Understanding Observability
In the rapidly evolving artificial intelligence (AI) ecosystem, large language models (LLMs) have emerged as the most powerful, transformative tools in the AI toolkit. Designed to process and comprehend human language, these models are revolutionizing fields such as customer service, tech, pharma, finance, retail, and myriad others, and also changing the face of Software as a Service (SaaS). As these AI models grow in capability and complexity, they present unprecedented potential for productivity and innovation. Their deployment in business environments also introduces unique challenges, the most serious of which is a vastly expanded attack surface, closely followed by siloed visibility. Simply put, when multiple models, including multimodal models, are deployed in an organization, they operate in parallel, rather than integrating, bringing with them a panoply of vulnerabilities. This means that security teams have a fragmented view of the system they must protect and defend, when what they really need is full, seamless visibility into and across all models in operation. In an enterprise, these could conceivably include big external models such as ChatGPT: targeted models such as BloombergGPT; models embedded in SaaS applications such as Salesforce; retrieval-augmented generation (RAG) models; and small, internal models fine-tuned on company data. Together, these models can number in the dozens or into the hundreds—and that growth is not slowing down any time soon. Enter the concept of Observability. Observability addresses the imperative of being able to see, evaluate, and synthesize insights about models’ performance and behavior; for instance, decision-making processes, inherent limitations, reliability, efficiency, and effectiveness. Integrating an Observability layer into the model deployment framework establishes a strong foundation for the successful use, security, and maintenance of AI-powered applications.The Benefits of Model Observability
The ability to observe model behavior can provide information about possible attack trends and patterns, as well as more granular analytics related to finance, operations, and potentially even personnel issues. Visibility into the following key focus areas can translate into enhanced resource allocation, productivity, innovation, and competitive advantage.- Performance Assurance: Observing models in action—from the API connection to the model's query response—allows organizations to continuously monitor and compare vital performance metrics, such as latency, throughput, and accuracy (within and across the models). This real-time oversight facilitates the immediate identification and mitigation of bottlenecks, ensuring consistent user experiences of AI systems and adherence to performance criteria.
- Error Management: A robust Observability framework will capture and log detailed metrics to provide administrators with an in-depth view of models’ decision-making processes, facilitating rapid error identification and resolution, and promoting trust in model performance.
- Usage Analytics: Observability tools enable review of real-world engagement scenarios, from tracking response times and API calls to analyzing traffic patterns and identifying recurring use cases. Real-time monitoring of model functionality (error rates, token usage, etc.) and user interactions (scanning prompts and responses for sensitive or prohibited content, attempted attacks, etc.) can provide insights to help organizations continually refine and realign models based on actual usage.
- Model Drift: Clear visibility into AI model performance can allow administrators and developers to identify degradation, or ‘model drift’, due to evolving data landscapes. Knowing when this has occurred is critical to making sure models are retrained or fine-tuned in a timely manner to ensure trust in the model’s output.
- Resource Optimization and Scaling: Deploying LLMs across an organization can entail high computational demands, such as memory and CPU/GPU usage, making resource utilization insights important data points when considering cost optimization or scaling for an increase in users or workload surges.
- Compliance: Knowing the state of system and user compliance with internal acceptable use, privacy, and other policies—as well as industry standards and governmental regulations—is critical in every organization, and especially so in regulated sectors such as healthcare and finance. Utilizing solutions that enable visibility into and auditability of detailed logs and other metrics can ensure that model deployments meet data privacy, security, and other compliance benchmarks.
