Closing the Observability Gap in Enterprise AI
3 min read
In brief:
SoftServe’s observability solution enables AI to be deployed with confidence on Cisco’s Secure AI Factory:
- Detects and resolves potential AI failures.
- Makes AI systems safe, reliable, cost efficient and compliant.
- A practical guide to end-to-end monitoring in AI systems.
Recent real-world AI incidents have shown that AI systems can fail while everything appears operational, especially in on-prem, hybrid, and sovereign AI environments.
It means organizations are already facing situations where AI assistants generate hallucinated responses. This includes the exposure of unsafe content, violations of policy controls, or misleading outputs, without any obvious infrastructure outage or application failure. Of more concern is that, in many cases, traditional monitoring tools continue to report healthy systems while business outcomes quietly degraded.
As enterprises move generative AI and agentic AI workloads from experimentation into production, these new operational risks are increasingly creating an observability gap that traditional monitoring approaches were never designed to address.
Modern solution
SoftServe applied its expertise in application and infrastructure instrumentation, architecture design and leveraged Splunk capabilities to protect against these new failure challenges AI systems can bring. It ensures AI systems that run on Cisco’s Secure AI Factory with NVIDIA are reliable, safe, cost-efficient, and compliant under enterprise governance constraints. It is a hands-on practical guide to establish end-to-end monitoring in AI Systems.
Using powerful Splunk analytics, SoftServe additionally developed custom observability use cases to address customer’s specific needs. This included customized evaluation, benchmarking, dashboards, and cross-platform correlations for enterprise AI environments running on the Cisco Secure AI Factory with NVIDIA. It also leveraged Splunk Observability Cloud and Splunk Enterprise to deliver unified AI observability.
The solution combines infrastructure observability, AI agent monitoring, governance controls, and operational workflows into a production-ready operating model for enterprise AI. It enables organizations to:
Our detailed whitepaper shows how the new approach introduces a split-plane observability architecture where operational telemetry is analyzed in Splunk Observability Cloud while keeping sensitive prompts, responses, audit records, and governed AI interaction data remain securely managed in Splunk Enterprise.
It also explores AI-specific observability domains including hallucination detection, quality evaluation, token and cost monitoring, guardrail visibility, troubleshooting workflows, and governance controls for enterprise AI systems.
For organizations moving AI from experimentation into production, observability is rapidly becoming as important as the models themselves.
Download the whitepaper
Discover how SoftServe addresses those AI observability gaps and AI-related production challenges in on-prem Cisco Secure AI Factory with NVIDIA environments, leveraging Splunk Platform and Splunk Observability Cloud for sensitive AI data governance and control.
