DeepSeek has emerged as a powerful and popular generative AI application, driving innovation while also raising security and privacy concerns. This article explores its related security risks, the impact on enterprises, and strategies organizations can adopt to mitigate threats and ensure safe, productive and responsible use.
What is DeepSeek and Why is It Raising Security Concerns?
DeepSeek is a popular GenAI application and LLM launched in January 2025 by a Chinese company, which also goes by the name of DeepSeek. (Unlike the ChatGPT application, released by the company OpenAI). Similar to ChatGPT, the DeepSeek GenAI application operates as a chatbot and offers content outputs to users who prompt it in natural language.
DeepSeek gained significant public attention since it has achieved advanced technological capabilities, similar to those offered by ChatGPT, Gemini, Claude, and other popular GenAI applications. However, it has done so at significantly lower training costs and by using approximately one-tenth of the computer power. This significantly disrupts the competitive GenAI landscape, and has macro-political consequences as well.
Also similar to other GenAI applications, DeepSeek creates security risks in organizations. This includes the need to ensure employees don’t expose sensitive data, ensuring DeepSeek outputs (like code snippets) don’t contain vulnerabilities or malware, and secure implementation if deploying the model locally.
In addition, DeepSeek’s “Open Weight” policy (unlike “open source”, which DeepSeek is often confused to be), means organizations need to ensure any deployed use of parameters in their environments is secure as well.
In this article, we’ll focus on the key risks enterprise face, related to data security and inadvertent unwanted exposure of sensitive data into the DeepSeel application.
The Key Privacy and Security Risks of DeepSeek
DeepSeek, like many other GenAI models, presents significant security and privacy challenges for enterprises. While its technological nature fosters innovation and accessibility, it also introduces substantial risks when introduced to sensitive information. Below, we explore the major vulnerabilities and DeepSeek privacy risks and their impact on enterprises.
1. Data Exfiltration
When employees paste or type corporate data in DeepSeek, they may inadvertently lead to exposure of sensitive corporate data. If DeepSeek is trained or fine-tuned on user prompts, then this data could become embedded in the model and unintentionally exposed in future outputs.
This means that sensitive data shared with DeepSeek (e.g., confidential business strategies, customer records, source code, or customer information) can become retrievable by unintended parties, whether competitors or adversaries.
2. Lack of Compliance and Governance
DeepSeek, like other generative AI applications, does not come with built-in compliance controls. This makes it complicated for enterprises to align its usage with regulatory frameworks such as GDPR, CCPA, HIPAA, and SOC 2. Users do not know which data is stored, for how long, under which circumstances and for what purposes.
This means that sharing regulated data with DeepSeek means it might be stored in unregulated international servers, used for unapproved use, and shared with prohibited parties.
3. Malicious Browser Extensions
Some implementations of DeepSeek, like a malicious DeepSeek browser extension, may unknowingly collect, store, or transmit browser information without adequate safeguards. Malicious browser extensions are known to exploit excessive permissions for credential harvesting, session hijacking, and data gathering. These can be used for gaining a foothold in the browser (and, consequently, the enterprise networks), phishing attacks, or exfiltrating sensitive information.
4. Shadow AI
If employees use DeepSeek without IT oversight, IT cannot govern and monitor use. This fragments IT governance, meaning IT cannot address the security risks, compliance issues, and data exposure concerns discussed above. They cannot implement policies, train employees or introduce security controls, simply because they are not aware of the risk. This augments the hazard even further, making the organization extremely vulnerable.
The Impact of DeepSeek Security Risks on Enterprises
How do the aforementioned risks impact enterprise AI security?
Confidential Information Exposed
Data leakage of confidential documents, codebases, or strategic plans that is ingested into the model or inadvertently exposed via AI-generated outputs, means that competitors or malicious actors can gain access to critical business information. This could have significant legal and business ramifications.
Potential Impact on Enterprises
- Loss of competitive advantage by exposing unique business strategies, algorithms, or product blueprints.
- Ransomware by attackers threatening to share sensitive data
- Lawsuits by customers whose PII was exposed
Regulatory Fines and Legal Ramifications
Lack of control over which data is shared with DeepSeek and how this data is stored and processed makes it difficult for enterprises to ensure adherence to data protection laws like GDPR, CCPA, HIPAA, and industry-specific regulations (e.g., SOX, PCI DSS, NIST 800-53).
Potential Impact of Compliance Risks on Enterprises
- Privacy violations
- Audit failures
- Fines
- Legal repercussions
Operational Security Threats
If attackers employ malicious browser extensions, they can gain access to internal systems and progress laterally in the network, a DeepSeek cybersecurity exposure.
Potential Impact on Enterprises
- Account takeovers
- Phishing attacks
- Ransomware
- Data exfiltration
- Operational shutdown
How Enterprises Can Secure AI Implementations Like DeepSeek
As employees integrate gen AI models like DeepSeek into their workflows, securing their usage becomes a high priority. Below are key best practices to proactively safeguard enterprise AI implementations.
- Map the data you can share with Gen AI – Classify organizational data based on confidentiality levels. Determine which data is proprietary, personal, or regulated and should never be exposed. Implementing GenAI DLP tools can help mitigate the risks of accidental data leaks.
- Train employees on the risks of Gen AI – Employees must understand that generative AI tools, while powerful, can introduce risks like data leakage, biased outputs, and compliance violations. Regular training sessions can cover topics responsible for AI usage, common attack vectors, and real-world examples of AI misuse. Your data security tool can assist with this by alerting and notifying employees about risky use.
- Monitor Gen AI use in the browser – Since DeepSeek is accessed via web applications, organizations should deploy browser security solutions to track these interactions. This helps identify and prevent potential data exposure, malicious browser extensions, and unusual behavior patterns.
- Monitor Gen AI browser extensions – Organizations should maintain an allowlist of approved extensions, conduct periodic security reviews, and use browser security tools to detect suspicious activity. Disabling unapproved extensions at the enterprise level can prevent unauthorized data access.
- Enforce usage of corporate accounts to access Gen AI – Requiring employees to use their corporate accounts for GenAI services helps prevent shadow AI use, ensuring better security oversight, auditability, and policy enforcement. It also helps prevent credential theft that can be used for exfiltrating the network. A browser security tool can track DeepSeek actions regardless of the account used to login, personal or private.
How to Secure DeepSeek Use
LayerX Security offers an all-in-one, agentless browser security platform that protects enterprises against the most critical risks and threats of the modern web, including GenAI data leakage, SaaS risk, identity threats, web vulnerabilities, DLP, and more.
LayerX is deployed as an Enterprise browser extension that integrates with any browser and provides organizations with full last-mile visibility and enforcement without disrupting the user experience.
Enterprises use LayerX to map GenAI usage in the organization, discover ‘Shadow’ AI apps and restrict sharing sensitive data with LLMs.
Explore LayerX today to fortify your enterprise AI governance and security.
