Demystifying AI TRiSM: A Roadmap to Model Trust and Security

Published On Fri Nov 29 2024
Demystifying AI TRiSM: A Roadmap to Model Trust and Security

How Can You Trust ChatGPT? AI TRiSM

AI Trust Risk and Security Management (AI TRiSM) is achievable by implementing cross-disciplined practices, methodologies, and tools to AI models. It involves applying these practices to AI models and supporting data, alongside utilizing tool sets that align with these methodologies.

We recently released an updated version of our AI TRiSM Market Guide that outlines a structure for managing model trust, risk, and security. The guide also highlights various vendors in niche software categories that support this framework. You can read more about it here.

Build trust, risk, and security management into AI delivery.AITRiSMarchitecturefinalforSYMP

The methods and tools of AI TRiSM are compatible with various models, including open-source LLM models like ChatGPT or custom enterprise models that utilize different AI techniques. While open-source models have their distinctions, such as safeguarding enterprise training data on shared infrastructure for updating models for enterprise applications.

Furthermore, enterprises may lack a specific ability to regulate open-source models using ModelOps tools mentioned in the Market Guide. However, explainability, model monitoring, and AI application security tools are applicable to both open-source and proprietary models to achieve the trust and reliability essential for enterprise users. It is crucial to use these tools on third-party models and products embedding AI to uphold the integrity of solution providers and guarantee product performance as advertised.

Related:

Testing for AI Bias: What Enterprises Need to Know

The AI TRiSM market is in its early stages and segmented, with most enterprises adopting TRiSM methodologies and tools post model deployment. However, it is shortsighted not to embed trustworthiness into models from the initial stages, i.e., during the design and development phase, as this approach leads to enhanced model performance.

The future direction of the AI TRISM market.AITRiSMMarketDirection_0

As AI models become more prevalent, we anticipate that AI TRiSM methods and tools will witness broader adoption by various teams within enterprises involved in AI endeavors. Here is the anticipated roadmap for this market:

This article was originally published on the Gartner Blog Network.

Learn more about the Gartner Blog Network, a platform offering expert insights on contemporary technology, business topics, trends, and more.

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