Enabling Threat Detection and Response

As the volume and complexity of cybersecurity threats continue to increase, organizations are turning to machine learning and artificial intelligence (AI) to augment their security monitoring and analytics capabilities. These technologies can help identify threats faster, reduce false positives, and enable security teams to respond more effectively to incidents.

In this session, we will learn:
- The various use cases for machine learning and AI in security, including threat detection, anomaly detection, and behavioral analysis
- The limitations of these technologies and best practices for implementing them effectively
- Real-world examples of how organizations are using machine learning and AI to enhance their security monitoring and analytics capabilities
- The types of data that are most effective for machine learning and AI-based threat detection
- How to interpret and act on the insights generated by these technologies
- Actionable insights and best practices for implementing machine learning and AI in their security operations

About the speaker:
Elango is an accomplished technology leader with 20+ years of experience building high-performance engineering organizations for high-tech and financial services businesses across multiple geographies. With an in-depth understanding of governance, compliance, regulatory, and cybersecurity requirements of ASEAN markets, Elango has championed several cloud implementations that meet the highest standards of security and compliance for top-tier banks across Singapore, Thailand and the United States.

As a speaker, Elango is known for his insightful and thought-provoking presentations that provide unique perspectives on the latest trends and challenges in the technology, cybersecurity and financial services industries. He is a passionate advocate for innovation, technology, and cybersecurity, and is committed to helping organizations navigate the rapidly evolving technology landscape.