Geely's Dilemma | Geely Auto's 'Fission'

Sedans form the bedrock of every automotive brand’s strength. From the Borui—born at a time when Chinese domestic-brand sedans were weakest—to the Emgrand family, which steadfastly held its ground in the dark days and successfully carved an opening for Chinese auto brands in the sedan market, to the introduction of the Binrui, which became Geely’s new weapon for winning over young consumers… Geely’s sedan portfolio is undeniably robust. The SUV market is no different. In the RMB 100,000 segment, Geely has already deployed multiple models—including the Vision X6, Bin Yue, Emgrand GS, and Vision S1. In 2020, Geely launched the ICON as a strategic move to further segment the SUV market. Distinct from existing model families such as the Bo-series and Bin-series, the ICON marked Geely’s exploration into a new generation of product lineups. This vast product matrix, coupled with strong product competitiveness, has sustained Geely’s position as China’s top-selling domestic automotive brand. Yet, the excessive density of models has gradually blurred the boundaries among sub-brands—eroding their individual identities. The rapid rollout of new products directly strains marketing rhythm and resource allocation, inevitably leading to stretched capacity. Does this accelerated pace leave sufficient buffer time for suppliers? Must dealers confront mounting inventory pressure month after month? These are critical questions Geely must continually address. An Conghui, President of Geely Holding Group and CEO & President of Geely Auto Group, has repeatedly emphasized: Chinese domestic brands do not lack vehicle models—they lack star models. Only through persistent, generational iteration can influence and reputation be built. However, an overly complex product lineup and excessively overlapping price points easily lead to internal competition during sales, resulting in inefficiency and undermining the development of breakout products and brand equity. Continuously overlapping price bands also challenge the stability of Geely’s pricing system. For instance, the Emgrand evolved from a single model into the Emgrand family—encompassing the Emgrand, Emgrand GL, and Emgrand GS—penetrating multiple segments and boosting market share, becoming Geely’s de facto sales anchor. As the Emgrand family matures, it stands poised to become Geely’s flagship ‘star model’. Yet, the long-serving Emgrand—now entering its tenth year—has ‘passively’ reached an inflection point of declining core competitiveness amid the emergence of newer models like the Binrui and ICON. As China’s longest-running sedan model among domestic brands, the Emgrand has surpassed nearly ten years on the market, with cumulative sales approaching three million units. Its success was never effortless. Yet while launching one successful model is relatively straightforward, building a successful model family is exceedingly difficult. Balancing inter-family dynamics within the Emgrand lineup—and avoiding cannibalization—is Geely’s most thorny challenge in cultivating the ‘Emgrand family’. Data from March shows the Emgrand dropped out of the top ten sedan sales rankings; the Lavida and Sylphy reclaimed the top two spots, and all top-ten positions were occupied by joint-venture brands—an unmistakable warning signal. The Emgrand’s crisis is Geely’s dilemma—and a test of Geely’s 2-million-unit annual sales ecosystem. Moreover, Lynk & Co—still solidifying its premium brand image—rushed to launch new products to capture greater market share. Yet due to overlapping launch cycles and order backlogs causing internal sales cannibalization, Geely and Lynk & Co. received a sobering lesson. Every grand vision needs dazzling global automotive stars to illuminate its brilliance.

Application of Twin-Based Deception Networks in Deception Defense

Currently, deploying traditional honeypots on-site involves high operational complexity: network and business characteristics must first be surveyed onsite; simulation strategies must be manually customized; and extensive on-site tuning is required—often demanding considerable time and advanced technical expertise. Onsite service costs frequently exceed the product’s purchase price itself. Furthermore, as customers’ networks and business systems evolve, continuous manual maintenance and upgrades remain necessary. Twin-based deception networks integrate cyber-space reconnaissance devices and attack-surface management systems, leveraging digital twin technology, pattern recognition, NFV (Network Functions Virtualization), and SDN (Software-Defined Networking) to fit and simulate templates—enabling rapid, automated deployment of honeynets tailored precisely to business-system characteristics, value, and enterprise-wide attack surfaces. Simultaneously, the honeynet system supports human-in-the-loop optimization and features dynamic self-adaptation: when networks or business systems change or upgrade, the system automatically adjusts accordingly. Compared with traditional honeypots, twin-based deception networks improve deployment and application efficiency by over 300%, effectively resolving the ‘last-mile’ bottleneck hindering honeypot adoption. Optimizing Security Investment ROI Traditional honeypots require deploying numerous simulation nodes to maximize deception capability; higher-fidelity simulations (e.g., high-interaction emulation) demand significantly more resources and drive up costs sharply. Due to budget constraints, customers often deploy undersized honeypot networks—resulting in poor detection efficacy. By contrast, twin-based deception networks are first constructed based on customers’ network-business profiles and internal/external attack surfaces and attack paths—rather than simply layering onto the network infrastructure. This approach drastically reduces resource consumption per simulated object without compromising simulation performance. Second, twin-based deception networks integrate threat intelligence, security situational awareness, attack-surface analysis, and XDR systems to gather real-time security status data across the customer’s network, enabling dynamic risk assessment. Combined with honeynet detection results, the system dynamically adjusts individual honeynet units: upgrading or expanding high-fidelity simulation units for high-risk or mission-critical business systems, while downgrading non-critical scenarios to medium- or low-interaction lures—or even passive bait—minimizing hardware resource usage and maximizing overall honeynet system effectiveness. Compared with traditional honeypots delivering equivalent performance, twin-based deception networks cost one-third less. Significantly Enhancing Operational Efficiency Twin-based deception networks pioneer a full-lifecycle, zero-touch operations paradigm—covering installation/deployment, day-to-day operation, and scaling/upgrades. During initial configuration, the system establishes a digital twin of the business network and supports visual, graphical interface editing. During operations, it automatically generates standardized threat intelligence feeds; produces and orchestrates rules for IDS and APT-detection systems; generates and coordinates defensive policies for firewalls (FW), web application firewalls (WAF), endpoint detection and response (EDR), and zero-trust network access (ZTNA) solutions; and develops attack models for integration with AI-powered situational awareness platforms. When customers’ networks or business systems change or upgrade, the system adapts dynamically and autonomously. Compared with traditional honeypots, twin-based deception networks boost operational efficiency by over 200%, substantially improving both detection accuracy and operational efficiency across security operations. Honeypot technology has matured from a niche technical tool into a commercialized product. Yet current market adoption remains severely constrained: heavy reliance on manual intervention and substantial hardware resources drives actual implementation costs prohibitively high. Twin-based deception networks leverage AI to advance intelligent, automated configuration and operations—making proactive strides toward seamless integration with existing security operations ecosystems. This helps overcome the awkward reality where active defense systems have historically functioned merely as peripheral add-ons to broader security operations frameworks. Looking ahead, active defense systems will evolve into a core technological pillar of security operations—playing pivotal roles in threat intelligence utilization, false-positive elimination, real-time response, and AI-driven analytics across critical security operations functions.

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