This whitepaper focuses on the unpredictability of AI-native applications and how non-determinism introduces new security risks. The opening section explains how identical inputs can produce different outputs, breaking traditional AppSec assumptions. The core sections detail risks such as compliance drift, prompt leakage, and jailbreak attacks, emphasizing that vulnerabilities emerge through model behavior changes rather than code changes. It introduces continuous tracing, AI red teaming, and adaptive guardrails as essential controls. The final sections highlight AI Security Posture Management (AISPM) as a framework to unify monitoring, policy enforcement, and behavioral analysis. The key takeaway is that AI security must shift from static testing to continuous, adaptive governance that ev

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