How AIOps Transforms IT Operations: From Early Risk Detection to Intelligent Management

AIOps Construction Background and Goals

With accelerating digital and distributed architecture transformation, IT operations are shifting from reactive to proactive, aiming to improve system vitality and management effectiveness.

Transform systems from merely surviving to thriving.

Shift management from having measures to achieving results.

The bank identifies four key challenges: hidden risk detection, emergency response, data support, and operational control.

Four Goals Expected from AIOps

Detect risks earlier through automated, intelligent, and traceable processes.

Resolve issues faster by providing a comprehensive view of system health for rapid root‑cause localization.

Make more precise operational decisions by extracting actionable insights from massive operational data.

Achieve smarter operational control by integrating AI and machine learning into management and automation.

Implementation Approach and Platform Framework

Effective AIOps deployment must address three problems: data assetization, simplifying inefficient analysis, and scenario‑driven application.

2.1 Solving Data Issues – Operations Data Marketplace

A standardized, unified view called the Operations Data Marketplace aggregates all non‑business operational data from dozens of platforms into six categories: operations management, configuration, monitoring & alerts, operation actions, runtime logs, and IT operations metrics.

The data is stored in a three‑layer model: buffer layer (raw data landing), middle layer (domain‑specific intermediate models), and application layer (standardized services for algorithms and analytics). This enables a unified operations indicator system covering runtime health, system‑level metrics, and operational KPIs.

2.2 Enhancing Analysis Efficiency – Building Analysis Engines

Two engines are deployed: an AI algorithm engine with an online, workflow‑based development framework, and a BI visualization engine for self‑service analytics. Together they accelerate algorithm deployment and empower users to create custom dashboards.

Human involvement is also emphasized to ensure users can effectively apply data throughout the analysis lifecycle.

2.3 Promoting Scenario Applications – Constructing a Scenario Ecosystem

Three scenario tiers are defined: pre‑risk analysis, real‑time fault localization, and post‑mortem summarization. Classic scenarios are provided out‑of‑the‑box, while a self‑service portal allows users to build customized analyses. For complex, sensitive systems, dedicated SRE teams receive tailored services.

2.4 Summary

The framework consolidates operational data, establishes indicator systems, and provides AI and BI engines to maximize analysis efficiency, while scenario‑driven applications empower users across the entire operational lifecycle.

Practical Case Analyses

3.1 Potential Risk Mining

Models predict system, business, and capacity risks using historical metrics, issuing early warnings for anomalies such as transaction spikes or resource exhaustion.

Different algorithms are applied to specific risk types, and the results are integrated into workflow for continuous improvement.

3.2 Panoramic Intelligent Insight

A unified view aggregates topology, performance, alerts, logs, and operational activities, enabling rapid information retrieval and root‑cause recommendation through correlation algorithms.

3.3 System Operation Portrait

Tag‑based profiling of systems after incidents provides precise targeting for operational improvements, such as resource utilization analysis.

Future Directions

Emphasize systematic intelligent operations beyond single‑scenario customization.

Shift from reactive response to proactive prevention, including fault prediction.

Extend AI‑driven empowerment to security and other domains.

Strengthen observability and explainability of AI decisions for operational staff.