AI can spot anomalies earlier, automate routine decisions and help security teams respond before an issue spreads, but many leaders remain uncertain about handing meaningful control to systems that are hard to interpret and difficult to audit. This whitepaper clarifies that the answer isn’t to slow AI adoption or default to mistrust. It’s to place AI inside a zero trust model built on continuous verification with constrained access and clear governance, so AI-native networks become auditable and accountable instead of a leap of faith.

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• Why the trust gap in AI-driven networking is widening even as its operational value becomes clear
• How zero trust principles, including continuous verification and micro-segmentation, map directly onto AI-native networks
• What transparency, accountability and governance look like in practice, including the questions CIOs and infrastructure teams need to answer before scaling automation
• How AI-native networking paired with zero trust improves security, operational integrity and cost efficiency
• How to overcome the two most common barriers to adoption: fear of losing control and the complexity of doing it all at once