DeepSeek has been dealt a significant blow in the US, even as it continues to innovate and keep pace with — or in some cases outperform — rival models from American labs.

The US Secretary of Homeland Security has issued an order banning all DeepSeek products and services from federal agencies. Government organizations must purge their IT systems of it within 60 days; those with “compelling justification” can make a case for exemption by written request to the Cybersecurity and Infrastructure Security Agency (CISA).

The Department of Homeland Security (DHS) cited multiple concerns about the Chinese-hosted model, including data sovereignty, lax Chinese laws, and security vulnerabilities.

Analyst Erik Avakian, technical counselor at Infotech, understands the concern. “When organizations use any type of hosted AI service, they’re potentially sending intellectual property, confidential business information, or other sensitive data into an environment where they may have very little visibility or control over what happens to it,” he said.

Government Concerns

According to a document published in the US Federal Register, all technology developed by China-based Hangzhou DeepSeek Artificial Intelligence Co. will be banned from procurement by federal civilian agencies. This includes affiliated products and any third-party tools or software built using DeepSeek implementations.

Affected civilian executive branch agencies include the Departments of Treasury, State, and Justice; the ban does not apply to private consumers, the general public, state and local governments, or Department of Defense (DoD) national security systems.

“DeepSeek’s models present multiple threat vectors,” the Order stated. The company’s servers are located in the People’s Republic of China (PRC), whose legal system grants “extensive authority” to government cyber and intelligence functions to access private infrastructure.

DeepSeek’s account generation process collects personally identifiable information (PII) and may also track and digitally fingerprint users, the Order asserted. Its prompt response process may result in the transfer of sensitive data to the PRC, potentially giving it “actionable intelligence” on US programs or individual users.

Further, the Order stated, DeepSeek models themselves may contain malware or other security vulnerabilities. DHS pointed to open-source security research published in February 2025 that uncovered multiple critical vulnerabilities in DeepSeek iOS mobile apps.

Until the ban takes full effect, DHS advised agencies to mitigate risk by monitoring inbound and outbound data traffic, implementing enhanced logging and controls on data exfiltration, and detecting and flagging anomalous activity.

Agencies can submit requests for exceptions to the ban in writing and must provide a “compelling justification,” such as impact on mission-critical functions or considerations related to national interest, including national security reviews, investigations, or security agreements.

Security Issues Are Rampant

The ban comes just as DeepSeek is open-sourcing DeepInfer, its in-house inference framework. DeepInfer powers DeepSeek-V4.1-Flash with what the company describes as high throughput and low latency. It features high-performance kernels, an optimized inference engine, structured outputs, and speculative decoding.

DeepSeek said its production token encoding and decoding components, which are aligned with processing logic behind the DeepSeek API, will bring the “same API behavior to community deployments.”

“Open models. Open kernels. Open inference,” DeepSeek posted to X. “From private deployments to public clouds, we’ll keep working with the community to make large-scale inference more efficient.”

This isn’t DeepSeek’s first roadblock from a cybersecurity perspective. Most notably, in January 2025, the company leaked more than one million log entries, including chat histories, API secrets, and other sensitive information, through an unsecured database.

DeepSeek reportedly addressed the exposure, but it raised serious questions about the platform’s overall security practices — particularly for organizations considering entrusting it with sensitive business information, Avakian pointed out.

That’s not to imply that every DeepSeek model is “inherently malicious,” he said, but major questions remain around data privacy, the potential for information access under Chinese laws, the security of the broader AI ecosystem, and the susceptibility of DeepSeek’s models to jailbreaks and manipulation.

“Taken together, [these issues] raise legitimate concerns about whether organizations can adequately understand and manage the risks involved,” Avakian said.

Enterprises Should Seriously Assess Associated Risk

Enterprises are increasingly exploring DeepSeek, although most publicly documented adoption has been in China, Avakian noted. Some DeepSeek models have also been made available through platforms like Microsoft Azure and AWS.

“There’s genuine enterprise interest in the technology, as organizations look for opportunities to improve productivity, offer new capabilities, or reduce costs,” Avakian said. Useful applications include document summarization, internal research, and coding assistance.

DeepInfer also has the potential to reduce costs, but possible benefits should be weighed against associated security risks. The framework was just released, so organizations will need to validate the claimed efficiencies in their own environments rather than assuming savings will automatically translate into lower operating costs.

Avakian encouraged organizations to start with relatively low-risk use cases, preferably using “public, synthetic, or appropriately sanitized information,” within a sandboxed environment they control themselves.

The important question to ask is: “What is the business problem we’re trying to solve?” From there, Avakian said, organizations should determine whether DeepSeek is the best way to solve it, validate security controls, and expand use cases based on risk tolerance. Effective governance and risk assessment are key: understand what you’re bringing into your environment, validate its security, restrict its permissions, and ensure you can monitor what it’s doing.

“Approach this like any other software supply chain decision,” Avakian advised. “The fact that something is open source, inexpensive, or technically impressive doesn’t automatically make it appropriate for enterprise use.”

Impact on the Broader AI Ecosystem

The fact that the Order also addressed third-party products and services developed using DeepSeek implementations is significant, Avakian said, because organizations may be using DeepSeek technologies indirectly — through vendors, software platforms, or other services — without knowing what’s operating underneath. “These are classic supply chain issues,” he observed.

It’s no longer enough to know which AI applications employees are using; enterprises must understand which models, frameworks, dependencies, and external services are embedded in the products they’re purchasing and deploying, Avakian said. This means revisiting AI inventories, potentially updating procurement requirements, and expanding third-party risk management processes.

“Ultimately, enterprises leveraging these tools need to understand not only the AI technologies they’re intentionally deploying, but also the technologies they’re inheriting through third parties,” he said. As AI becomes further embedded in enterprise software stacks, that kind of supply chain visibility will only grow more critical.

This article originally appeared on CIO.com.