Rising Anxiety Over Zero-Day Attacks and Limited Staff Capacity

As part of its cybersecurity program, Chuo Spring deployed an endpoint protection platform (EPP/EDR) across employee devices and upgraded its firewalls from Layer 4 to Layer 7. These measures formed the core of its perimeter defenses, and the company reinforced this multi-layered approach by using a web proxy to further minimize exposure to external threats. Even so, ransomware and zero-day attacks made it increasingly clear that existing controls were not enough.

Operationally, the security team faced a persistent workload issue. The various perimeter systems produced an overwhelming volume of logs, and limited staffing meant those logs often went unanalyzed for long periods. The environment also included devices on which EDR agents could not be deployed, creating blind spots that perimeter defenses alone could not cover. Compounding the problem, many of the existing controls relied on signature-based detection, which offered little protection against novel attack vectors or zero-day exploits.

Confronted with these limitations, the company recognized that continuing to bolt on additional point solutions would not resolve its underlying visibility gaps. Instead, it began evaluating network detection and response (NDR) platforms that could deliver unified monitoring and end-to-end visibility across internal communications and device activity. The objective was to understand all inbound and outbound traffic flows, whether at the perimeter or deep inside the network. Believing that Darktrace’s self-learning, AI-driven approach could help offset its staffing constraints, Chuo Spring launched a proof of value (POV) covering roughly 2,400 IP-connected devices across its on-premises infrastructure.

An NDR Platform That Learns the Network’s Daily Patterns and Makes Them Fully Visible

Chuo Spring chose Darktrace / NETWORK as its NDR solution because it takes a fundamentally different approach from traditional perimeter-based defenses. Delivered as an integrated hardware and software appliance, the platform does not rely on predefined rules or signatures, nor does it require network redesign or architectural maintenance. Instead, Darktrace’s Self-Learning AI continuously models the normal behaviors and communication patterns of users and devices across diverse digital infrastructures. By establishing a clear baseline of what is considered normal, the system enables Chuo Spring to autonomously detect and contain cyber threats in real time whenever activity deviates from expected patterns. The platform also features Cyber AI Analyst, the first technology capable of automatically investigating detected threats and producing Japanese-language reports within seconds.

In evaluating Darktrace / NETWORK, Chuo Spring was particularly drawn to the simplicity of its machine learning approach. The appliance connects directly to a core network switch and uses port mirroring to ingest header information from all traffic flowing between endpoints and servers. By analyzing packet destination, timing, traffic volume, and communication frequency, the AI builds and continuously updates behavioral profiles for each user, device, and subnet. Any deviation from these learned baselines, whether an unknown external threat or potential insider activity, is immediately detected and surfaced, providing the visibility the organization was seeking.

At Chuo Spring, the system also operates in autonomous response mode, issuing reset packets to disrupt high-risk connections on a 24/7 basis.

Real-Time Detection and Triage Powered by AI

Chuo Spring operates Darktrace products with a threshold set and to date the system has not identified any serious threats that could indicate data leakage or disrupt business operations. During the proof-of-value period, however, Darktrace / NETWORK flagged several lower-level anomalies in real time. This included SMB communication initiated from one PC to another on the internal LAN, an employee exporting data to external storage, and traffic that suggested potential command-and-control activity.

Under the company’s previous perimeter-based tools, these events would appear only as logs that required manual deep-dive analysis to understand what was happening. Darktrace, by contrast, autonomously and immediately alerts on activity that deviates from normal communication patterns, and its AI triages alerts based on their severity. This has significantly reduced the effort required to identify abnormal behavior and determine root causes, while giving the security team greater confidence that threats are being addressed comprehensively.

Reducing SOC Costs with Automated AI Reporting

Cyber AI Analyst, the automated investigation engine built into Darktrace / NETWORK, takes this even further. The technology is trained on years of analyst decision-making, capturing millions of investigative patterns to replicate how human experts work. When an alert is triggered, the AI rapidly performs the equivalent of an expert-level analysis, identifies causal relationships, and generates written reports in multiple languages, including Japanese. These AI-driven reports can be exported to PDF with a single click if desired.

By automating both threat detection and the generation of Japanese-language reports, (With the ability to automate not only detection but also objective, Japanese-language reporting,) Chuo Spring has been able to maintain round-the-clock monitoring and investigation without building an internal SOC or outsourcing to a third-party provider. This capability effectively eliminates the additional costs associated with standing up a staffed security operations function.

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