Results for “monitoring”

Monitors dark web forums, marketplaces, paste sites, and ransomware leak sites for mentions of organizational assets, leaked credentials, threatened attacks, and threat actor communications to provide early warning intelligence. Use when establishing dark web monitoring coverage, investigating specific data breach claims, or enriching incident investigations with dark web context. Activates for requests involving dark web OSINT, leak site monitoring, credential exposure, Recorded Future dark web, or Tor hidden service intelligence.

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Dark web intelligence collection methodology — vendor-first access posture, sourced reference lists for 35+ underground forums and 30+ Telegram channels, OPSEC primer, passive-monitoring strategy, and bundled Python CLIs for onion-indexer search, Telegram channel monitoring, and local keyword matching. Use when the user wants to design or run dark-web collection, build a selector list, pick a vendor, or set up monitoring infrastructure.

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Use when monitoring, diagnosing, or managing Batch Apex, Scheduled Apex, Queueable, and Flow scheduled jobs: Setup > Apex Jobs, AsyncApexJob queries, concurrent limits, failure detection, and notification patterns. NOT for writing the Batch or Schedulable class itself — use apex/batch-apex-patterns or apex/apex-scheduled-jobs. NOT for BatchApexErrorEvent alerting in code — use apex/scheduled-apex-failure-detection-and-monitoring. Trigger keywords: Setup > Scheduled Jobs, Apex Flex Queue, CronTrigger, CronJobDetail, FlowInterview, System.abortJob, System.schedule, CRON expression, Holding status, BLOCKED state, reschedule after sandbox refresh, scheduled job runbook.

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When the user wants to monitor brand mentions, detect trademark infringement, or set up brand monitoring. Also use when the user mentions "brand monitoring," "brand watch," "trademark watch," "brand mentions," "impersonation detection," "counterfeit detection," or "brand abuse monitoring." For enforcement, use brand-protection.

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Suggests either continuous glucose monitor (CGM) or self-monitoring of blood glucose (SMBG) for pregnant individuals with type 2 diabetes. Triggers include decisions about glucose monitoring methods for pregnant patients with T2DM needing a monitoring strategy.

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Deploys and configures osquery for real-time endpoint monitoring using SQL-based queries to inspect running processes, open ports, installed software, and system configuration. Use when building visibility into endpoint state, threat hunting across fleet, or implementing compliance monitoring. Activates for requests involving osquery deployment, endpoint visibility, fleet management, or SQL-based endpoint querying.

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Configures host-based intrusion detection systems (HIDS) to monitor endpoint file integrity, system calls, and configuration changes for security violations. Use when deploying OSSEC, Wazuh, or AIDE for endpoint monitoring, building file integrity monitoring (FIM) policies, or meeting compliance requirements for change detection. Activates for requests involving HIDS configuration, file integrity monitoring, OSSEC/Wazuh deployment, or host-based detection.

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ISTQB/JSTQB のテストモニタリング&コントロール（test monitoring and control）活動のうち、モニタリングの「基盤構築」を支援するスキル。進捗・カバレッジ・失敗率・flaky 率・成果物トレーサビリティ（リスク→条件カバー率・条件→ケース化率）などのメトリクスを test-plan の完了基準と紐づけて定義し、プロジェクトの CI/ツール構成を調査した上で計測基盤（CI 設定・集計スクリプト・バッジ等）の実装を支援して test-monitoring.md と実装物を作る。「テストのメトリクスを決めたい」「CI でテスト進捗を可視化したい」「カバレッジや失敗率を計測する仕組みを作りたい」「トレーサビリティを継続計測したい」と依頼されたときに使う。**AI が継続的なモニタリングを代行することはしない（計測は基盤が行う）。集まったデータの分析・傾向判断は対象外**。単発のテスト作成依頼（単にユニットテストを書きたいだけ）も対象外。/test-monitor <テスト対象名> で明示的に呼び出されたときのみ使用する。

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Deploys and configures osquery for real-time endpoint monitoring using SQL-based queries to inspect running processes, open ports, installed software, and system configuration. Use when building visibility into endpoint state, threat hunting across fleet, or implementing compliance monitoring. Activates for requests involving osquery deployment, endpoint visibility, fleet management, or SQL-based endpoint querying.

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Configures host-based intrusion detection systems (HIDS) to monitor endpoint file integrity, system calls, and configuration changes for security violations. Use when deploying OSSEC, Wazuh, or AIDE for endpoint monitoring, building file integrity monitoring (FIM) policies, or meeting compliance requirements for change detection. Activates for requests involving HIDS configuration, file integrity monitoring, OSSEC/Wazuh deployment, or host-based detection.

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Implement comprehensive monitoring, logging, metrics, tracing, and alerting for production applications to ensure reliability and quick incident response. Use when setting up application monitoring, implementing structured logging, creating metrics and dashboards, setting up alerts, implementing distributed tracing, monitoring performance, tracking errors, or building observability into applications.

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When the user wants to monitor brand mentions, detect trademark infringement, or set up brand monitoring. Also use when the user mentions "brand monitoring," "brand watch," "trademark watch," "brand mentions," "impersonation detection," "counterfeit detection," or "brand abuse monitoring." For enforcement, use brand-protection.

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Deploys and configures osquery for real-time endpoint monitoring using SQL-based queries to inspect running processes, open ports, installed software, and system configuration. Use when building visibility into endpoint state, threat hunting across fleet, or implementing compliance monitoring. Activates for requests involving osquery deployment, endpoint visibility, fleet management, or SQL-based endpoint querying.

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Configures host-based intrusion detection systems (HIDS) to monitor endpoint file integrity, system calls, and configuration changes for security violations. Use when deploying OSSEC, Wazuh, or AIDE for endpoint monitoring, building file integrity monitoring (FIM) policies, or meeting compliance requirements for change detection. Activates for requests involving HIDS configuration, file integrity monitoring, OSSEC/Wazuh deployment, or host-based detection.

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Plan and review machine learning systems for production use, covering data requirements, repeatable training, quality checks, deployment, monitoring, and rollback. Produce concrete plans and review findings for moving model experiments into operational systems.

• Turning notebook experiments into reusable training pipelines
• Defining quality checks before releasing a model
• Investigating data leakage and training–serving mismatches

Create monitoring dashboards for Grafana, SigNoz, and similar platforms. Choose metrics around operational questions, organize panels by purpose, and define clear titles, units, filters, and thresholds to help operators assess health and bottlenecks.

• Build a Kafka monitoring dashboard
• Monitor Elasticsearch health and performance
• Track API gateway latency and errors

Prepare a production release with checks for code quality, security, performance, accessibility, and infrastructure. Plan monitoring, gradual feature exposure, and rollback steps, then use release metrics and reliability limits to guide rollout decisions.

• Check readiness for a first production release
• Plan a gradual rollout for a major feature
• Define monitoring and post-launch checks

Calculate portfolio risk and performance measures, including potential losses, declines from past peaks, and returns relative to risk. Use multiple indicators to support risk limits, position sizing, and ongoing monitoring.

• Measuring portfolio loss risk
• Comparing returns relative to risk
• Setting risk limits and position sizes

Design monitoring for industrial attacks that alter controller programs while hiding changes with false sensor readings. Compare PLC program blocks against known-good copies and check physical measurements against process models to identify suspicious inconsistencies.

• Monitoring industrial controller programs for unauthorized changes
• Investigating discrepancies between sensor readings and equipment behavior
• Planning detection across IT and industrial control systems

Monitor requests to an AI prediction API for signs of model copying or training-data exposure. Test an authorized model with simulated attacks, measure the risk, and evaluate defenses such as request limits and reduced prediction detail.

• Monitoring a public or partner prediction API
• Measuring model copying risk before deployment
• Testing request limits and reduced prediction detail

Analyze DNS logs to find signs of hidden data transfers. Use query length, character randomness, traffic volume, and response sizes to identify suspicious domains and support monitoring rules with Python, Suricata, and Splunk examples.

• Investigating possible data theft through DNS traffic
• Ranking suspicious domains found in DNS logs
• Creating DNS tunneling detection rules

Monitor local network traffic for ARP spoofing, where false address mappings can redirect traffic. Use ARPWatch, Wireshark, switch inspection settings, and Python monitoring to flag address changes, duplicate IP claims, gateway impersonation, and unusual ARP volumes.

• Investigating suspected interception on a local network
• Monitoring changes in IP-to-MAC address mappings
• Checking duplicate IP claims and gateway impersonation

Deploy and operate Falco to monitor running containers in Kubernetes or Docker. Choose a driver, validate detection rules, confirm alerts for suspicious activity, and forward those alerts to a central security monitoring system.

• Installing Falco on a Kubernetes cluster
• Choosing a driver for the Linux environment
• Checking alerts for suspicious container activity

Deploy Suricata to detect suspicious network traffic or block it inline. Configure protocol inspection, detection rules, and structured EVE JSON logs, then connect logs to analysis platforms and check packet drops and alert quality during operation.

• Deploying network intrusion monitoring
• Configuring inline blocking of malicious traffic
• Adding custom detection rules and reducing noisy alerts