AI-driven observability must adapt to compliance, context, and complexity.
At Quest Global, we operate in a uniquely complex environment— serving engineering clients across aerospace, rail, automotive, and industrial sectors, each with its own toolchains, compliance needs, and architectural constraints. In such a world, observability isn’t just a platform—it’s a puzzle.
One of the toughest challenges we face is managing observability in siloed, heterogeneous environments, where each client may use a different set of platforms, security postures, and access restrictions. This makes traditional, one-stack-fits-all monitoring irrelevant. AI-driven RCA must be context-aware, capable of navigating the dimensional complexity of varied configurations and fragmented ownership.
Another dimension is regulatory compliance observability. In aerospace, for instance, design-time decisions must comply with industry-specific safety standards. If a compliance violation is caught late—say, at the prototype or pre-production stage—it can derail timelines and budgets. That’s why we’re looking at ways to embed compliance observability into DevOps pipelines, especially in domains where rework is expensive and delays are unacceptable.
AI-driven observability must adapt to compliance, context, and complexity.
We’ve also recognized the need to mine past RCAs for patterns. Often, recurring failures trace back to similar root causes— but the tribal knowledge isn’t always codified. By layering AI over our observability data, we’re building a system that proactively surfaces RCA patterns, enabling fast er resolution and reducing cognitive load on engineering teams.
What’s different in engineering services is that DevOps here isn’t only about code-todeploy—it’s about code-to-compliance-toclient-handoff. Every touchpoint is potentially a handover to another organization or system outside our direct control. That makes dependency observability—across tools, APIs, data layers, and validation workflows—an absolute must.
We’re also exploring automated anomaly detection that understands real-world signals—not just telemetry but also external triggers like customer escalations, audit outcomes, or regulatory updates. For us, observability must go beyond logs and metrics—it must ingest external context to remain relevant.
Ultimately, the goal isn’t just uptime. It’s engineering assurance—knowing that what we build, monitor, and deliver stands up to the quality and compliance expectations of the world’s most demanding industries. And for that, observability has to be intelligent, contextual, and engineered to adapt.
–Authored by Aravindan Raghavan, Global Head – Business Excellence, CISO & DPO, Quest Global
