AI TRiSM: ARTIFICIAL INTELLIGENCE TRUST

Artificial intelligence, which has been developed with different methods in almost all fields for the comfortable life of humanity, is a tremendous invention aiming to make the workforce more efficient. Nevertheless, throughout the development process of artificial intelligence, it has also brought with it a concern that has been reflected in movies and various branches of art by being seen as a “danger to trust”. Given the current state of artificial intelligence research, this concern is sometimes justified, and there are various views on the ethics of artificial intelligence. Of course, according to experts, a parallel path is also possible, where trust, responsibility and security issues can be addressed in the development and use of artificial intelligence. AI TRiSM is an acronym that stands for AI, Trust, Responsibility and Security Model. AL TRiSM, an important artificial intelligence concept that will be heard very often by organizations in the near future: AI Trust has already taken its place in the field terminology.

Global research firm Gartner explains the term as follows: “AI trust, risk and security management (AI TRiSM) ensures AI model governance, reliability, fairness, trustworthiness, robustness, effectiveness and data protection. This includes solutions and techniques for model interpretability and explainability, AI data protection, model operations, and resilience to adversary attacks.” In other words, AI Trust can be defined as a concept that deals with the security, risk management and safety management of artificial intelligence (AI) systems. Experts consider AI trust to be about guiding the development of AI in a systematic, controlled and reliable way.

According to Gartner’s AI predictions announced this year, by 2026, organizations that organize their AI activities by prioritizing transparency, trust and security will be able to achieve their business goals with a 50% improvement. These results indicate that on the road to success, organizations should not only invest in AI, but also scrutinize how and in what direction they implement their investments. For this reason, experts continue to carry out various studies on the ability of organizations to use artificial intelligence applications within the framework of artificial intelligence trust.

As well as the advantages of AI applications, there are, of course, risks. Technical risk is the potential for problems caused by factors such as technical defects, obstacles, or shortcomings encountered during the implementation of a project or technology. These risks can arise from technical requirements or the implementation of systems during the implementation phase. They can be identified, anticipated and managed in a professional manner with the equipment required by technical expertise. Emergency plans and security protocols are critical in this sense. Technical risk management can be supported by creating recovery plans and various crisis scenarios for situations such as system crashes or data loss. Monitoring and managing risks should be seen as a never-ending process, and risk assessments should be updated and renewed at every stage of projects, and structured to ensure continuity. Trainings should be prepared for all these risk management efforts without interruption. Identifying and effectively managing technical risks as well as managing them ethically is an important factor that can affect the success of the project.

For organizations, having expert support in AI applications, managing risks, identifying and managing technical risks, and taking measures to ensure trust in AI can help ensure the successful completion of projects. Yet it may not be enough on its own. As technological advancements increase, their implementation problems will create new variations and various consequences in business life. To minimize the impact of unexpected problems, it is a worthwhile investment to enlist the support of expert organizations experienced in keeping up with innovations, updating protocols, improving the reliability and stability of projects, and effectively implementing AI efforts. Above all, organizations should consider budgeting for AI trust solutions or proactive practices as working capital.