Balancing AI cost efficiency with data sovereignty

AI cost efficiency and data sovereignty are at odds, forcing a rethink of enterprise risk frameworks for global organisations.

For over a year, the generative AI narrative focused on a race for capability, often measuring success by parameter counts and flawed benchmark scores. Boardroom conversations, however, are undergoing a necessary correction.

While the allure of low-cost, high-performance models offers a tempting path to rapid innovation, the hidden liabilities associated with data residency and state influence are forcing a reassessment of vendor selection. China-based AI laboratory DeepSeek recently became a focal point for this industry-wide debate.

According to Bill Conner, former adviser to Interpol and GCHQ, and current CEO of Jitterbit, DeepSeek’s initial reception was positive because it challenged the status quo by demonstrating that “high-performing large language models do not necessarily require Silicon Valley–scale budgets.”

For businesses looking to trim the immense costs associated with generative AI pilots, this efficiency was understandably attractive. Conner observes that these “reported low training costs undeniably reignited industry conversations around efficiency, optimisation, and ‘good enough’ AI.”

AI and data sovereignty risks

Enthusiasm for cut-price performance has collided with geopolitical realities. Operational efficiency cannot be decoupled from data security, particularly when that data fuels models hosted in jurisdictions with different legal frameworks regarding privacy and state access.

Recent disclosures regarding DeepSeek have altered the math for Western enterprises. Conner highlights “recent US government revelations indicating DeepSeek is not only storing data in China but actively sharing it with state intelligence services.”

This disclosure moves the issue beyond standard GDPR or CCPA compliance. The “risk profile escalates beyond typical privacy concerns into the realm of national security.”

For enterprise leaders, this presents a specific hazard. LLM integration is rarely a standalone event; it involves connecting the model to proprietary data lakes, customer information systems, and intellectual property repositories. If the underlying AI model possesses a “back door” or obliges data sharing with a foreign intelligence apparatus, sovereignty is eliminated and the enterprise effectively bypasses its own security perimeter and erases any cost efficiency benefits.

Conner warns that “DeepSeek’s entanglement with military procurement networks and alleged export control evasion tactics should serve as a critical warning sign for CEOs, CIOs, and risk officers alike.” Utilising such technology could inadvertently entangle a company in sanctions violations or supply chain compromises.

Success is no longer just about code generation or document summaries; it is about the provider’s legal and ethical framework. Especially in industries like finance, healthcare, and defence, tolerance for ambiguity regarding data lineage is zero.

Technical teams may prioritise AI performance benchmarks and ease of integration during the proof-of-concept phase, potentially overlooking the geopolitical provenance of the tool and the need for data sovereignty. Risk officers and CIOs must enforce a governance layer that interrogates the “who” and “where” of the model, not just the “what.”

Governance over AI cost efficiency

Deciding to adopt or ban a specific AI model is a matter of corporate responsibility. Shareholders and customers expect that their data remains secure and used solely for intended business purposes.

Conner frames this explicitly for Western leadership, stating that “for Western CEOs, CIOs, and risk officers, this is not a question of model performance or cost efficiency.” Instead, “it is a governance, accountability, and fiduciary responsibility issue.”

Enterprises “cannot justify integrating a system where data residency, usage intent, and state influence are fundamentally opaque.” This opacity creates an unacceptable liability. Even if a model offers 95 percent of a competitor’s performance at half the cost, the potential for regulatory fines, reputational damage, and loss of intellectual property erases those savings instantly.

The DeepSeek case study serves as a prompt to audit current AI supply chains. Leaders must ensure they have full visibility into where model inference occurs and who holds the keys to the underlying data. 

As the market for generative AI matures, trust, transparency, and data sovereignty will likely outweigh the appeal of raw cost efficiency.

See also: SAP and Fresenius to build sovereign AI backbone for healthcare

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