「promise」の検索結果

Detects anomalous authentication patterns using UEBA analytics, statistical baselines, and machine learning models to identify impossible travel, credential stuffing, brute force, password spraying, and compromised account behaviors across authentication logs. Activates for requests involving authentication anomaly detection, login behavior analysis, UEBA implementation, or suspicious sign-in investigation.

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。

Detect cyber attacks on OT historian servers (OSIsoft PI, Ignition, GE Proficy, Wonderware InSQL) using a Python detector that flags unauthorized queries, data manipulation, and lateral-movement indicators as historians pivot between IT and OT networks. Use when monitoring historians bridging IT/OT zones for compromise, investigating historian-specific CVE exploitation, or validating historian data integrity after a suspected OT incident.

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。

Perform Pass-the-Ticket (PtT) lateral movement by extracting Kerberos TGT/TGS tickets from LSASS memory on a compromised host and injecting them into another session to impersonate the ticket owner without knowing their password. Use during authorized post-exploitation to move laterally or escalate access via stolen Kerberos tickets, or to validate detections for Kerberos ticket theft and reuse.

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。

Build a systematic threat-hunt workflow that turns threat intelligence and ATT&CK gap analysis into testable hypotheses, then executes and validates them via EDR/SIEM queries (CrowdStrike, Defender, Splunk, Elastic, Sysmon, Velociraptor, Sigma) and documents findings in a standardized hunt report. Use when planning or running a proactive threat hunt or scoping compromise from an intel- or anomaly-driven lead.

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。

Respond to security incidents in AWS, Azure, and GCP via identity-based containment, cloud-native log analysis (CloudTrail, Azure Activity Logs, GCP Audit Logs), resource isolation, and forensic evidence acquisition adapted for ephemeral cloud infrastructure. Use when CSPM alerts or audit logs show compromised cloud credentials, unauthorized IAM changes, or a breach spanning cloud services.

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。

Build an automated pipeline that ingests raw IOCs (URLs, IPs, domains, emails), normalizes and deduplicates them, then produces defanged renderings for safe human reading alongside canonical STIX 2.1 bundles distributed via TAXII servers, MISP, or email reports. Use when preparing indicators of compromise for safe analyst sharing or automating threat intel distribution to TAXII/MISP feeds.

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。

Generate forensic super-timelines with Plaso's log2timeline.py, pinfo.py, psort.py, and psteal.py CLI tools (fusing file-system MACB, registry, EVTX, browser history, prefetch, LNK, and more), then triage and filter the results in Timesketch. Use when reconstructing the full sequence of events on a compromised or forensically imaged host during a DFIR investigation.

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。

Extract and analyze Windows Registry hives with tools like RegRipper and Registry Explorer to uncover user activity, installed software, autostart/persistence entries, and evidence of system compromise. Use when investigating registry-based persistence, reconstructing user or system activity, or performing DFIR triage on a Windows image.

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。

Scan Linux systems for persistence mechanisms including crontab/systemd entries, LD_PRELOAD injection, shell profile modifications (.bashrc, .profile), and SSH authorized_keys backdoors, then correlate findings with auditd logs into an installation timeline. Use during incident response or threat hunting to detect or confirm how an adversary maintained access to a compromised Linux host.

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。

Automates the enrichment of raw indicators of compromise with multi-source threat intelligence context using SOAR platforms, Python pipelines, or TIP playbooks to reduce analyst triage time and standardize enrichment outputs. Use when building automated enrichment workflows integrated with SIEM alerts, email submission pipelines, or bulk IOC processing from threat feeds. Activates for requests involving SOAR enrichment, Cortex XSOAR, Splunk SOAR, TheHive, Python enrichment pipelines, or automated IOC processing.

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。

Analyzes bootkit and advanced rootkit malware infecting the Master Boot Record (MBR), Volume Boot Record (VBR), or UEFI firmware for below-OS persistence, covering boot sector analysis, UEFI module inspection, and anti-rootkit detection. Use when compromise survives OS reinstallation or antivirus/EDR fails to detect malware despite clear infection signs.

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。

Analyze malicious Linux ELF binaries — botnets, cryptominers, ransomware, and rootkits targeting Linux servers, containers, and cloud infrastructure — through static analysis, dynamic tracing, and reverse engineering of x86_64 and ARM samples. Use when investigating Linux malware, triaging a suspicious ELF binary, assessing a compromised Linux server, or analyzing container-targeted malware.

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。

Parses Kubernetes API server audit logs (JSON lines) to detect exec-into-pod, secret access, RBAC modifications, privileged pod creation, and anonymous API access, and builds SIEM detection rules from the event patterns. Use when investigating a suspected cluster compromise, reconstructing what an attacker did through the API server, or writing Kubernetes-specific detection content. Keywords: audit policy, audit log, kube-apiserver, exec into pod, RBAC change, anonymous access, detection rules. Do not use for syscall-level detection inside a running container - use detecting-container-runtime-threats-with-falco. '

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。

Use Sysinternals Autoruns to systematically enumerate and analyze malware persistence mechanisms across Windows registry run keys, scheduled tasks, services, drivers, and startup locations. Use when hunting for persistence during Windows incident response, triaging a compromised endpoint, or validating that malware autostart entries have been fully identified and removed.

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。

Performs Linux memory acquisition using LiME (Linux Memory Extractor) kernel module and analysis with Volatility 3 framework. Extracts process lists, network connections, bash history, loaded kernel modules, and injected code from Linux memory images. Use when performing incident response on compromised Linux systems.

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。

Analyzes RAM memory dumps from compromised systems using the Volatility framework to identify malicious processes, injected code, network connections, loaded modules, and extracted credentials. Supports Windows, Linux, and macOS memory forensics. Activates for requests involving memory forensics, RAM analysis, volatile data examination, process injection detection, or memory-resident malware investigation.

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。

Investigate compromised Docker containers by analyzing images, layers, volumes, logs, and runtime artifacts to identify malicious activity and evidence.

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。

Draft a reply to your newest Gmail email that knows what you discussed in your Granola meetings, what you promised the sender, and how you write, using cognee memory. Use when someone asks to answer or reply to their latest email in their own tone. Runs locally; reads Gmail read-only; the draft is printed, never sent.

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。

Set up and operate the Gmail/Calendar/Contacts connector and the open-loop engine: who is waiting on the user, what they promised, and the context needed to respond. Covers painless BYO OAuth setup (exactly two user interactions), the daily `gbrain waiting` digest, loop closing/muting, and troubleshooting via the typed error catalog.

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。

Zoom Meeting SDK Web - Component View. Embeddable Zoom meeting components with Promise-based API for flexible integration. Ideal for React/Vue/Angular apps and custom layouts. Uses ZoomMtgEmbedded with async/await patterns and embeddable UI containers.

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。

Zoom Meeting SDK for Web - Embed Zoom meeting capabilities into web applications. Two integration options: Client View (full-page, familiar Zoom UI) and Component View (embeddable, Promise-based API). Includes SharedArrayBuffer setup for HD video, gallery view, and virtual backgrounds.

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。

Reads a forwarded customer email or ticket, pulls order and refund status from a payments connector (PayPal, Square, or Stripe) or Shopify, account history from the CRM, and open tickets from a support desk (Zoho Desk), drafts a tone-matched reply in the owner's writing voice, and can issue a refund through the payments connector with explicit owner approval. With Shopify connected it also runs a proactive order-triage mode that surfaces orders needing attention — unfulfilled past the promised window, payment problems, pending refunds, stuck shipments — and drafts the next action for each before the customer has to ask. Use when the user says "draft a response," "answer this customer," "where's my order," "I want a refund," "check my orders," or "anything about to blow up."

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。

Use when creating or modifying any .mjs file in the Handsontable monorepo - scripts, utilities, or library modules. Covers .mjs conventions, native node: imports, top-level await, cross-platform compatibility, and fs/promises async patterns. Trigger on any new .mjs file creation, not just files in scripts/ directories.

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。

Find explicit promises and deadlines in selected work history and reconcile their latest status.

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。