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-## 2.2 Appendix B: Distributed, Immutable, Ephemeral (DIE) Threat Identification
+## 4.2 Appendix B: Distributed, Immutable, Ephemeral (DIE) Threat Identification
 
 AI models operate at massive scale, rely on highly dynamic data flows, and often function as opaque “black boxes,” which can introduce unexpected failure modes and attack surfaces. To address this, the DIE Triad, Distributed, Immutable, Ephemeral offers a complementary, resilience-focused framework. Originally proposed by Sounil Yu \[20\], the DIE model shifts the focus from traditional data protection toward system survivability, adaptability, and architectural robustness. In AI contexts, where model drift, adversarial inputs, and dependency chains are constant concerns, applying the DIE principles helps ensure that AI systems are not only secure, but also resilient by design, capable of withstanding disruption and degradation.