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AI TRiSM Framework for Trust and Security in Network Copilot™

Source Decision Insights Coverage Published November 20, 2025 Companies 1
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2nd article in the last 90 days, one of 4 articles referencing Protect AI. Previous coverage: Daily Intelligence Brief: InterDigital, BEYOND, Tencent Cloud and more - October 23, 2025 (Oct 2025).

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This article discusses AI-Trust, Risk, and Security Management (AI TRiSM), emphasizing the importance of ethical development and use of Artificial Intelligence (AI) technologies. It outlines the framework designed to ensure trust, address risks, and maintain security across AI Operations (AIOps), which is relevant for IT leaders and decision-makers.

Framework Overview

The AI TRiSM framework comprises three main components. AI Trust focuses on transparency and explainability, enabling users to understand AI decision-making. AI Risk centers on establishing governance to manage potential risks during the AI lifecycle. Finally, AI Security Management aims to protect AI systems against unauthorized access and misuse.

Pillars of AI TRiSM

This framework is built upon four pillars. The first is Explainability and Model Monitoring, which ensures transparency in AIOps and tracking model performance. ModelOps is the second pillar, targeting the comprehensive management of AI models throughout their lifecycle from development to deployment. The third is AI Application Security, which safeguards the integrity of AI applications from potential threats. The final pillar, Privacy, emphasizes compliance with data protection regulation and responsible data handling.

Application in Network Copilot

Network Copilot implements the AI TRiSM methodology, functioning as a conversational AI tailored for modern network systems. It is designed for compatibility with existing systems and aims to provide reliable and secure operations.

Documentation and Monitoring

Proper documentation and monitoring of AI models are critical aspects of the AI TRiSM framework, enhancing accountability and performance tracking.

Lifecycle Management

Establishing a defined lifecycle for AI models is crucial for ensuring ongoing effectiveness and reliability, from development through to regular maintenance.

Bias and Data Handling

Implementing systematic checks to identify and balance biases is important within the AI TRiSM framework. Furthermore, responsible data handling is necessary to maintain trust and security in AI processes.

Conclusion

The AI TRiSM framework provides a structured approach to ensuring that AI technologies, such as Network Copilot, are developed and managed responsibly. This summary reflects essential components for IT professionals seeking to integrate AI solutions responsibly.

Blog post, originally published by Aviz Team at Aviz Networks.

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