Your database is more powerful than you think. Learn how built-in vector capabilities can power your GenAI applications and save you from the hassle of adopting a new database. The heart of Generative AI (GenAI) workloads rely on the ability of computers to categorize and understand the world's data (images, sounds, text) as numerical representations called vectors. This is achieved through a process called "embedding," where a model translates the data into vectors.

Regardless of industry specifications, creating and maintaining a highly functional incident management process is crucial for organizations of all sizes. The various potential applications of Generative AI in this process can significantly enhance the efficiency, accuracy, and speed of incident detection, analysis, and resolution. GenAI can be utilized across all stages of the incident management process, including preparation, response, communication, and learning.

Fragmented insights. Limited visibility. Unreliable solutions. For too long, traditional IT tools have not just failed – they’ve actively sabotaged both IT departments and businesses at large. These antiquated systems drain high-value engineering resources, crush frontline support teams, and turn problem resolution into an endless, futile struggle. In an age where AI reshapes entire industries overnight, clinging to these traditional IT approaches border on organizational malpractice.

This whitepaper explores the pivotal roles of security and artificial intelligence (AI) in advancing IT monitoring capabilities, with a specific focus on Microsoft SCOM (System Center Operations Manager) and Azure Monitor SCOM Managed Instance (MI). It highlights how security measures safeguard monitoring data integrity and confidentiality while AI enhances predictive analytics, anomaly detection, and automated responses.

The proliferation of managed LLM services like OpenAI, Amazon Bedrock, and Anthropic have introduced a wealth of possibilities for generative AI applications. Application engineers are increasingly creating chain-based architectures and using prompt engineering techniques to build LLM applications for their specific use cases.