Security refers to ensuring the cybersecurity of AI systems, which includes mechanisms against malicious attacks specific to AI such as data poisoning, model inversion, the tampering of datasets, byzantine attacks in federated learning5, as well as other attacks designed to reverse engineer personal data used to train the AI.

personal data Protection Commission of Singapore’s (PDPC) Guide on Job Redesign in the Age of AI6 provides useful guidance to assist organisations in considering the impact of AI on its employees, and how work tasks can be redesigned to help employees embrace AI and move towards higher value tasks.

Personal data protection Commission of Singapore’s (PDPC) Guide on Job Redesign in the Age of AI6 provides useful guidance to assist organisations in considering the impact of AI on its employees, and how work tasks can be redesigned to help employees embrace AI and move towards higher value tasks.

privacy and Data Governance

AI systems should have proper mechanisms in place to ensure data privacy and protection and maintain and protect the quality and integrity of data throughout their entire lifecycle.

Data privacy and protection should be respected and upheld during the design, development, and deployment of AI systems.

The way data is collected, stored, generated, and deleted throughout the AI system lifecycle must comply with applicable data protection laws, data governance legislation, and ethical principles.

Some data protection and privacy laws in ASEAN include Malaysia’s personal data Protection Act 2010, the Philippines’ Data Privacy Act of 2012, Singapore’s personal data Protection Act 2012, Thailand’s personal data Protection Act 2019, Indonesia’s personal data Protection Law 2022, and Vietnam’s personal data Protection Decree 2023.

Some data protection and privacy laws in ASEAN include Malaysia’s Personal data protection Act 2010, the Philippines’ Data Privacy Act of 2012, Singapore’s Personal data protection Act 2012, Thailand’s Personal data protection Act 2019, Indonesia’s Personal data protection Law 2022, and Vietnam’s Personal data protection Decree 2023.

Some data protection and privacy laws in ASEAN include Malaysia’s Personal Data Protection Act 2010, the Philippines’ Data privacy Act of 2012, Singapore’s Personal Data Protection Act 2012, Thailand’s Personal Data Protection Act 2019, Indonesia’s Personal Data Protection Law 2022, and Vietnam’s Personal Data Protection Decree 2023.

Some data protection and privacy laws in ASEAN include Malaysia’s Personal Data Protection Act 2010, the Philippines’ Data privacy Act of 2012, Singapore’s Personal Data Protection Act 2012, Thailand’s Personal Data Protection Act 2019, Indonesia’s Personal Data Protection Law 2022, and Vietnam’s Personal Data Protection Decree 2023.

Organisations should ensure that necessary consent is obtained from individuals before collecting, using, or disclosing personal data for AI development and deployment, or otherwise have appropriate legal basis to collect, use or disclose personal data without consent.

data protection and governance frameworks should be set up and adhered to by developers and deployers of AI systems.

These frameworks should also be periodically reviewed and updated in accordance with applicable privacy and data protection laws.

These frameworks should also be periodically reviewed and updated in accordance with applicable privacy and data protection laws.

For example, data protection impact assessments (DPIA) help organisations determine how data processing systems, procedures, or technologies affect individuals’ privacy and eliminate risks that might violate compliance7.

For example, data protection impact assessments (DPIA) help organisations determine how data processing systems, procedures, or technologies affect individuals’ privacy and eliminate risks that might violate compliance7.

Developers and deployers of AI systems should also incorporate a privacy by design principle when developing and deploying AI systems.

privacy by design is an approach that embeds privacy in every stage of the system development lifecycle.

privacy by design is an approach that embeds privacy in every stage of the system development lifecycle.

Data privacy is essential in gaining the public’s trust in technological advances.

Another consideration is investing in privacy enhancing technologies to preserve privacy while allowing personal data to be used for innovation.

Another consideration is investing in privacy enhancing technologies to preserve privacy while allowing personal data to be used for innovation.

Another consideration is investing in privacy enhancing technologies to preserve privacy while allowing personal data to be used for innovation.

privacy enhancing technologies include, but are not limited to, differential privacy, where small changes are made to raw data to securely de identify inputs without having a significant impact on the results of the AI system, and zero knowledge proofs (ZKP), where ZKP hide the underlying data and answer simple questions about whether something is true or false without revealing additional information

privacy enhancing technologies include, but are not limited to, differential privacy, where small changes are made to raw data to securely de identify inputs without having a significant impact on the results of the AI system, and zero knowledge proofs (ZKP), where ZKP hide the underlying data and answer simple questions about whether something is true or false without revealing additional information