AI Strategy

With the recent progress of modern AI, it has become increasingly important to pay attention to the strategic aspects of managing AI, from paying attention to the risks of AI, to thinking about precautions with outlook of prevention over cure.

AI Trust, Risk, Security Management (AI TRiSM)

As artificial intelligence (AI) continues to advance, concerns regarding its reliability, trustworthiness, fairness, and security become paramount. AI Trust, Risk, and Security Management (AI TRiSM) is a set of solutions designed to proactively identify and mitigate these risks. In the dynamic landscape of AI applications, the need for a comprehensive TRiSM program is evident, especially as organizations often grapple with unforeseen risks once AI models are deployed. Recognizing the significance of governance upfront, AI TRiSM ensures that AI systems not only comply with regulations but also prioritize fairness, reliability, and data privacy. This multifaceted approach is crucial in instilling trust in AI technologies. AI TRiSM is also considered one of Gartner's top 10 strategic technology trends for 2024.

The 4 Pillars of AI TRiSM

• Explainability/Model Monitoring - The explainability aspect ensures that AI decision-making processes are understandable, providing insights into how and why specific conclusions are reached. Simultaneously, model monitoring involves continuous scrutiny of AI models to ensure their ongoing alignment with intended behavior. By enhancing interpretability, these pillars not only instill confidence in end-users but also facilitate regulatory compliance.

• ModelOps - ModelOps addresses the operational challenges associated with deploying and managing AI models throughout their lifecycle. This facet emphasizes the importance of operationalizing AI, ensuring scalability, version control, and performance monitoring.

• AI Application Security - By integrating security practices into the development and deployment phases, AI application security not only protects against external threats but also promotes the overall resilience of AI systems in the face of evolving cybersecurity challenges.

• Privacy - Privacy emphasizes the responsible and ethical handling of sensitive data processed by AI systems. This component ensures compliance with data protection regulations and employs privacy-preserving techniques to safeguard user information.