AI TRiSM

Gartner’s AI TRiSM: Tackling Trust, Risk, and Security in AI Models

Recognizing the complexities of AI governance, industry experts like Gartner have developed specific frameworks to guide organizations in this area. Per Gartner, AI Trust, Risk, and Security Management (AI TRiSM) is a structured approach that will revolutionize businesses in the coming years. This framework, focusing on risk mitigation and alignment with data privacy laws in the use of AI, comprises four pillars: Explainability and model monitoring, Model operations, AI application security, and Model privacy.

Explainability/Model Monitoring

Model monitoring and explainability are crucial components in ensuring transparency and reliability in AI systems. They aim to provide clear explanations for the decisions or predictions made by AI models, facilitating a deeper understanding of their functioning. Regular monitoring of these models helps to verify their performance, identify potential biases, and comprehend their strengths and weaknesses. By elucidating details and reasons tailored to specific audiences, such as stakeholders or end-users, these practices enhance trust in AI systems and enable informed decision-making based on the likely behavior of the models. This also helps organizations fulfill the legal requirement of ensuring user transparency, i.e., informing users that they are interacting with an AI system, the logic of the AI system, and the rights individuals have with the help of privacy notices, instructions of use, and other similar mechanisms.

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. This involves AI system governance and AI system classification.

AI Application Security

Since AI models often deal with individuals’ data that can be personally identifiable data or 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 against unauthorized access or tampering.

Model Privacy

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. Model privacy refers to managing data flows as per privacy laws, such as fulfilling data purpose limitation, storage limitation, data minimization, and other data protection principles.

AI TRiSM is an approach that is supposed to enhance 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.