/六西格玛派(Deming-Juran-Shewhart→GE/Motorola,信减少变异与过程能力)** vs **运营科学/工厂物理学派(Hopp & Spearman:用排队论与数学定律解释,认为精益与 TOC 都是特例)** vs **需求驱动/DDMRP 派(Ptak & Smith:预测不可靠,靠战略解耦缓冲)** vs **韧性/风险派(Sheffi、Simchi-Levi:效率之外必须为断链设计)**。不含: 跨境电商卖家的发货与头程物流(已由 cross-border-ecommerce 覆盖)、纯采购的商务谈判技巧、企业级 ERP 的代码实施与二次开发、通用项目管理。) Master OS — automated mastery of 供应链与生产运营 (Supply Chain & Production Operations) — 面向制造业与消费品实体供应链的『计划-制造-交付』运营判断,不是采购谈价技巧,也不是跨境电商发货物流。覆盖: (a) 第一性张力 — 这行的核心世界观分歧: **流动 > 局部效率(用有效产出/流动时间看系统,而不是用设备利用率/人均产出看工位;非瓶颈上的效率是幻觉) ⇄ 精益拉动 vs 推式预测(TPS/JIT 按下游消耗拉动、小批量平准化 vs MRP 按预测展开净需求推动;DDMRP 解耦缓冲是第三条路) ⇄ 精益/丰田 TPS 派 vs 约束理论 TOC 派(消除七大浪费、全局改善 vs 五步聚焦瓶颈、鼓-缓冲-绳 DBR 排产;两派互相指责对方浪费时间/只治标) ⇄ 变异是敌人(Deming/六西格玛/Factory Physics 的 VUT 排队论: 利用率越靠近满载排队时间越爆炸,交期长不是产能不够而是变异+高负荷) ⇄ 库存是负债 vs 库存是缓冲(会计把库存记成资产、精益视其为掩盖问题的水位、TOC 与韧性派视其为吸收变异的必要缓冲;真问题是缓冲放在哪一层而不是有没有) ⇄ 精益 JIT vs 韧性冗余(2020-2023 断链后

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Design detection rules for a threat — SIEM queries, alert logic, and MITRE ATT&CK mapping. Use when asked to "write a detection rule", "how do we detect this attack", or "build a SIEM query for this TTP".

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Identifies lateral movement techniques in enterprise networks by analyzing authentication logs, network flows, SMB traffic, and RDP sessions using Zeek, Velociraptor, and SIEM correlation rules to detect attackers moving between systems.

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Correlates security events in IBM QRadar SIEM using AQL (Ariel Query Language), custom rules, building blocks, and offense management to detect multi-stage attacks across network, endpoint, and application log sources. Use when SOC analysts need to investigate QRadar offenses, build correlation rules, or tune detection logic for reducing false positives.

日本語の概要は準備中です。原文の説明を表示しています。