Explain what adversarial attacks against financial AI systems are and how to construct a robust defense strategy against them.

Adversarial attacks against financial AI systems are deliberate attempts to manipulate the input data fed to these models, causing them to make incorrect predictions or decisions. The goal is to exploit vulnerabilities in the AI system's learning process and often leads to a financial gain for the attacker or a financial loss for others. These attacks can manifest in various forms, targeting different aspects of the AI model and the data it processes. For example, in the context of fraud detection, an attacker might slightly modify transaction records in such a way that they appear legitimate, thereby bypassing the AI model’s detection mechanisms. This doesn't always need to be human manipulation of the data, as another AI model can be used to generate these adversarial examples automatically, meaning the attacker can conduct the attack in scale, allowing for many more attempts. This illustrates an important concept, where the vulnerabilities can be attacked through the data which is fed to the AI system, rather than attacking the AI system directly.

One common form of attack is the evasion attack, where the attacker manipulates data to fool the model at inference time (when the model is used to make predictions). An example of this would be in a credit scoring system. An individual with a poor credit history could subtly alter their loan application data, making it appear slightly more favorable to the AI model, even though no change in the individual’s actual credit rating is taking place. This could be achieved by adding more fictional income to the form and other similar changes. These changes may seem small and insignificant for a human looking over the data, but the AI model is very sensitive to the change in input features, and may ultimately approve a fraudulent loan. Another form of attack is the poisoning attack, where the attacker aims to degrade the performance of the AI model by manipulating the training data. This involves injecting carefully crafted, malicious data points into the training dataset to skew the model's decision boundary. For example, in a high-frequency trading (HFT) algorithm, an attacker might inject manipulated trading data that influences the model to make suboptimal trading decisions. This injected data can be used to train the algorithm in a way that benefits the attacker while causing the model to fail. Another potential attack is by modifying the model itself, and in this scenario, the attacker may try to steal the model, which is often considered a trade secret, or modify its parameters, thereby making the AI system fail. This type of attack is not as common as the other two, as it requires much more sophisticated techniques and access to the model directly.

Constructing a robust defense strategy against adversarial attacks requires a multi-faceted approach. One approach is to implement adversarial training, a technique where the model is trained using both clean data and adversarial examples, and this can be performed throughout the training of the model. This forces the AI system to learn to handle and understand examples that may have been manipulated, and makes it more resistant to future manipulations. For example, if a model was used for detecting fraudulent loan applications, this would include generating examples of slightly manipulated loan application forms that would usually bypass the system, and the model is trained to specifically flag those examples. Another method is using input sanitization techniques to detect and mitigate anomalies and manipulations in the input data. This can involve adding layers that remove small perturbations to input data using techniques such as input denoising. These layers can be trained as part of the full AI system, and are specifically designed to remove the small perturbations caused by adversarial manipulations to make the data similar to clean data that was used for training, ensuring that the model does not incorrectly flag a slightly modified example.

Another strategy includes implementing defensive distillation, where a smoother version of the original model is trained. This process involves training a new model on the outputs of the first model, so, the gradients of the new model become smoother and they are more resistant to changes of the data, making them harder to be attacked by adversarial techniques. Another approach to defend against poisoning attacks is to use robust learning algorithms which are less sensitive to outliers in the data, or implement data validation techniques to identify and remove suspicious data points before they enter the training set. Furthermore, ensemble methods, where multiple different AI models are used in conjunction and their results are combined to give a final prediction, can also be used, since it is unlikely that all models will fail simultaneously in the case of an adversarial attack. Regular security audits and continuous monitoring of the AI system for unexpected behavior are also critical. This also includes testing the model with different adversarial attacks to see how it would respond in a real-world setting, and training the model to be resistant against those specific attacks. Additionally, explainable AI techniques can be used to understand which features are most influential in the AI's decision making. This allows cybersecurity professionals to understand why a specific example has been flagged as suspicious or not, which can give more insight into potential vulnerabilities of the system. Finally, building a strong access control system will also help prevent attackers from accessing sensitive training data and models, and the system must also log all activity and changes to the system.