Systemic gaps in oversight
Analysts point out that this is not just a DeepSeek issue but a systemic risk across the AI foundational model ecosystem, citing the lack of cross-border standardization and governance.
“As the number of foundation models proliferates and enterprises increasingly build applications or code on top of them, it becomes imperative for CIOs and IT leaders to establish and follow a robust multi-level due diligence framework,” Shah said. “That framework should ensure training data transparency, strong data privacy, security governance policies, and at the very least, rigorous checks for geopolitical biases, censorship influence, and potential IP violations.”
Experts recommend that CIOs review the transparency of training data and algorithms, account for geopolitical context, and use independent third-party assessments and controlled pilot testing before moving to large-scale integration. “There is also a growing need for certification and regulatory frameworks to guarantee AI neutrality, safety, and ethical compliance,” Ram said. “National and international standards could help enterprises trust AI outputs while mitigating risks from biased or politically influenced systems.”