Panel Topic: The Role of Quality Data in Enhancing AI-Driven Cybersecurity

In an era where cyber threats are growing in complexity and scale, Artificial Intelligence (AI) has become a critical component in modern cybersecurity frameworks. However, the effectiveness of AI-driven threat detection, risk assessment, and automated response systems is heavily dependent on the quality of data being processed. Poor data quality characterized by inconsistencies, inaccuracies, or biases can lead to false positives, missed threats, and flawed security decisions, ultimately undermining the efficacy of AI solutions.

This panel will explore why high-quality, well-governed, and diverse data is essential for leveraging AI in cybersecurity. Experts will discuss best practices for data collection, validation, and governance, ensuring that AI models are trained on reliable datasets to enhance cyber defense mechanisms. Key topics will include:

• The AI-Cybersecurity Dependency: Understanding how AI models rely on structured and unstructured data for detecting and mitigating threats.
• Challenges of Data Quality: Identifying risks associated with poor data hygiene, including biases, outdated threat intelligence, and misinformation.
• Data Governance & Integrity: Strategies for improving data accuracy, completeness, and timeliness to optimize AI-driven security decisions.
• Real-World AI Failures & Lessons Learned: Examining case studies where compromised or low-quality data led to AI-driven security failures.
• Regulatory and Compliance Considerations: Ensuring cybersecurity AI adheres to global data privacy and protection frameworks.