In this month's Masterclass we do a deep dive into an issue impacting data engineers with the advent of AI - What does it mean for data privacy?
Hosted by Julien Redmond from Ignition, we hear from ADAPT Group's Sam Redmond and Data Design Consultancy's Richard Harris to discuss the nuance of of how AI needs access to Personal Information to work and how that can be squared off with data privacy concerns.
1. Transparency and Accountability in AI Data Use: Discusses the importance of transparency and accountability when using AI with personal data, emphasizing the need for clear guidelines and policies to ensure responsible data collection and usage.
2. Privacy Impact Assessments (PIA) and Organizational Responsibility: Introduces the concept of PIAs as a way to identify and mitigate risks when handling sensitive data, stressing that organizations are ultimately accountable for the data they collect and how it is used.
3. Trust and Privacy by Design: Highlights the critical role of trust between organizations and the public, advocating for “Privacy by Design” practices that establish secure data workflows and transparent communication around data use.
4. Access Control and Risk Mitigation: Explores how establishing strict access controls for AI agents can help prevent unauthorized data exposure and security breaches, reinforcing the need for continuous review and adjustment.
5. Challenges of Keeping Pace with AI Innovation in Regulation: Examines the difficulty of aligning regulatory frameworks with rapid advancements in AI, urging regulators to adopt AI tools themselves to maintain relevancy and control.
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