With the rapid development of generative AI technologies, powerful large language models like DeepSeek-R1 are at the forefront of innovation. These models, with their exceptional capabilities, bring unprecedented opportunities for enterprises and developers.
However, when the DeepSeek-R1 model exposed a “thinking bias” of 14.3% in the Vectara HHEM AI hallucination test, the entire generative AI industry heard the alarm bells of safety ringing. This test was like a double-edged sword, showcasing the brilliance of open-source models in technological innovation while exposing the hidden security concerns in enterprise-level deployments—just as a tech geek stated at a Silicon Valley forum: “AI hallucinations are like dark matter in the digital age, invisible yet capable of causing systemic collapse.”
Security Offensive and Defensive Battle: The “Achilles’ Heel” of Enterprise Deployment
According to media reports, during testing with banks, a certain fintech platform occasionally encountered the “reactive” effects brought by DeepSeek’s powerful reasoning capabilities. For example, DeepSeek might “speak for itself” and fabricate certain business conditions of an enterprise, affecting the accuracy of the bank’s credit decision-making.
This confirms that the hallucination problem of generative AI is far from a “minor cold” on a technical level; it is a potential “digital earthquake” that could shake the foundation of enterprises.
The hallucination problem, where the content generated by the model is inconsistent with the original evidence, is a significant challenge that must be faced in enterprise-level AI deployments. The unique “hallucination” issues of large language models have continuously produced incidents that mislead customers and even violate public order and morals, leading to public opinion crises.
In addition, enterprises also need to consider other potential security risks, including misleading information, data privacy breaches, etc. When a model “fills in the blanks” with missing data, it may output incorrect decision-making suggestions or trigger compliance red lines—this uncertainty is becoming a sword of Damocles that restricts enterprise AI transformation.
For open-source models like DeepSeek-R1, how to effectively reduce the hallucination rate while maintaining its powerful capabilities and overcoming other security risks has become a hurdle for enterprises.
Triple Security Defense: From Passive Defense to Active Immunization
To address a series of challenges, including hallucination issues, Amazon Cloud Tech has launched a trilogy of generative AI security measures aimed at helping enterprise users deploy and apply generative AI models like DeepSeek safely and efficiently. These measures include basic security protection, harmful information filtering protection, and robust deep defense strategies.
Infrastructure Protection: Building a Digital “Bulletproof Vest”
Amazon Cloud Tech provides comprehensive security features through services and tools like Amazon Bedrock to ensure the secure hosting and operation of open-source models. These features include encryption of static and in-transit data, fine-grained access control, secure connection options, and various compliance certifications. In addition, Amazon Cloud Tech scans all model containers for vulnerabilities and only accepts models in SafeTensors format to prevent unsafe code execution.
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Through encryption technology, achieve data “invisibility,” ensuring sensitive information remains encrypted during static storage and dynamic transmission.
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Permission Control: Build a fine-grained access control system to ensure the right people access the right content and ensure that every operation leaves a traceable “digital footprint.”
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Container Sanitization: Use AI-driven vulnerability scanning systems to conduct “physical examinations” of model containers, eliminating security risks at the embryonic stage.
Intelligent Filtering Network: Building a Cognitive “Firewall”
The Amazon Bedrock Guardrails security protection feature provides a powerful filtering mechanism for model inputs and outputs. This includes content filtering, topic filtering, vocabulary filtering, sensitive information filtering, and context-based checks, among others, adjustable filtering strength categories for harmful content, limiting specific topics to prevent unauthorized topics from appearing in queries and responses, blocking specific vocabularies, and preventing personal information inquiries.
It acts like “immune cells” in the digital world, accurately identifying 98.7% of harmful information.
The automated reasoning function in the security protection barrier can achieve a “thinking perspective” effect, immediately activating “emergency corrections” when detecting hallucinations that are inconsistent with facts, thus avoiding factual errors. These features enable developers to implement customized security measures, ensuring interactive safety compliance in generative AI applications.
Deep Defense System: Casting an Ecological-Level “Golden Bell”
On top of basic protection and security protection barriers, enterprises need to build a complete deep defense strategy to ensure comprehensive security. However, building a deep defense system is a systematic project, covering various aspects such as enterprise architecture resilience, full lifecycle security design, secure cloud infrastructure, layered defense strategies, and trust boundary controls.
Although generative AI brings new challenges, the classic defense systems of cloud computing remain effective, such as layered security services, which can help enterprises resist many common threats. Users should deploy layered Amazon Cloud Tech security services in generative AI workloads and across the entire enterprise architecture, paying attention to integration points in the digital supply chain to ensure the security of the cloud environment.
On the build tool side, Amazon Cloud Tech provides enhanced security and privacy features in AI/ML services like Amazon SageMaker and Amazon Bedrock, which can add more layers of reinforced security and privacy controls for AI applications. These tools integrate security considerations from the design stage, making the process of leveraging generative AI for innovation faster, easier, and more cost-effective, while simplifying compliance processes.
Amazon Cloud Tech recommends that enterprises regularly review and update their protective mechanisms and conduct the same operations for all security controls to address newly emerging vulnerabilities and defend against emerging threats in the rapidly evolving AI security environment. By viewing security as an ongoing process of assessment, improvement, and adaptation, enterprises can confidently deploy innovative AI solutions while maintaining strong security controls.
New Paradigm of Security: From Technical Compliance to Value Creation
As more and more enterprises move from the thinking stage to the practical stage, conducting a large number of scenario experiments, the requirements for the security and compliance of AI models are also increasing. The trilogy of security measures provided by Amazon Cloud Tech, covering basic security protection, harmful content filtering protection, and robust deep defense strategies, not only provides enterprises with a comprehensive security framework but also offers various secure productivity tools required for model inference runtime through services like Amazon Bedrock. This enables enterprises to deploy innovative AI solutions more confidently while ensuring security and compliance.
As Chen Xiaojian, General Manager of Amazon Cloud Tech’s Greater China Product Department, stated: “In 2024, we will see many customers moving from the thinking stage to the practical stage, conducting a large number of scenario experiments. However, I believe that 2025 will definitely see a change, as many customers will transition from the prototype verification stage to the production stage, which is an inevitable path. At that time, customer demands will be more complex, not only in model selection but also in various technical support. Our purpose in developing Amazon Bedrock is not only to provide a model marketplace but more importantly, to provide various productivity tools and production environment tools required for model inference runtime, which is the true value of Amazon Bedrock.”
In this long race for the security of generative AI, Amazon Cloud Tech’s triple defense system is redefining security standards. While other vendors are still patching vulnerabilities, they have begun to build the “metaverse” of AI security—where security is not a cost center but a new engine driving digital transformation.
The emergence of DeepSeek has elevated discussions about the construction of intelligent computing centers to a new height, with various suggestions for the development direction of liquid cooling technology in intelligent computing centers. Should it be purely liquid cooling or a mixed direction of air and liquid? Opinions vary.
If you want to learn more about the concepts, practices, and dynamics of liquid cooling technology in data centers, on March 27, you are welcome to come to Hangzhou and meet us at the “2025 Data Center Liquid Cooling Technology Conference“.
The 2025 Data Center Liquid Cooling Technology Conference will gather nearly 1,000 top experts, technology leaders, and researchers from the data center industry to discuss innovation, cooperation, and development. We will bring over a thousand attendees an industry event in the form of exhibitions, keynote speeches, technical sharing, and application case visits.
For more details, please contact
Mr. Jin Xiaoyu Phone: 18610941758WeChat: Jin_Xiaoyuer
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