- According to KPMG, it takes time and human expertise to unlock the full potential of generative AI.
- Malaysia lags behind its neighbors in AI adoption.
The rise of generative artificial intelligence (AI) models such as ChatGPT and DALL-E has opened up a world of possibilities, delivering unprecedented automation capabilities to enterprises at breakneck speed.
Generative AI has already found numerous uses in various industries. For example, in a recent interview with JLL Asia Pacific Data Center Managing Director Chris Street, he said: Techwire Asia We explored the impact of often-overlooked generative AI on data center demand. Demand for computing power in data centers is skyrocketing as society integrates this technology more deeply into everyday life. However, the high server computer density required to operate AI also poses challenges in terms of energy efficiency and sustainability. Together, AI and machine learning have the potential to improve data center performance and enable operations-based technologies such as liquid cooling.
Nevertheless, it is important to recognize that generative AI is not perfect. According to KPMG, it takes time and human expertise to unlock the full potential of generative AI in a responsible, trustworthy and secure manner.
A global survey conducted by KPMG involved more than 17,000 participants in 17 countries known for their AI work and readiness. Three (61%) were found to be wary of trusting AI systems. Cybersecurity risk was the main concern cited by 84% of respondents.
Generative AI running across various business functions
According to Alvin Gan, Head of Technology Consulting at KPMG in Malaysia, generative AI models offer positive benefits across various business functions including IT, human resources, operations, finance, and more. For example, these models contextualize his ESG data to support his reporting work and help the organization clearly outline his ESG efforts.
Alvin Gan, Head of Technology Consulting, KPMG Malaysia
Gan emphasized that while generative AI has expanded its uses, it is not without risks. Many of these models rely on user-supplied data to improve the underlying algorithms over time. However, this data can be used to generate responses to other users, potentially exposing your organization’s intellectual property and trade secrets. This risk is heightened if employees are not properly trained in using AI applications, including confidentiality and quality assurance measures.
Data quality and ethics are major concerns, as ownership of content processed through generative AI applications remains unclear. Unrestricted use of such applications can expose organizations to a wide range of risks related to intellectual property infringement, fraud, brand reputation, and public perception.
Gan further emphasized that users of generative AI are using the technology and playing a role in its self-learning evolution. This places a significant responsibility on the Chief Information Security Officer (CISO) to shift the focus from simply solving a problem to defining it and devising new approaches for team-to-machine collaboration. increase. These efforts are aimed at improving operational efficiency while ensuring compliance with applicable laws and professional standards.
This responsibility spans many angles, including the software developer. The advent of AI has significantly lowered the barriers to entry for individuals with diverse backgrounds and experiences. Almost anyone, even with limited existing knowledge, can transform into a junior developer with a clear vision of what they want to achieve. This democratization of access could be a positive development, especially since Singapore’s digital economy relies on the development of its developer community.
However, it is important to note that generative AI cannot replace developer experience and skill. AI transforms the way developers work, enabling them to be faster, more efficient, and happier, while maintaining control and ownership of the resulting code. To do.
Closing the Security Gap for a Digital Future
Although AI adoption is increasing in Malaysia, it still lags behind its neighbors, as shown in the Malaysian National Artificial Intelligence Roadmap 2021-2025 (AI Map) released by the Ministry of Science, Technology and Innovation (MOSTI). I am taking AI Map reports that only 16% of Malaysian organizations deploying AI have taken steps to ensure the security of their AI applications/systems. Even fewer (10%) have developed AI-specific risk management and cybersecurity policies.
Alvin concludes: “With growing concerns about security, privacy, data trust and ethics, it is important to be vigilant in ensuring that organizations can use AI while maintaining digital trust. To take full advantage of it, we need to establish the necessary guardrails for its secure implementation and use, including addressing potential cybersecurity gaps at the board level.”
Clearly, the rise of generative AI models presents both opportunities and challenges for businesses. While these models offer immense potential for automation and efficiency, there are concerns related to data quality, ethics, intellectual property, and cybersecurity. It is critical that organizations approach generative AI carefully and ensure proper training, data management, and security measures are in place.
