# Resilience and Resilient Systems of Artificial Intelligence: Taxonomy, Models and Methods
## 3 Background, Taxonomy and Ontology
### Taxonomy and Ontology of AIS Resilience
The principles of Adaptability, Evaluability, and Plasticity-stability trade-off are interrelated and specify the ability of AIS to learn and, more specifically, engage in continual learning with regularization to avoid the effect of overfitting and catastrophic forgetting [4,6,14]. The principle of AIS Evaluability implies the possibility of making necessary  
Figure 8: Taxonomy of AIS resilience. structural, architectural or other changes to the AIS, which will reduce vulnerability and increase resilience to potential future destructive impacts. These principles also include improving the speed of adaptation to new disturbances through meta-learning techniques. A resilient AIS can be characterized by a set of properties: ability to recover, rapidity, resourcefulness, reliability, robustness, security, high confidence, maintainability, survivability, performance. These properties should be self-explanatory from their names or from description in the previous subsections. A set of indicators (metrics) can be proposed to AIS resilience assess: rapidity indicator, robustness indicator, redundancy indicator, resourcefulness indicator, integral resilience indicator. Integral resilience indicator deserves special attention, as it simultaneously takes into account the degree of robustness and recovery rate. Resourcefulness can be conceptualized as consisting of the ability to apply resources to meet established priorities and achieve goals [11]. An organizational resourcefulness can be measured by ratio of the increase in resilience to the increase in the amount of involved resources [11; 45]. A certain set of tools should be used to ensure AIS resilience. The most important tools are resilience assessment methods, which allow to assess, compare and optimize the AIS resilience. To ensure robustness and performance recovery, it is necessary to use various disturbance countermeasures. Meta-learning tools, methods of reconfiguring the AI model with regard to performance or resources provide certain level of evolvability. The implementation of AIS adaptation and evolution mechanisms requires the inclusion of constraints imposed by the principle of plasticity-stability trade-off [6; 14]. Figure 9 shows an ontological diagram of the AIS resilience. Moreover, the diagram specifies the conditions for the emergence of the AIS evolution. AIS should be improved to quickly eliminate drift caused by these changes through domain adaptation and meta-learning. In addition, there is a need to initiate evolutionary improvement of the AIS as a response to influence of non-specified faults/failures, the effective processing of which was not provided in the current configuration. In addition, Figure 9 shows a list of the main disturbance countermeasures required for absorbing disturbances, graceful degradation and performance recovery. This list includes the following countermeasures: data sanitization, data encryption, homomorphic encryption; gradient masking methods; robustness optimization methods, adversary detection methods, fault masking methods, methods of error detection caused by faults, method of active recovery after faults, drift detection methods, continual learning techniques, few-shot learning techniques, active learning techniques [46; 47; 48]. Conventional cybersecurity techniques reduce the risks of insider attacks, which denies attackers access to sensitive information about AIS. In addition, data sanitization and data encryption methods prevent data poisoning. Data Sanitization methods are based on the Reject on Negative Impact approach; they remove samples from the dataset which negatively affect the performance of the AIS [33; 47]. Homomorphic Encryption methods provide defense against cyber-attacks on privacy. Homomorphic Encryption encrypts data in a form that a neural network can process without decrypting the data.