AI is evolving rapidly, and organisations are continuously exploring new models to enhance their applications and services. One emerging AI technology currently generating significant interest is DeepSeek.
In January, a Chinese owned AI startup released its latest model DeepSeek R1, an open-source AI model that offers potential cost advantages over existing solutions like Azure OpenAI. Its release made headlines as its claim that its model was made at a fraction of the cost compared to its rivals caused the US tech stocks to drop. However, as businesses begin to evaluate their use of AI, it is essential to assess the broader implications of integrating models such as DeepSeek into enterprise environments.
As AI becomes more accessible, many software providers are embedding AI capabilities directly into their applications. This integration offers advantages such as streamlined workflows and enhanced automation, but it also raises concerns around security, compliance, and the potential for bias. With AI models being embedded at scale, organisations may find it difficult to control or even identify the underlying AI systems used within third-party applications.
One major consideration when evaluating DeepSeek is data governance. Unlike widely adopted AI solutions hosted in highly regulated environments (e.g., Azure OpenAI) DeepSeek lacks clear assurances regarding data residency and processing.
Additionally, organisations handling EU citizen data must comply with regulations such as GDPR. DeepSeek operates within China, a jurisdiction that does not currently have an adequacy agreement with the EU as their privacy laws are not up to GDPR standard, which means additional due diligence is necessary to ensure compliance when processing sensitive data.
Concerns have also been raised about the potential for state access to data, given China's regulatory framework requiring companies to share information with authorities upon request. This raises significant implications for intellectual property protection and data security.
AI supply chains are complex, and ensuring trust in an embedded AI system is a growing challenge. Organisations often rely on third-party software providers that integrate AI into their products, making it difficult to ascertain where data is processed and whether security best practices are being followed. Without visibility into how AI models are managed, businesses face risks related to intellectual property protection and exposure to potentially insecure AI-driven systems.
Another key consideration is the potential for DeepSeek it to exhibit bias or content filtering based on Chinese influences. While all AI models have some level of content moderation, the concern arises when there is a lack of transparency regarding how information is filtered, prioritized, or omitted. This becomes a particular concern in business applications where accuracy and neutrality are important. Organisations need to ensure that AI-generated outputs align with ethical guidelines and business requirements.
While DeepSeek and similar AI models offer promising capabilities, organisations should take a structured approach before adoption:
AI is a powerful tool, but its adoption must be carefully managed to avoid risks. Embedded AI within third-party applications presents a unique set of challenges that organisations need to be proactive about when it comes to addressing. By prioritising security, supply chain transparency and compliance, businesses can make informed decisions when integrating AI solutions like DeepSeek into their operations.
As the AI landscape continues to evolve, staying vigilant and informed about emerging risks will be essential in maintaining a secure AI-driven ecosystems.