In the first part of this article, I discussed the various challenges faced by developing countries in managing cybersecurity risks associated with AI. Now, in this second part, I will focus on key strategies to address these challenges. These strategies aim to overcome infrastructure unpreparedness, resource limitations, lack of expertise, immature laws and regulations, and the risks arising from the decentralization of AI development and dependence on foreign AI products.
Strengthening Infrastructure and Resource Investment
The first step is to strengthen technological infrastructure and increase investment in cybersecurity resources. Developing countries need to invest in more sophisticated cybersecurity systems and build robust IT infrastructure to support the safe implementation of AI. This includes the acquisition of state-of-the-art hardware and software for security, as well as ensuring stable and secure network infrastructure. To support the safe and efficient deployment of AI, developing countries should focus on key aspects of technology and cybersecurity infrastructure development. Firstly, enhancing network security through the procurement of advanced firewalls, intrusion detection and prevention systems, and other network security solutions, similar to what Singapore has done in implementing its national cybersecurity policy. Secondly, the importance of acquiring up-to-date hardware and software for security, such as antivirus, anti-malware, and encryption software, as demonstrated by Estonia with its strong IT and cybersecurity infrastructure.
Additionally, building secure data centers to store and manage the large amounts of data generated by AI applications is a priority, following the model of Finland which has been successful with its secure and efficient data centers. Lastly, the importance of implementing specific security protocols for AI and machine learning, a step that Israel has taken to protect its AI innovations. By adopting these approaches, developing countries can make significant strides in creating an environment conducive to the safe and responsible development of AI.
Human Resource Development and Enhancing Cybersecurity Awareness
In the effort to develop human resources in the field of cybersecurity, the main focus should be on several essential skills. IT professionals need to be specially trained in identifying and handling cyber-attacks, such as phishing and malware, as well as in network security management. These skills include in-depth knowledge of how cyber-attacks work and their prevention measures, as well as the management and implementation of security tools like firewalls and intrusion detection systems. In addition, understanding data security and privacy, which involves encryption and access management, is crucial for protecting data and IT infrastructure.
Education in digital forensics is also a priority, giving professionals the ability to trace and analyse the sources of cyber-attacks. Similarly, skills in developing secure software and application security are crucial, considering many cyber-attacks occur through vulnerabilities in applications. For the general public, raising cybersecurity awareness through information campaigns is important, emphasizing basic security practices such as using strong passwords and recognizing phishing tactics.
By prioritizing the development of these skills among IT professionals and the general public, developing countries can make significant progress in managing cybersecurity risks. This not only builds a skilled workforce to face cybersecurity challenges but also creates a society that is more aware and prepared to face cybersecurity risks associated with AI.
Stronger Legal and Regulatory Framework
Developing a robust legal and regulatory framework is a crucial step in managing cyber security risks related to AI in developing countries. This not only includes technical aspects such as personal data management and cyber security but also should involve AI ethics that align with local social and cultural conditions. The importance of AI ethics emerges from the recognition that AI is not just a technical matter but also has broad impacts on society and cultural values. Effective regulations should ensure that AI is used in a way that does not reinforce biases or discrimination and does not harm specific groups in society. An example of AI regulation development can be seen in the measures taken by the European Union, which has been a pioneer in enacting AI-related regulations. The EU’s regulations emphasize transparency, accountability, and fairness in AI use.
This policy requires AI algorithms to be auditable and explainable, ensuring that decisions made by AI can be understood and reviewed by humans. This approach emphasizes that AI must follow not only technical rules but also ethical principles and societal values. Developing countries can take inspiration from the European Union by adapting their legal and regulatory frameworks to reflect their local social and cultural needs and values.
Regulations in developing countries could include specific provisions governing the use of AI in various fields, such as education or justice, to ensure ethical and responsible AI implementation. It’s also crucial to have dialogues between policymakers, technology experts, and civil society to ensure that the regulations truly reflect the local community’s needs and aspirations in the AI era.
Promoting Local AI Development
Finally, encouraging local AI development and research is an essential step in reducing dependence on foreign AI products. This involves investing in research and development at universities and research institutions, as well as supporting local startups and tech companies to develop innovative AI solutions that meet local needs. To encourage local AI development and research in the face of budget constraints, governments of developing countries can implement various strategic incentives. One way is to provide tax incentives or subsidies for companies and startups investing in AI research. This could be in the form of tax reductions, research tax credits, or grants for specific research projects.
Additionally, collaboration between industry and universities can be facilitated to connect academic resources with the practical needs of the AI industry, such as through programs integrating students and professors in AI projects at local companies or supporting joint research centers. Incubation and acceleration programs for AI startups are also key, providing guidance, resources, and networks for young entrepreneurs. Governments can also provide access to large datasets and infrastructure like cloud computing to support AI model training. Investment in education and training at universities and other institutions is also crucial for developing local talent, including scholarship programs, internships, and specialized AI courses.
Lastly, encouraging international collaboration in AI research projects can expand local knowledge and experience and open new opportunities for researchers and AI developers in developing countries. With these steps, governments can create an ecosystem that supports AI innovation, even with budget limitations, and reduce dependence on foreign AI products while strengthening domestic innovation capacity. By adopting these strategies, developing countries can take proactive steps to face AI-related cyber security challenges. Through proper investment in infrastructure, human resources, laws and regulations, and international cooperation, these countries can ensure safe, ethical, and beneficial use of AI for all segments of society.
