OneOps - AWS AI-Powered LLM Governance & Operational Intelligence Implementation

Customer Name : OneOps

Partner Name : Onedata Software Solutions

OneOps faced a critical challenge in managing and governing enterprise-wide usage of Large Language Models (LLMs) across multiple teams while ensuring security, compliance, and cost control. As adoption increased, the lack of centralized governance led to fragmented usage, rising operational costs, and increased risk of data exposure.

To overcome this, OneOps partnered with OneData Software Solutions to design and develop OneOps.pro, an AI governance and operational intelligence platform. The solution enabled centralized control over LLM usage, improved visibility and auditability, and introduced guardrails for secure and compliant AI adoption—transforming how the organization managed AI at scale.

About the Customer

OneOps operates as an enterprise AI governance and operational intelligence platform provider, enabling organizations to securely adopt and manage Large Language Models (LLMs) at scale.

The platform focuses on providing centralized control over AI usage, ensuring compliance, cost optimization, and data security. With capabilities in governance, monitoring, and contextual intelligence, OneOps plays a key role in supporting enterprises in the emerging AI management landscape.

Challenges Before Implementation

The core challenge was the lack of centralized governance over enterprise AI usage—leading to uncontrolled adoption, rising costs, and increased security risks.

Fragmented LLM Usage – Multiple teams independently used different AI providers with no centralized control.
Lack of Access Control – No structured mechanism to define which users or teams could access specific models.
No Usage Visibility – Limited ability to track prompts, model usage, or user activity across the organization.
Uncontrolled Costs – Token-based usage was not monitored, leading to increasing and unpredictable AI expenses.
No Cost Attribution – Inability to allocate AI costs to specific teams or users.
Lack of Contextual Intelligence – AI responses were generic and not aligned with business-specific knowledge.
Data Security Risks – Potential exposure of sensitive enterprise data with no validation or guardrails.
Compliance Challenges – Difficulty in ensuring auditability and adherence to enterprise policies.

Without a governance layer, the organization risked uncontrolled AI adoption, compliance violations, and unsustainable operational costs.

Objectives

The implementation aimed to:

Establish centralized governance and control over all LLM usage.
Enable user activity tracking and auditability for compliance.
Optimize AI usage and reduce operational costs.
Provide cost visibility and attribution across teams.
Deliver context-aware AI responses using internal knowledge.
Implement data security guardrails to prevent sensitive data exposure.
Ensure compliance with enterprise and regulatory standards.
Build a scalable foundation for secure AI adoption.

AWS Architecture Implemented

The solution was built using AWS services to enable secure, scalable AI governance:

AWS Bedrock – Unified access layer for multiple LLM providers.
Centralized Governance Layer – Controlled access to models based on user roles and policies.
Prompt Monitoring & Audit Layer – Tracked all user interactions, prompts, and model usage.
Cost Monitoring Engine – Enabled token-level tracking and cost attribution per user/team.
RAG (Retrieval-Augmented Generation) Framework – Integrated internal knowledge bases for context-aware responses.
Data Security Guardrails – Implemented prompt validation and data protection mechanisms.
Logging & Monitoring Services – Enabled full auditability and compliance tracking.

Implementation Approach

Assessment & Design

Identified gaps in governance, visibility, and cost control across AI usage.
Designed a centralized platform to manage LLM access, monitoring, and security.

Governance & Access Control

Implemented role-based access control for LLM usage.
Restricted model access based on team and use case.
Secured API key distribution and usage.

Monitoring & Auditability

Enabled tracking of all prompts, responses, and user activity.
Established audit logs for compliance and accountability.

Cost Optimization & Visibility

Implemented token-level usage tracking.
Enabled cost attribution across teams and users.
Provided real-time visibility into AI consumption.

Contextual Intelligence (RAG)

Integrated internal knowledge sources with AI models.
Delivered business-specific, context-aware responses.

Security & Compliance

Implemented prompt validation guardrails.
Prevented sensitive data exposure.
Ensured compliance with enterprise policies and regulations.

Results Achieved

Centralized AI Governance – Established full control over enterprise-wide LLM usage.
Improved Cost Efficiency – Enabled visibility and optimization of token-based usage, reducing unnecessary spend.
Full Usage Transparency – Achieved complete tracking of prompts, users, and model interactions.
Enhanced Security & Compliance – Implemented guardrails to prevent data exposure and ensure audit readiness.
Context-Aware AI Responses – Improved relevance and accuracy through integration with internal knowledge.
Operational Control at Scale – Enabled secure and scalable AI adoption across multiple teams.
Increased Accountability – Cost attribution and activity tracking improved ownership and governance.

Key Takeaways

The implementation of OneOps.pro enabled the client to:

Establish centralized control over enterprise AI usage.
Reduce costs through visibility and optimization of AI consumption.
Ensure compliance through full auditability and monitoring.
Deliver secure, context-aware AI experiences.
Prevent data leakage through strong guardrails.
Enable scalable and responsible AI adoption.
Build a robust AI governance framework for enterprise use.