In public institutions, Generative AI holds disruptive potential, enabling organizations to quickly gain insights from vast amounts of data and enhance service efficiency. However, given that public sectors handle a large volume of sensitive data, securely implementing Generative AI poses a significant challenge. AWS shared five best practices for public institutions to ensure the safe and compliant use of Generative AI in a blog post.
- Data Privacy and Security as a Top Priority
Since Generative AI requires processing large-scale data, data privacy and security are paramount. Institutions must adopt a zero-trust architecture, encrypt data during storage and transmission, and establish strong access controls to limit the handling of sensitive data. Additionally, ensuring data anonymization can reduce the risk of data breaches. - Maintain Human Oversight to Ensure Fairness
AI models may unintentionally introduce data bias, which is particularly concerning for the public sector. When implementing Generative AI, institutions should adhere to the principle of data minimization, collecting only the necessary data, and verifying the sources. To avoid bias, institutions should ensure the transparency and auditability of AI models and maintain human involvement in critical decision-making processes. - Foster a Culture of Innovation and Upskill Workforce
Before adopting Generative AI, public institutions must cultivate a culture of innovation. This can be achieved by establishing a "center of excellence" dedicated to advancing the application and technological innovation of Generative AI. Furthermore, retraining opportunities should be provided to employees, equipping them with AI skills and alleviating concerns over AI replacing jobs. - Build Modern Digital Infrastructure and Update Governance Framework
To support Generative AI, institutions need a robust digital infrastructure, including strong security architectures and data governance. This includes utilizing APIs for data exchange and ensuring the modernization of applications and data models to support Generative AI deployment. - Establish AI Cost Control Early
The operational costs of Generative AI are similar to cloud computing, where charges are based on model usage. Institutions should understand the cost model before implementing AI and set up effective monitoring tools to track cost usage, avoiding budget overruns.
Conclusion and Recommendations
Generative AI holds significant potential in public institutions, from data analysis to process automation, leading to notable efficiency improvements. However, when implementing these technologies, institutions must also consider security, privacy protection, and compliance. Here are some additional recommendations to help public institutions move forward with secure deployment of Generative AI:
- Dynamic Assessment and Continuous Improvement: AI technology evolves rapidly, so public institutions need to establish dynamic assessment mechanisms to stay updated on the latest security vulnerabilities, regulatory requirements, and technological advancements. Regularly update AI models and security strategies to ensure they continually meet business needs and legal standards.
- Cross-Department Collaboration and Standardization: Implementing Generative AI often involves multiple departments. Institutions should establish cross-departmental collaboration mechanisms to ensure consistency in data sharing and technology application. Unified standards and policies help enhance overall security and efficiency.
- Risk Assessment and Contingency Planning: Before deploying Generative AI, institutions should conduct risk assessments to identify potential technical failures or security risks. A comprehensive contingency plan should be in place to ensure quick response and recovery, preventing business interruptions or data breaches.
- Third-Party Audits and External Professional Support: Public institutions may consider bringing in external professional organizations for technical and security audits to ensure AI applications meet the latest security standards and regulatory requirements. Additionally, working closely with cloud service providers and AI technical teams can provide strong support for secure deployment.
By following these measures, public institutions can advance the implementation of Generative AI safely and efficiently, leading to successful digital transformation. Strengthening security measures, upskilling employees, and continuously optimizing management will be key to ongoing innovation and breakthroughs in AI applications within public institutions.
These recommendations not only ensure data and system security but also help institutions maximize the potential of Generative AI, enabling efficient and intelligent public services.
Source：https://aws.amazon.com/tw/blogs/publicsector/generative-ai-for-public-agencies-5-best-practices-for-secure-implementation/