Skill UI

Guides security architects through designing and deploying a continuous CSPM program across AWS, Azure, and GCP, covering tool selection, policy baselines, automation, and SOC integration to monitor misconfigurations and prioritize remediation.

This tool deploys advanced machine learning techniques to detect anomalies within Operational Technology (OT) and Industrial Control Systems (ICS) environments. It establishes behavioral baselines by analyzing network traffic (Modbus, DNP3, OPC UA) and communication timing. Use cases include continuous monitoring, detecting zero-day deviations, and enhancing security monitoring platforms beyond standard signature-based intrusion detection.

Utilizes User and Entity Behavior Analytics (UEBA), statistical baselines, and machine learning models to monitor authentication logs. This skill identifies sophisticated security threats such as impossible travel, credential stuffing, brute force attacks, and compromised accounts in real-time. Essential for security operations and identity access management.

This skill provides methods and tools for detecting sophisticated cyber attacks targeting Supervisory Control and Data Acquisition (SCADA) and Industrial Control Systems (ICS). It focuses on detecting anomalies like unauthorized command injection, Man-in-the-Middle attacks on industrial protocols (Modbus, DNP3), and data manipulation, which traditional IT security tools often miss. Usage involves establishing deterministic communication baselines and monitoring network traffic for deviations.

This tool detects critical anomalies in DNP3 communications used within SCADA and ICS environments. It employs deep packet inspection and machine learning to monitor for unauthorized control commands, protocol violations, firmware update attempts, and deviations from established network baselines. It is essential for securing critical infrastructure, such as energy grids and industrial control networks, and building robust anomaly-based Intrusion Detection Systems (IDS).

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 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.

An advanced Intrusion Detection System (IDS) module designed for Operational Technology (OT) and ICS/SCADA environments. It monitors Modbus TCP/RTU traffic to detect unauthorized writes, anomalous function codes, malformed frames, and deviations from established communication baselines. Essential for investigating suspicious PLC register modifications and responding to industrial threats.

This skill guides the security hardening of Windows endpoints (10/11, Server 2019/2022) using established CIS Benchmarks. It provides a comprehensive workflow for establishing secure organizational baselines, configuring Group Policy Objects (GPOs), and enforcing security controls across critical areas like password policies, audit logs, and firewall rules. It is essential for achieving compliance with standards like PCI DSS, HIPAA, and SOC 2.

This tool analyzes historical network traffic baselines to design secure and functional segmentation architectures for Operational Technology (OT) environments. It helps map flat Purdue-model networks into secure zones (VLANs) and automatically generates required firewall rules based on observed legitimate communication paths, adhering to standards like IEC 62443. Ideal for IT/OT convergence and risk mitigation.

A comprehensive guide to implementing Kubernetes Pod Security Admission (PSA), a native admission controller stable since v1.25. PSA enforces Pod Security Standards (Privileged, Baseline, Restricted) at the namespace level, replacing the deprecated PodSecurityPolicy. Use this skill to harden Kubernetes environments, establish robust security baselines, and control pod deployment security using enforce, audit, or warn modes.

This skill guides security teams on systematically tuning SIEM detection rules in platforms like Splunk and Elastic. It focuses on reducing alert fatigue and false positives by analyzing historical alert volumes, establishing environmental baselines, creating context-aware whitelists, and measuring detection efficacy using metrics like precision and recall. This is crucial for maturing a Security Operations Center (SOC).