 Craft different models via separate training data or fine-tuning for different use-cases to create a more granular and accurate generative AI output as per it's defined use-case Ensure sufficient sandboxing is present to prevent the model from scraping unintended data sources which could hinder the machine learning output Use strict vetting or input filters for specific training data or categories of data sources to control volume of falsified data.  Data sanitization, with techniques such as statistical outlier detection and anomaly detection methods to detect and remove adversarial data from potentially being fed into the fine-tuning process Adversarial robustness techniques such as federated learning and constraints to minimize the effect of outliers or adversarial training to be vigorous against worst-case perturbations of the training data An "MLSecOps" approach could be to include adversarial robustness to the training lifecycle with the auto poisoning technique An example repository of this would be Autopoison testing, including both attacks such as Content Injection Attacks (“how to inject your brand into the LLM responses”) and Refusal Attacks (“always making the model refuse to respond”) that can be accomplished with this approach.  OWASP. org 11 | OWASP Top 10 for LLM v1. 0 Testing and Detection, by measuring the loss during the training stage and analyzing trained models to detect signs of a poisoning attack by analyzing model behavior on specific test inputs