DeepSeek Security Map: Three Major Armies, Who is Running?

1.Entropy based technology for overtaking the "power" at the entrance and exit: officially integrated with DeepSeek's large model, its self-developed "BioCV TinyML model" has been successfully adapted, achieving end-to-end AI upgrade, and launching an AI access control machine that integrates access control, audio and video, and AI scene recognition, supporting "voiceprint+face+semantic" triple authentication.

2.The dimension reduction of smart community "players" hits the fluorite: officially connected to the DeepSeek big model, realizing voice interaction upgrade, precise understanding of instructions in seconds, personification communication and other highlights, unlocking new gameplay of AI intelligent terminals.

3.The breakthrough of the AIoT "new aristocracy" cloud technology: the calmly large model training and push all-in-one machine has successfully adapted to DeepSeek, supporting out of the box private deployment. Its AI intelligent agent all-in-one machine comes pre installed with mainstream models such as DeepSeek, and the AI intelligent agent platform can be used out of the box. (The above companies are for reference only and incomplete statistics) Currently, top companies such as Hikvision, Dahua Corporation, and Uniview Technology have not officially announced their integration into DeepSeek. However, there are still many actions in the field of AI big models, such as the recent launch of Hikvision's multimodal big model and the release of its article search storage series products, which sends a signal of accelerated industry change.

Thinking about the AI Wave: AB Side and Survival Rules of the Security Revolution.

1,Behind the explosive growth of DeepSeek, the "fatal paradox" of security and efficiency, lies a sharp contradiction between data security and efficiency improvement. Wiz report shows that DeepSeek has also experienced privacy breaches. This reveals a cruel reality: if a company only pursues model access speed and ignores security infrastructure, it is like running naked on a "data gold mine". When the AI wave strikes, enterprises are prone to falling into the "efficiency first syndrome", hastily integrating into large models but having only a partial understanding of data anonymization and model permission management. In the future, customer claims caused by security vulnerabilities will surge, and we must be vigilant not to become the next industry cannon fodder.

2.The "jungle rule" of ecological niche has enabled leading companies to seize the ecological niche through differentiation strategies. The ecological competition in the security industry has shifted from 'single point breakthrough' to 'system warfare'. If small and medium-sized players continue to internalize hardware parameters, they will only become the 'packet foreman' of the ecosystem giants. The real opportunity may lie in finding niche scenarios that giants are unwilling to delve into, such as vertical categories such as campus perimeter protection and hazardous material monitoring in chemical parks, and building moats using vertical models and industry know how.

3.The "life and death game" of cost and value, DeepSeek's low-cost strategy has lowered the technological threshold, but intensified the industry price war. Taking the camera scenario as an example: Traditional engineering companies need to purchase a camera that supports local inference for 3000 yuan, while the cost of equipment integrating DeepSeek models can be compressed to 1800 yuan. The implicit investment costs of model fine-tuning, data annotation, security reinforcement, etc. have skyrocketed. When customers start demanding payment based on recognition accuracy, the traditional profit model collapses. Future quotations must be broken down into "hardware cost+AI service fee+security premium". Companies that do not include this account may not even qualify for price wars.

The Life or Death Decision of Security Engineering Companies: Transformation Roadmap and Avoiding Pits Guide 1 Technological upgrade: From "selling devices" to "selling intelligence", traditional engineering companies rely on hardware price differences for profit, but DeepSeek's integration requires it to have data cleaning and model fine-tuning capabilities. The demand for hardware+AI capabilities has emerged, embedding lightweight models in devices such as cameras and access control systems to support localized inference (such as license plate recognition and face comparison) and improve efficiency. 2. Service transformation: Security operation and maintenance has shifted from "retrospective analysis" to "pre analysis and prevention". The core competitiveness of future security engineering companies may lie in predictive maintenance. By analyzing device logs through DeepSeek, problems such as camera malfunctions and storage node overload can be predicted in advance based on video data. 3. Ecological cooperation: How many legions are bound or how many moats are built? Small and medium-sized engineering companies are facing difficulties. If they bind to the DeepSeek ecosystem, they can quickly gain AI capabilities but may lose bargaining power. It is recommended to start from segmented scenarios. For example, engineering companies focused on campus security can collaborate with education big models to develop customized risk assessment modules. In the quotation of future security engineering companies, there must be a line called 'AI operation and maintenance service fee'. If there is no data engineer in the team who understands model tuning, the customer may not even be able to give you the qualification to bid.