Video
Data Governance Approaches to Mitigating AI Risk
Data has become the lifeblood of businesses, and in turn, the lifeblood of artificial intelligence (AI) applications. Akin to a living organism, data is generated, enhanced, changed, harvested, and reused to build and operate AI systems.
In this webcast, IAPP was joined by TELUS and FTI information governance, privacy, and AI experts Nina Bryant and Luisa Resmerita who discussed pragmatic approaches to navigating data-related requirements in the context of AI innovation. They explored practical processes and tools organizations can adopt to uphold strong data governance at every junction of the AI system lifecycle, including the considerations described below, which are also covered and expanded upon in the recent publication of IAPP-FTI Technology AI Governance in Practice Report 2024. They also discussed privacy, security, and AI governance challenges specific to the telecommunications sector in light of the data-generation and connective force of the industry.
- Data privacy: Reuse of personal data in line with data minimization and purpose specification principles.
- Intellectual property: Identification and permitted use of copyrighted content such as text and/or images.
- Contractual data terms: Navigating boilerplate commercial restrictions around third-party data reuse.
- Sector-specific requirements: Data related requirements applicable to highly regulated sectors such as financial services and health care.
- Information governance: Data classification, retention, disposal and access control management strategies.
- Information security: Technical and organizational measures to mitigate the risk of compromised security of AI systems and associated data.