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In 2026, the competitive advantage in AI will no longer come from model speed or scale—it will come from trust. As Southeast Asia and Korea accelerate enterprise AI adoption amid fragmented regulatory landscapes (from Singapore’s FEAT to Korea’s AI Basic Act), the CIO’s mandate has shifted from deployment to defense. 

“AI integrity”—the convergence of data provenance, bias mitigation, explainability, and ethical resilience—is now a board-level risk. For heads of compliance and data science, the question is no longer “Can we scale AI?” but “Can we certify its integrity?” Without it, you face regulatory fines, model drift, and reputational collapse. 
 In this PodChats for FutureCIO, we are joined by Lee Anstiss, Regional Director, Southeast Asia and Korea at Infoblox, to hopefully get insights and tips on how to safeguard AI integrity.

1.       Do Asia’s CIOs and CISOs know exactly which AI models are running across all our business units—or is shadow AI already creating integrity risks we cannot see?

2.       Given the fragmented state of each model’s regulatory status, what options are there for CIOs as they navigate fragmented rules with fragmented data?

3.       In your view, are enterprises in Asia prioritizing raw accuracy over an “integrity scorecard” that includes bias, explainability, and robustness—and if so, are CIOs trusting models that are fast but not fair?

4.       Based on current technologies and practices, can CIOs trace every piece of training data, including synthetic data, back to its source? Do organisations have blind spots where hidden bias or poisoned inputs could enter?

5.       Can CIOs test their production AI for bias against local languages and cultural norms in each market? Can they produce an audit trail for any regulator who asks?

6.       If a regulator demands proof of real-time bias disclosure tomorrow, is there a way for enterprises to have automated logs mapped to specific legal articles?

7.       Should organisations maintain a human-in-the-loop for high-stakes decisions, with override logs that feed back into retraining? How risky is using the discipline of oversight as a checkbox?

8.       Our topic is “tips on how to safeguard AI integrity” Can you share some of the most common or practical tips for CIOs, heads of AI, for ensuring or safeguarding AI integrity.