US bans DeepSeek across federal agencies, putting AI supply chains under scrutiny
InfoWorld ·
DeepSeek has been dealt a huge 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; it does not apply, however, to private consumers, the general public, state/local governments, or Department of Defense (DoD) national security systems. “DeepSeek’s models present multiple threat vectors,” the Order stated. To begin with, 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. This could give 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 many iOS mobile apps with multiple critical vulnerabilities. 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 its impact on mission-critical functions, or other considerations related to national interest such as national security reviews, investigations, or security agreements, as reasons why such exemption should be granted,. Security issues are rampant The ban comes just as DeepSeek is open-sourcing DeepInfer, its in-house interference framework. DeepInfer powers DeepSeek-V4.1-Flash with what the company said is 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 social media site X . “From private deployments to public clouds, we’ll keep working with the community to make large-scale inference more efficient.” This isn’t its 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 still raised questions about the overall security practices surrounding the platform, particularly for organizations considering entrusting it with their own sensitive business information, Avakian pointed out. That’s not to imply that every DeepSeek model is “inherently malicious,” he said, but there are still major questions 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, he pointed out. The framework was just released, so organizations are going to need to validate the claimed efficiencies in their own environments rather than assuming savings will automatically translate into lower operating costs. He 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: ‘What is the business problem we’re trying to solve?’ From there, Avakian said, determine whether DeepSeek is the best way to solve it, validate security controls, and expand the use cases based on risk tolerance. Effective governance and risk assessment are key, he noted: understand what you’re bringing into your environment, validate its security, restrict its permissions, and make sure 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 important, Avakian said, because organizations may be using DeepSeek technologies indirectly, through vendors, software platforms, or other services, without necessarily 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 products they’re purchasing and deploying, Avakian said. This means revisiting AI inventories, potentially updating procurement requirements, and expanding processes around third-party risk management, he advised. “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. This article originally appeared on CIO.com .