"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."
"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."
Systemic gaps exist across the AI foundational model ecosystem, driven by a lack of cross-border standardization and governance. CIOs and IT leaders must implement robust multi-level due diligence frameworks to ensure training data transparency, strong data privacy, and comprehensive security governance. Due diligence should include rigorous checks for geopolitical biases, censorship influence, and potential intellectual property violations. Enterprises should assess training data and algorithms, account for geopolitical context, and use independent third-party assessments and controlled pilot testing before large-scale integration. Certification and regulatory frameworks at national and international levels can help guarantee AI neutrality, safety, and ethical compliance.
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