AI TRiSM Explained: AI Trust, Risk & Security Management

AI Trust, Risk and Security Management (AI TRiSM) is an emerging technology trend that will revolutionize businesses in coming years. 

The AI TRiSM framework helps identify, monitor and reduce potential risks associated with using AI technology in organizations — including the buzzy generative and adaptive AIs. By using this framework, organizations can ensure compliance with all relevant regulations and data privacy laws.

In this article, you'll learn what AI TRiSM is, how it works, and how organizations can use it for their benefit.

What's AI Trust, Risk, and Security Management (TRiSM)?

Gartner defines AI TRiSM as: a framework that supports AI model governance, trustworthiness, fairness, reliability, robustness, efficacy and data protection. 

This technology trend helps detect potential risks associated with using AI models while also guiding how to mitigate those risks. (Just consider what ChatGPT means for cybersecurity.) By this, organizations can ensure that decisions are based on reliable data sources, leading to realistic and authentic outcomes for every process.

According to Gartner, organizations that incorporate this framework into business operations of AI models can see a 50% improvement in adoption rates due to the model’s accuracy. 

What can organizations do with AI TRiSM?

AI models are vulnerable to cyberattacks. That means cybercriminals can victimize AI models to automate and optimize malicious processes, such as:

Around 236.1 million ransomware attacks occurred globally in the first half of 2022, which shows a dramatic increase from previous years. And this is due to the widespread adoption of new technologies without any safety implementation. 

This is where AI TRiSM is needed — it allows businesses to use AI models securely and safely. Its framework comprises techniques that create a secure foundation for AI models. By including measures such as data encryption, secure data storage and multi-factor authentication, TRiSM ensures the production of accurate outcomes from AI models. 

By providing a secure platform for AI, companies can focus on using these models to drive growth, increase efficiency and create better customer experiences. They can also achieve their improved goals. For example, AI TRiSM provides an automated way to analyze customer data, allowing businesses to quickly identify trends and opportunities to improve their products and services.

With this framework, your organization can maximize the value it gets from its data by using advanced analytics and machine learning algorithms to uncover insights and trends.

AI TRiSM use cases & real-world examples

There are two use cases that demonstrate the power and potential of AI TRiSM. These two examples show how organizations have started using AI TRiSM to drive innovation, improve outcomes, and create value for businesses and society.

Use case 1: AI models that are fair, transparent, accountable

The Danish Business Authority (DBA) wanted to ensure its AI models are fair, transparent, and accountable. So, they created a process for infusing its AI models with high-level ethical standards. To achieve this, DBA has tied its ethical principles to concrete actions, such as:

  • Regularly checking model predictions against fairness tests.
  • Setting up a model monitoring framework. 

They used these strategies to deploy and manage 16 AI models that monitor financial transactions worth billions of euros. This approach not only helped DBA ensure that its AI models are ethical, but it also helped build trust with its customers and stakeholders.

(Find out what ethics & governance means in AI.)

Use case 2: AI models that create explainable cause-and-effect relationships

Abzu is a Danish startup that has built an AI product capable of generating mathematically explainable models that identify cause-and-effect relationships. Their clients use these models to validate results efficiently, which has led to the development of effective breast cancer drugs. 

Abzu's product can analyze large amounts of data and identify patterns and relationships that might not be immediately apparent to humans. And doing so helps their clients make more informed decisions and develop better treatments for patients. 

The explainable models generated by Abzu's AI product can also help build trust with patients and healthcare providers, as they provide a clear understanding of how the AI arrived at its conclusions.

The AI TRiSM Framework

The AI TRiSM framework has 4 pillars.

  1. Explainability or model monitoring
  2. Model operations
  3. AI application security
  4. Model privacy

By following the framework's four pillars, your organization can build trust with its customers while benefiting from artificial intelligence's upcoming technologies.

Explainability/model monitoring

Model monitoring and explainability focus on making AI models more transparent — meaning that the AI models can provide clear explanations for their decisions or predictions. 

It involves regularly checking the AI models to ensure they work as intended and do not introduce biases. This further helps in understanding how the AI models perform and make informed decisions.

Model operations

Model operations involve developing processes and systems for managing AI models throughout their lifecycle, from development and deployment to maintenance. Maintaining the underlying infrastructure and environment, such as cloud resources, is also a part of ModelOps to ensure that the models run optimally.

AI application security

Since AI models often deal with sensitive data, and any security breaches could have serious consequences, application security is essential. AI security keeps models secure and protected against cyber threats. So, organizations can use TRiSM's framework to develop security protocols and measures for safeguarding unauthorized access or tampering. 


Privacy ensures the protection of data used to train or test AI models. AI TRiSM helps businesses develop policies and procedures to collect, store and use data in a way that respects individuals' privacy rights. 

This is becoming important in industries such as healthcare, where sensitive patient data is processed using diversified AI models.

Key AI TRiSM actions for companies to consider

These are best practices that help you maximize the possibilities for AI TRiSM.

Setting up an organizational task force

Businesses should start setting up an organizational task force or dedicated unit to manage their AI TRiSM efforts. This task force or dedicated team should develop and implement tested AI TRiSM policies and frameworks. 

Your task force must 100% understand how they have to monitor and evaluate the effectiveness of those policies and establish procedures for responding to any changes in case of any incidents. For example, your task force should educate employees on the implications and potential risks of using AI technologies and how to use those technologies.

Maximizing business outcomes through robust AI TRiSM

Companies should not just be focused on meeting the minimum legal requirements. Instead, they should focus on implementing measures to ensure their AI systems' security, privacy, and risk management. This will help better manage the AI systems and maximize the business outcomes. 

For example, an AI system designed to analyze customer data should have the appropriate security measures to protect the customer data from unauthorized access or misuse.

(See how the governance, risk & compliance trifecta relates to this.)

Involving diverse experts

Since various tools and software are used to build AI systems, many stakeholders — tech enthusiasts and data scientists, business leaders and legal experts — should participate in the development process. 

You can create a comprehensive AI TRiSM program by bringing together different experts because they understand the technical aspects of AI and the legal implications. For example…

  • A lawyer could provide advice on compliance and liability.
  • A data scientist could assess the data needed to train the AI.
  • An ethicist could develop guidelines for the responsible application of the technology.

Prioritizing AI explainability & interpretability

Your company should make its AI models explainable or interpretable using open-source tools or vendor solutions. By understanding the inner workings of models, you can ensure that the models act ethically and responsibly, which will help protect both customers and the company itself. 

For example, AI explainability tools can provide insight into which input variables are most important for a given model and indicate how a model's output is calculated.

Tailoring methods to use cases & components

Data is valuable, and AI models rely heavily on it to make accurate predictions and decisions. This means that companies must prioritize data protection to prevent unauthorized access, misuse and theft of data used by their AI systems. 

Implementing solutions such as encryption, access control and data anonymization can help keep data safe and secure while ensuring compliance with data privacy regulations. However, different use cases and components of AI models may require other data protection methods. 

By preparing to use different data protection methods for different use cases and their components, companies can ensure that their AI systems are secure and protect customer privacy and reputation. 

Ensuring data and model integrity & reliability

When building and deploying AI models, you should focus on their performance and accuracy and the potential risks they may pose to the organization. So, it's crucial to incorporate risk management into the model's AI operations.

One way to do this is by using solutions that assure model and data integrity. This means implementing security measures to protect the models and data from manipulation and ensuring that the models are accurate and reliable. For example, your organization can use automated testing to validate model accuracy and detect data anomalies or errors that can lead to inaccurate model outcomes.

Revolutionize your AI models with AI TRiSM

AI TRiSM is an emerging technology that is predicted to enhances AI models' reliability, trustworthiness, security and privacy. By using AI models more securely and safely, businesses can achieve improved goals, support various business strategies, and protect and grow their brands. 

What is Splunk?

This posting does not necessarily represent Splunk's position, strategies or opinion.

Laiba Siddiqui
Posted by

Laiba Siddiqui

Laiba Siddiqui is a technical writer who specializes in writing for SaaS companies. You can connect with her on LinkedIn and at contentbylaibams@gmail.com.