This article is part of a series of written products inspired by discussions of cybersecurity and artificial intelligence working group sessions. Additional ideas and perspectives of this series are accessible here.
In recent months, the working group has evaluated how to best integrate artificial intelligence (AI) with cybersecurity, the search for areas of deep advantages, potential and safety risk. We have made it possible to understand and exercise tolerance at risk in evolving governance approaches in a manner that balances the risks and rewards of AI. We believe that this approach also allows the creation of holistic and resilient solutions that can effectively respond to the complexities of our dynamic cybersecurity and improved by AI and digital ecosystems.
While the working group turned to governance solutions at the link between AI and cybersecurity, three critical areas emerged: the security of AI infrastructure and development practices, promoting responsible AI applications and improving the efficiency of labor and skills development. This exploration assesses progress and identifies persistent challenges, offering tailor -made recommendations for political decision -makers responsible for navigating these subtleties to promote the advancement of AI and exploit its full potential.
1 and 1 Secure AI infrastructure and development practices
Effective safety measures and practices for AI systems are multi-layerincluding the protection of data, models and network systems from incidents such as unauthorized access And cyber attacks. Due to potential security problems in this area, AI development practices Must prioritize security and respect ethical standards throughout its life cycle. Growth awareness Among the organizations, users and political decision-makers on the need to implement complete cybersecurity strategies that cover both physical and cyber-defense defenses is a positive trend.
However, the challenges remain better to guarantee the infrastructure and the development of the AI. A main challenge is globally audit And assessment Capabilities of the ia system. The absence of universally adopted audit norms and reliable metric creates potential inconsistencies in AI assessments, which are crucial To identify vulnerabilities and ensure robust cybersecurity.
We have several recommendations to meet this challenge and support the current governance efforts. First of all, we recommend obtaining support for the government and private sector sector in research and standardization initiatives in IA security and security. Targeted efforts to develop reliable measures to assess data security, model protection and robustness against attacks would provide a base for more coherent audit practices. Continuous efforts, such as those by the US Ai Safety Institute Consortium “(Develop) guidelines for red equipment, capacity assessments, risk management, security and security and synthetic content of the watermark,” must be encouraged and funded in an appropriate manner. Such investments could also facilitate the creation and generalized adoption of balanced and complete standards and executives for AI risk management, based on existing initiatives such as the Profile of AI risk management standards for AI systems for general use and foundation models and the National Institute of Standards and Technology IA risk management framework.
In addition, the evaluation of security risks associated with the development of AI with open source and to be closed is necessary to promote transparency and robust security measures that mitigate potential vulnerabilities and ethical violations. Understand the risks and combination opportunities Great language models With other cybersecurity capacities of AI and inheritance, will refine the development of enlightened safety strategies. Finally, the development of IA security frameworks adapted to the unique needs and vulnerabilities of different industries may explain the risk and regulatory requirements specific to the sector, ensuring that AI solutions are secure and flexible.
2 Promote responsible use of AI
Promoting Manager The use encourages organizations and developers to respect best voluntary practices in ethical development, deployment and management of AI technologies, ensuring support for safety standards and proactively countering potential potential use. Integrate ethical practices throughout the life cycle of AI systems strengthens confidence And responsibility While AI applications continue to develop through Critical infrastructure Sectors.
Despite significant extensions in AI cybersecurity applications, continuous challenges have hampered responsible use of AI. THE absence clear definitions and standards, in particular with key terms such as “open source», Bases various safety practices which can make efforts of compliance heavy or impossible. Obsolete inheritance systems Often cannot support emerging AI security solutions, leaving them vulnerable to exploitation. In addition, as Cloud Computing becomes more and more integral with the deployment of the AI system because of its scalability and its efficiency, ensuring that AID Applications On these platforms, robust cybersecurity practices have been difficult. For example, security vulnerabilities In the code generated by AI-AI, appeared as a concern for cloud safety.
To overcome these challenges, we encourage a multifaceted approach that includes in -depth standards and security processes. The development of clear and widely accepted definitions and advice would lead to more coherent and ethical security practices in all AI applications in the cybersecurity sector and beyond. The modernization of systems inherited to adapt to responsible IA principles will guarantee that these systems can support emerging security updates and responsible standards of use. Given the emerging field of IA security, monitoring discoveries of new security problems or new threat actor techniques to attack AI systems will guarantee that organizations are ready to protect their systems. In addition, encouraging innovations on cloud safety to take advantage of AI for improved threat detection, posture management and the application of secure configuration will further strengthen cloud safety measures. The implementation of these recommendations will promote the applications of AI responsible for cybersecurity which reduce the risk and deliberate and unintentional abuses.
3 and 3 Improvement of the efficiency of labor and skills development
In progress talent shortages Reflect a notable deficit in people who can understand and use AI cybersecurity technologies. Substantial progress has already been made in the use of AI to improve cybersecurity awareness, labor efficiency and skills development. For example, Simulations based on AI And educational platforms Now offer dynamic and real -time learning environments that adapt to the learner’s rate and highlight the areas that require additional concentration. These advances also made training more accessibleallowing a broader range and facilitating continuous education on the latest threats and developments in AI.
Although this progress is encouraging, additional education and awareness can improve the understanding of organizational leaders from the moment when and how to guide the integration of AI into cyber-work as well as in organizational practices, considering the different recommendations and regulations that govern these implications. This is particularly the case for small and medium -sized enterprises, where Resource constraints And Regulatory compliance challenges Can limit the ability to implement AI effectively compared to larger entities.
We recommend several solutions to meet these challenges. The complete development of labor and training on the intersection of cybersecurity laws, ethical considerations and AI should guarantee that all levels of labor – in particular those of government and military roles as well as entrepreneurs and suppliers serving these sectors – understand the implications of the deployment of IA solutions within the legal, ethical and security limits. The training and skills focused on AI for cybersecurity labor should also be promoted to accelerate the training process and prepare the workforce for current and future challenges. Finally, organizations should learn to take advantage of AI to transform cybersecurity practices through modeling, simulation and innovation. The development and use of AI for cybersecurity applications, such as digital twins For cyber-men’s analysis, should be encouraged and supported by continuous investments. These additional recommendations guarantee that the cybersecurity workforce is equipped with advanced IA-focused solutions and remains sensitive to emerging cybersecurity threats.
The upcoming road
Obviously, the regulations on AI always take shape, even while our technological capacities in AI and cybersecurity continue to advance quickly. During the next decade, we are planning the emergence of autonomous AI agents And more sophisticated AI capacity assessments (among other developments) which will create optimism and the need for continuous preparation.
Significant progress has been made in the governance of AI-CYBERSECURY to guarantee IA infrastructure and development practices, to promote responsible AI applications and to improve the efficiency of labor and skills development. These efforts have thrown a solid base for the integration of AI into cybersecurity. However, there is still a long road to come. Employees through government, industry, academic world and civil society should pursue an appropriate balance between security and innovation principles. Political decision -makers and cybersecurity leaders, in particular, must remain proactive in updating governance executives and approaches to ensure the safe and innovative integration of AI technologies. By prioritizing adaptability and continuous education in our strategic approaches to the security of the security of the IA-CYBERSECURY, we can effectively exploit the transformer potential of the AI to guarantee our technological leadership and our national security.
The group considers a range of subjects ranging from uses to guarantees, the intention to identify current and future cases for cybersecurity AI applications and to offer best practices to respond to concerns and weigh them against potential advantages.