As the artificial intelligence landscape continues to advance, particularly in the realm of generative models known as Large Language Models (LLMs), the industry faces increasing risks of producing biased or harmful content. The burgeoning necessity for a methodology to assess and enhance the resilience of these models has led to the inception of PyRIT (Python Risk Identification Tool). Designed to act as an automated red teaming framework, PyRIT empowers engineers to effectively evaluate their AI application's robustness and anticipate potential misuses or privacy issues. It navigates the terrain of AI security with a structured and systematic approach, setting itself apart from other, more manual and fragmented solutions. PyRIT’s infrastructure is composed of several elements: the Target, Datasets, Scoring Engine, Attack Strategy, and Memory. These components collectively orchestrate the testing of LLMs by producing numerous prompts, gauging the model's responses, and documenting the test interactions for incurred vulnerabilities. By employing what is known as a 'self-ask' methodology, PyRIT is capable of not only requesting responses from these LLMs but also retrieving supplementary details about the nature of the prompts. Such detailed examinations enable it to perform nuanced classification tasks that contribute significantly to the overall assessment of the AI model's safety. Lastly, PyRIT excels in providing actionable insights with its ability to classify risks into discrete harm categories like fabrication or prohibited content and supports varying complexity in attack scenarios from single-turn to multi-turn exchanges. With its array of features, PyRIT serves as a compass for the responsible innovation and application of LLMs, anchoring AI development in the cornerstones of security and integrity. It's not just a tool but a strategic partner in the journey towards creating AI that is not only powerful but also protected against misuse and unintended consequences.