Skill UI

Hindsight is an open-source tool designed for digital forensics, parsing artifacts from various Chromium-based browsers (Chrome, Edge, Brave, etc.). It extracts and correlates all web activity data, including browsing history, cookies, cached content, downloads, and saved credentials, into a unified timeline. This tool is essential for incident response, insider threat investigations, and reconstructing user web activity in security and legal contexts.

This tool correlates multiple system artifacts, including Windows registry keys (USBSTOR, MountedDevices), Event Logs, and SetupAPI logs, to reconstruct a detailed timeline of USB device usage. It determines first/last connection timestamps, tracks device provenance, and maps drive letter assignments. Essential for digital forensics investigations, insider threat detection, and verifying compliance regarding removable media usage.

This skill analyzes AWS CloudTrail logs to detect suspicious API call patterns, such as new event sources, geographic anomalies, or high-frequency usage. It establishes a statistical baseline of normal activity and flags deviations indicative of compromised credentials, privilege escalation, or insider threats, providing a detailed JSON report for security investigation and threat hunting.

This skill provides structured procedures and Python examples for detecting internal data exfiltration. It analyzes endpoint, cloud, and email logs by establishing behavioral baselines and performing statistical anomaly detection. Use cases include investigating insider threats, monitoring DLP policy violations, and building robust User and Entity Behavior Analytics (UEBA) rules for SOC teams.

This guide provides a structured methodology for proactively detecting and hunting various insider threat behaviors. It focuses on identifying indicators such as unusual data access, off-hours activity, mass file downloads, and privilege misuse. It is crucial for threat-hunting during incident response or investigating departed employees for potential data theft.

This skill implements User and Entity Behavior Analytics (UEBA) using Elasticsearch/OpenSearch. It goes beyond static rules to build behavioral baselines (e.g., login times, data volume) for users and entities. By calculating anomaly scores and performing peer group analysis, it detects suspicious activities such as data exfiltration, privilege abuse, and unauthorized access, allowing organizations to proactively identify high-risk insider threats.

This skill provides SOC teams with a structured, multi-source workflow to investigate potential insider threats. It correlates data from SIEM, DLP, EDR, and HR feeds to detect data exfiltration attempts (via cloud, USB, email), unauthorized access patterns, and policy violations. It is crucial for building comprehensive investigation timelines, especially when handling departing employees or suspected data theft.

This comprehensive guide outlines a structured workflow for investigating insider threats—incidents where authorized employees or contractors misuse their access to steal data or sabotage systems. It integrates DLP alerts, User Behavior Analytics (UBA), and digital forensics, emphasizing mandatory collaboration between Legal, HR, and Security teams to build an admissible, evidence-based case.

A comprehensive toolset for digital forensics, designed to detect, extract, and analyze covert data hidden within images, audio, and video files. It utilizes advanced steganalysis techniques (LSB, DCT, frequency domain analysis) and specialized tools (StegDetect, zsteg, binwalk) to uncover hidden communications channels, making it crucial for investigations involving data exfiltration or insider threats.

This skill performs User and Entity Behavior Analytics (UEBA) to identify anomalous user activities such as impossible travel, unusual access patterns, and privilege abuse. It establishes behavioral baselines using SIEM logs and statistical analysis, helping SOC teams detect insider threats and compromised accounts by flagging deviations from normal user behavior. Use when monitoring security hygiene and suspicious activity is critical.

A comprehensive guide to deploying decoy artifacts, such as fake credentials, URLs, and files, called honeytokens or canarytokens. These tokens have no legitimate operational use; therefore, any interaction with them provides a high-fidelity, low-false-positive signal of an intrusion, data theft, or insider threat. Use this technique to enhance detection in low-telemetry areas like file shares or credential stores.