References
- Shrivastava, A. (2022). Resilient AI Systems. Medium.
- Lande, D. & Strashnoy, L. (2024). Resilient Artificial Intelligence Architecture. National Technical University of Ukraine ▴ Igor Sikorsky Kyiv Polytechnic Institute; University of California, Los Angeles.
- Tranchulas Research Team. (2025). Defensive Strategies ▴ Building AI-Resilient Security Architectures. Tranchulas.
- Sivanandan, S. (2023). AI-Driven Resilience Analysis for Architectural Designs. Medium.
- AWS re:Invent. (2024). Designing generative AI workloads for resilience (COP332). YouTube.
Reflection
The construction of a resilient technological framework for an AI-based surveillance system is a complex and multifaceted undertaking. It requires a deep understanding of not only the technological components involved, but also the operational processes and governance structures that support them. The principles and strategies outlined in this guide provide a roadmap for building a system that is not only robust and reliable, but also adaptable and secure. However, the journey does not end with the implementation of the system.
Resilience is not a static state; it is a continuous process of assessment, improvement, and adaptation. As new threats and challenges emerge, the system must evolve to meet them. The ultimate measure of a resilient system is not its ability to withstand a single, catastrophic failure, but its ability to adapt and thrive in a constantly changing world.
