Tier0 Sentinel

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Overview

Tier0 designed and implemented Security Information Event Management (SIEM) solution by incorporating Microsoft Sentinel.

Detailed Description

Data from various sources of different clients will be ingested into centralized repository called Log Analytic Workspace(LAW). Sentinel will use built-in KQL queries of enabled analytics rules.

Sentinel uses LAW as its backend for storing events, various resources logs and other information. For Tier0 clients, Sentinel should be pre-configured in client's subscription.

Once the security event is ingested, based on the analysis of the security event data, Sentinel can generate alerts and trigger automated responses. If a security event matches predefined detection rules, correlations, or behavioral patterns, an alert is raised. The alert may include relevant details about the event, severity, and recommended actions. These alerts contribute to incident creation and management within Sentinel.

Objective

• Threat detection and response
  Azure Sentinel helps in detecting and analyzing security events and incidents across the SGS-Tier0's infrastructure. It aggregates and correlates data from various sources, including Azure services, on-premises systems, and third-party tools, to identify potential threats and malicious activities.
• Security orchestration and automation
  Azure Sentinel enables security automation by providing built-in and customizable playbooks. These playbooks automate common security tasks and response actions, allowing SGS-Tier0 to respond quickly and efficiently to security incidents.
• Incident investigation and hunting
  Azure Sentinel provides advanced query and analysis capabilities to investigate and hunt for security incidents. It allows security teams to search and analyze large volumes of data in real-time, making it easier to identify indicators of compromise and understand the scope and impact of security incidents.
• Threat intelligence and analytics
  Azure Sentinel integrates with external threat intelligence sources, such as Microsoft Threat Intelligence and other industry feeds, to provide context and enrichment to security events. It leverages machine learning and artificial intelligence to identify patterns and anomalies, helping in the early detection of sophisticated threats.

Limitations of SIEM tools

• False Positives
  SIEM tools can produce false-positive alerts, which are security alerts triggered by events or activities that appear to be malicious but are actually benign. False positives can waste valuable time and resources, leading to alert fatigue and diverting attention from actual security threats.
• Complex Deployment and Configuration
  Implementing and configuring a SIEM solution can be complex, especially when dealing with a large-scale infrastructure. SGS-Tier0 may face challenges in integrating the SIEM tool with different systems, devices, and applications, resulting in delays and increased implementation costs.
• Incomplete Data Collection
  SIEM tools rely on collecting data from various sources, such as network devices, servers, and applications. However, some data sources may not be compatible or supported by the SIEM tool, leading to incomplete data collection. This can result in blind spots and gaps in security monitoring and analysis.
• Limited Scalability
  As SGS-Tier0 grow and their security needs evolve, SIEM tools may struggle to scale and handle large volumes of security data. Inadequate scalability can lead to performance issues, delays in data processing, and increased costs to upgrade or replace the SIEM infrastructure.
• Lack of Context and Actionable Insights
  SIEM tools often generate alerts and events without providing sufficient context or actionable insights. Security analysts may need to spend additional time investigating and correlating data from different sources to gain a comprehensive understanding of the security incident. This can slow down incident response and hinder effective decision-making.
• Integration Challenges
  Integrating a SIEM tool with existing security solutions and technologies can be challenging. SGS-Tier0 may encounter compatibility issues, lack of standardized formats for data exchange, and difficulties in synchronizing data and workflows between different security tools. These integration challenges can impede the effectiveness and efficiency of the overall security operations.

Benefits and Business Case

In everyday business operations of any application, various challenges arise, including

• Data overload
  SGS-Tier0 generate vast amounts of security-related data from multiple sources, making it challenging to detect and respond to security threats effectively. Azure Sentinel addresses this problem by collecting, analyzing, and correlating data from diverse sources, providing a unified view of security events.
• Complexity and scalability
  Managing security at scale can be complex, especially in hybrid and multi-cloud environments. Azure Sentinel simplifies security operations by providing a cloud-native, scalable solution that integrates with Azure services and supports data ingestion from a wide range of sources.
• Lack of visibility
  Without a centralized security monitoring and analytics solution, SGS-Tier0 may lack visibility into security events and incidents across their infrastructure. Azure Sentinel offers a unified view of security data, enabling SGS-Tier0 to detect and respond to threats more effectively.
• Manual and time-consuming processes
  Many security operations involve manual tasks and processes, which can be time-consuming and error-prone. Azure Sentinel automates security tasks through playbooks and enables security teams to focus on high-value activities, such as threat hunting and incident response.

To address these concers and more, Tier0 sentinel offers a variety of usage scenarios such as:

• Log Collection
  
  To collect, detect, investigate and respond to security threats and incidents by making use of analytic rules which are built on custom tables by creating relevant custom queries , automation rules and playbooks deployed via Sentinel AnalyticsRules Automation Framework on a customer basis. Thus, delivering intelligent security analytics and threat intelligence across the multiple client’s ecosystem.
  
  
• Data Visualization
  
  To create workbooks for each client which will enable you to visualize and analyze security data from Sentinel in a customized and interactive manner.
  
  
• Custom Dashboards
  
  To build custom dashboards for each client in Sentinel using workbooks. You can create multiple pages within a workbook, each containing different visualizations and reports. Dashboards provide a consolidated view of security data and insights, allowing you to monitor key security metrics and incidents at a glance.
  
  
• Ad-hoc Data Analysis
  
  Workbooks enable ad-hoc data analysis by providing query capabilities. You can use the Log Analytics Query Language (KQL) to query and filter security data in real-time. This allows you to drill down into specific events, perform data exploration, and investigate security incidents directly within the workbook.
  
  
• Sharing and Collaboration
  
  Workbooks can be shared with other users or groups within your SGS-Tier0. You can grant different levels of access, such as view-only or edit permissions, to control collaboration. Sharing workbooks facilitates knowledge sharing, collaborative analysis, and enables team members to benefit from pre-built visualizations and reports.
  

Responsibilities

Initial Setup

• Client has to submit the required information for the initial setup. All the required details are already captured as part of onboarding under centralized logging section.

Tier 0/ Support Team

• The configured alert should trigger as expected.
• The alert email should be received on client provided email address without any delay.

Client/ Platform Team

• No action required from client's end.