A Practical Guide to Trust, Risk, and Security in Artificial Intelligence.
With the advent of AI technology in business operations comes a new challenge. How can companies ensure that AI continues to be used productively and legally? Designing an AI algorithm is one thing; however, properly managing it once it is around is entirely something else.
This is where AI TRiSM (AI Trust, Risk, and Safety Management) comes in.
AI TRiSM is an approach to governance that allows companies to monitor, protect, and manage AI during its lifetime. It focuses not only on the technical capabilities of the AI system. It encourages business leaders to ask bigger questions such as whether the AI system can be trusted, whether it brings unexpected dangers, whether personal information is safeguarded, and whether there can be an explanation of the decisions made.
Why AI TRiSM is Important
Consider an AI model used to scan resumes submitted for a job. It can analyze hundreds of resumes in the matter of minutes, which is very time-saving for recruiters. However, in case the AI model cannot explain the hiring decision or favors a limited circle of candidates, the organization will have to address much more than merely a technical problem.
AI TRiSM is aimed at minimizing said risks by advising companies to adopt a continuous monitoring approach instead of relying on the idea that an AI system will behave as expected.
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Governance is not Merely a Technical Solution
Companies have a misconception that AI governance means that IT specialists will do the job. In reality, effective implementation of AI TRiSM requires cooperation among leaders, IT managers, compliance specialists, cyber security and risk management experts.
The collaborative approach is becoming especially valuable amidst the growing expectations of regulators and increasing demands for transparency on the part of customers and investors.
Trust Requires Continuous Monitoring
No governance model can manage all the risks at once. AI models evolve, business environment varies, and new threats arise.
Thus, the governance of AI must be perceived as a process rather than a single event.
In fact, firms that make AI trustworthy are likely to reap the rewards even in terms of national regulations.
