Panel: Securing the Foundation: The Role of Quality Data in Driving Reliable AI Outcomes

As organizations increasingly leverage artificial intelligence to transform industries, the quality and security of the underlying data have become critical. This panel will explore the dual challenges of ensuring data integrity and protecting sensitive information, while emphasizing how robust data governance directly impacts the reliability and ethical use of AI. Experts will discuss strategies for safeguarding data pipelines, mitigating risks of biased or incomplete data, and navigating regulatory landscapes in a data-driven AI ecosystem.

Key Discussion Points:

1. The Nexus of Data Security and Quality:
   How secure and high-quality data underpins effective AI applications and reduces risks of inaccuracies or harmful outcomes.
2. Data Integrity in AI Development:
   Ensuring datasets are complete, unbiased, and protected from tampering throughout the AI lifecycle.
3. Emerging Threats to Data Security in AI:
   Exploring vulnerabilities in AI data pipelines, including adversarial attacks, data poisoning, and theft.
4. Best Practices for Data Governance in AI Projects:
   Real-world examples of how organizations manage data quality, security, and privacy to enable successful AI initiatives.
5. Future-Proofing AI with Secure and Ethical Data Practices:
   Innovations and tools for improving data security while scaling AI applications responsibly.