Using agentic AI for real-time drift detection with automated remediation workflows

About Client

A leading Saudi Arabian financial institution headquartered in Riyadh, with a history dating back to the 1950s. The bank offers personal, commercial, investment, and Islamic banking services through an extensive branch network and secure digital platforms. As part of its digital transformation strategy, the organization continues to invest in innovative technologies while maintaining the highest standards of regulatory compliance, data security, and operational resilience.

Key Challenge

Operating within a highly regulated banking environment, the customer required a fully private AI infrastructure capable of supporting enterprise AI initiatives without compromising data sovereignty, security, or compliance.

The bank faced several challenges:

• Limited visibility into configuration changes across network environments.
• Increasing compliance and security risks from undocumented changes.
• Manual audit processes that required significant effort and delayed issue resolution.
• Difficulty identifying configuration drift in real time.
• Lack of automated remediation capabilities to proactively address operational risks.
• Need for an enterprise-grade AI platform that could remain completely private and on-premises.
• Requirement for strong governance, operational control, and auditability across the AI ecosystem.

Additionally, the bank needed an AI-ready platform capable of supporting future GenAI and Agentic AI workloads while meeting stringent regulatory and privacy requirements.

Solution

Microland designed and deployed a fully private, on-premises AI foundation tailored to the bank's regulatory, security, and operational requirements. The solution combined AI infrastructure, LLMOps capabilities, and an Agentic AI application to create a secure, scalable, and governance-driven environment.

Key components included:

• A private AI infrastructure optimized for enterprise Generative AI workloads.
• An LLMOps framework enabling model governance, monitoring, orchestration, and lifecycle management.
• An Agentic AI solution for automated network configuration drift detection, compliance validation, and remediation.
• Support for multilingual AI models while maintaining complete data sovereignty and operational control.
• Enterprise-grade governance, auditability, and scalability aligned with banking and regulatory requirements.

This fit-for-purpose architecture enabled the bank to establish a secure AI foundation while accelerating adoption of AI-driven operations and automation.

Business Value Delivered

• Reduced AI infrastructure implementation costs by 40%.
• Strengthened compliance with automated drift detection and remediation.
• Improved visibility and control over network configuration changes.
• Enabled a secure, fully private AI environment aligned with regulatory requirements.
• Established a scalable foundation for future AI-driven innovation.