The rapidly changing digital environment and accelerated cloud migration are increasing business efficiency for enterprises while simultaneously raising the complexity and frequency of cyber threats to unprecedented levels. Attack techniques are becoming more sophisticated, and it's difficult to effectively respond to these threats with traditional perimeter-based security strategies alone. Against this backdrop, many enterprises face the dual challenge of a shortage of skilled security professionals and limited budgets, keenly realizing the need for advanced 24/7 security operations systems.
Modern Enterprise Security Environment: New Scenarios and Goals
A mid-sized IT service company recently completed a large-scale infrastructure migration to a cloud environment. As this company actively adopted SaaS-based collaboration tools and expanded its remote work environment, it began to experience limitations with its existing on-premise-centric security operations system in gaining visibility across distributed environments and detecting threats. Furthermore, as its services expanded to handle various customer data, the importance of complying with regulatory requirements such as the Personal Information Protection Act and ISO 27001 increased. The company's core objective was to ensure business continuity by real-time detection and rapid response to pervasive threats across cloud, on-premise, and endpoint environments. Through this, they aimed to strengthen customer trust and demonstrate regulatory compliance capabilities.
In summary, this company had the goals of strengthening comprehensive threat detection and response capabilities in a complex hybrid cloud environment, and meeting heightened compliance requirements.
Limitations of Traditional Security Operations and Faced Challenges
This company previously performed security operations primarily by collecting and analyzing logs and generating rule-based alarms using a SIEM solution. However, due to the dynamic nature of cloud services and the adoption of microservice architectures, the vast volume of logs generated often exceeded the processing capacity of their existing SIEM. Furthermore, unknown Zero-day attacks or highly clandestine APT (Advanced Persistent Threat) attacks were difficult to identify with simple rule-based detection. The security operations team experienced significant staff burnout due to excessive false positive alarm processing and manual analysis tasks, lacking in-depth analysis and rapid response capabilities for truly critical threats. In particular, gaining visibility into threats caused by misconfigurations or API misuse in cloud environments was a very challenging task. These issues ultimately led to delayed threat detection and response times, increasing the risk of potential security incidents.
In short, traditional security operations failed to cope with the complexity of infrastructure and the sophistication of threats, revealing clear limitations in terms of human resources and operational efficiency.
MDR and XDR: A Comparative Analysis of Next-Generation Threat Detection and Response Solutions
To address the challenges faced, the organization reviewed various next-generation security solutions. The main candidates were MDR (Managed Detection and Response) and XDR (Extended Detection and Response). First, let's examine the core concepts of both solutions.
- MDR (Managed Detection and Response): MDR is a service where a professional security service provider monitors and detects threats occurring in various customer environments such as endpoints, networks, and clouds 24/7, and provides rapid response and recovery measures based on this. It leverages the expertise of security professionals and the latest threat intelligence to solve the internal security staffing shortage and helps organizations focus on critical threats by reducing false positives.
- XDR (Extended Detection and Response): XDR is a platform-based solution that collects and integrates data from various security layers within an organization, including endpoints (EDR), networks (NDR), cloud (CDR), email, and identity, for unified analysis. It utilizes correlation analysis and artificial intelligence/machine learning (AI/ML) technologies to holistically detect threats and supports automated responses, maximizing the efficiency of security operations. XDR is often provided as a single-vendor solution, offering the advantage of integrated management.
The main characteristics of the two solutions are compared below:
| Category | MDR | XDR |
|---|---|---|
| Solution Type | Service-centric | Platform-centric |
| Detection Scope | Extensive, including endpoints, networks, cloud (varies depending on service provider capabilities) | Integrated across endpoints, networks, cloud, email, identity, etc. |
| Key Strengths | 24/7 monitoring and rapid response by security professionals, leverage of threat intelligence | Extensive integrated data analysis, AI/ML-based automated detection and response |
| Required Resources | Reduced internal security staffing burden (service outsourcing) | Requires internal security professionals and resources for platform operation |
| Flexibility | Easy interoperability and integration with existing security solutions | Optimized integrated provision in a single-vendor environment |
The organization prioritized the staffing shortage issue and desired flexible scalability through integration with various existing security solutions (SIEM, EDR, etc.). They determined that securing in-depth analysis and real-time response capabilities for advanced threats with the help of external experts was crucial. Considering these factors comprehensively, they decided to adopt MDR as a Service. In particular, the KYRA MDR service, which provides AI-based threat detection and analysis capabilities, received high evaluations. KYRA MDR integrates with Seekurity SIEM/SOAR/XDR to enable integrated visibility and automated responses, while also offering threat hunting services by skilled security professionals, with the expectation that this would elevate the organization's security posture.
In summary, to compensate for internal staffing limitations and secure professional threat response capabilities, they chose MDR as a Service, specifically the AI-based KYRA MDR.
Implementing an Integrated Security Operations System Using KYRA MDR
1. Requirements Analysis and Initial Architecture Design
First, a detailed investigation of the current IT infrastructure environment (on-premise servers, cloud infrastructure, endpoint devices) was conducted. The types and volume of logs to be collected, and the possibility of integration with existing security solutions (e.g., EDR, firewalls, IPS) were meticulously analyzed. Based on this, an initial architecture was designed, including strategies for deploying log collection agents to integrate with the KYRA MDR service, and methods for API integration with cloud environments. To compensate for deficiencies in cloud security settings, identifying security vulnerabilities and compliance violations in the cloud environment beforehand through FRIIM CNAPP/CSPM/CWPP solutions, and establishing a secure baseline environment were also considered important preliminary tasks.
2. Data Collection and Integrated Platform Construction
Next, the step of collecting data from various sources was carried out. Lightweight agents were installed on on-premise servers and endpoints to configure the transmission of data such as system logs, network traffic, and process activities to the KYRA MDR platform. In cloud environments (primarily AWS, Azure), they adopted a method of directly integrating services like CloudTrail, VPC Flow Logs, and Azure Activity Logs with the KYRA MDR platform, or integrating them into Seekurity SIEM before forwarding. During this process, security protocols like mTLS (mutual Transport Layer Security) were applied to ensure all data was encrypted and transmitted securely. All data flows into Seekurity SIEM, undergoes normalization and parsing, and is then stored in an integrated manner for utilization by KYRA MDR's professional security analysis system.
3. Building a KYRA AI Sandbox-based Threat Detection and Analysis System
Now, based on the collected data, the core capability of KYRA MDR, an AI-based threat detection and analysis system, was built. KYRA AI Sandbox plays a decisive role in executing unknown files or URLs in a virtual environment to analyze malicious behavior and identify Zero-day attacks or sophisticated threats in advance. Furthermore, the system was optimized to effectively identify complex attack scenarios that are difficult to detect through single events, by leveraging threat modeling based on the MITRE ATT&CK framework and Seekurity SIEM's advanced correlation analysis capabilities. Concurrently, Seekurity SOAR was used to automate initial responses to specific detection rules (e.g., blocking malicious IPs, isolating user accounts), reducing the security team's workload.
4. 24/7 Monitoring and Response Playbook Optimization
Finally, a collaboration system with KYRA MDR's professional security operations team was established. Together, they developed customized response playbooks considering the organization's characteristics and business criticality. For instance, it was detailed which alert channels to use for reporting to whom, and what initial actions to take if a specific type of threat was detected. Moreover, the effectiveness of the response playbooks was verified and continuously improved through regular simulated drills. The KYRA MDR team monitors threats 24/7, promptly shares analysis results upon threat occurrence, and, if necessary, supports remote response actions to minimize the organization's security gaps.
In summary, an integrated security operations system was successfully implemented through a multi-stage approach, ranging from requirements analysis and data integration to AI-based detection system construction and collaboration with a professional monitoring team.
Enhanced Security and Increased Operational Efficiency Through Implementation
After KYRA MDR adoption, the organization's security posture showed the following quantitative and qualitative achievements:
| Category | Before MDR Implementation | After MDR Implementation | Improvement Effect |
|---|---|---|---|
| Threat Detection Time (Mean Time To Detect) | Average several hours ~ several days | Average several minutes ~ several hours | Reduced by over 90% |
| Threat Response Time (Mean Time To Respond) | Average several days | Average several hours | Reduced by over 80% |
| Security Operations Team False Positive Handling Time | Over 20 hours per week | Less than 5 hours per week | Reduced by over 75% |
| Burden of Securing Expert Security Personnel | High | Low (utilizing external services) | Substantially alleviated |
In addition to quantitative figures, several qualitative achievements were gained. First, visibility into the cloud environment significantly improved, strengthening detection capabilities for misconfigurations or unauthorized access attempts. Furthermore, the KYRA AI Sandbox enhanced defense capabilities against new types of malware and sophisticated attacks that were previously difficult to detect. The security operations team could shift away from repetitive alarm handling to focus on critical threat analysis and improvement activities, leading to increased job satisfaction and efficiency. Additionally, continuous provision of professional threat intelligence and information on the latest attack trends from the KYRA MDR service had the collateral effect of raising the overall security awareness level within the organization. Finally, the strengthened security framework demonstrated compliance capabilities, earning higher trust from customers and partners.
In short, the adoption of KYRA MDR dramatically increased the speed and accuracy of threat detection and response, contributing to both operational efficiency and professional expertise in security.
Lessons Learned from MDR Adoption and Future-Oriented Security Strategy
Several important lessons were learned during the KYRA MDR adoption process. First, the initial data collection and integration process faced more difficulties than anticipated due to diverse system environments and log formats. It was confirmed that meticulous prior analysis considering each system's characteristics and the establishment of a data normalization strategy were essential. Second, close collaboration with the MDR service provider and establishing clear communication channels were key to successful operation. Regular meetings and feedback exchanges were crucial to reflect the organization's characteristics and needs in the service and continuously refine the response playbooks. If this project were to be conducted again, more time and resources would be allocated to data governance and log standardization in the initial phase.
An unexpected side effect was the strengthening of the internal security team's capabilities. Collaboration with KYRA MDR experts provided an opportunity for internal team members to acquire practical knowledge of the latest threat trends and analysis techniques, and to learn advanced usage of Seekurity SIEM/SOAR. This contributed significantly to the long-term capability building of the team, beyond just threat response. For a future-oriented security strategy, it is crucial to adopt specialized services like MDR/XDR, enhance security across the entire cloud environment lifecycle with cloud security solutions such as FRIIM CNAPP/CSPM/CWPP, and continuously develop AI-based threat analysis capabilities through KYRA AI Sandbox.
In summary, it was realized that MDR adoption is not merely a solution implementation but a journey that fosters the organization's security culture and capabilities simultaneously.
Practical Guide for Successful MDR/XDR Adoption
Here are some practical tips for other organizations in similar situations to successfully adopt MDR or XDR solutions. First, it is crucial to objectively assess the organization's current security maturity and clearly understand internal staffing capabilities and budget constraints. If there is a severe shortage of personnel and 24/7 monitoring is difficult, MDR as a Service can be an effective alternative. Essential prerequisites include establishing an inventory of all IT assets and formulating basic security policies (e.g., patch management, access control). The scope of data collection and retention policies must be established by clearly defining data collection targets and thoroughly reviewing relevant regulatory requirements such as GDPR or personal information protection laws.
Establishing a phased implementation roadmap is also important. Rather than applying MDR/XDR services to all environments from the outset, prioritizing core systems or areas deemed most vulnerable and gradually expanding the scope enables a stable transition. Furthermore, a clear SLA (Service Level Agreement) with the service provider must be defined, and regular reporting and feedback processes established to continuously manage service quality. AI-based solutions like SeekersLab's KYRA MDR may require an initial learning period, so it is important to plan sufficient time accordingly. Through these practical approaches, organizations can build a robust defense system to more effectively respond to new security threats.
In conclusion, through systematic preparation and the strategic utilization of specialized solutions, organizations can concurrently ensure business continuity and security trustworthiness within an evolving cyber threat landscape.