Protecting sensitive data requires a robust approach, with Data Security Posture Management (DSPM) and Data Loss Prevention (DLP) at the forefront. DSPM aligns security policies with data architecture, while DLP prevents unauthorised access and leaks.
Understanding data classification and custodianship is key to this, as it ensures that sensitive data is prioritised. Integrating AI further strengthens these strategies, offering real-time threat detection and automated protection.
In this episode, Chris Steffen VP of Research at EMA speaks to Shannon Murphy, Global Security & Risk Strategist at Trend Micro, to discuss data security management.
Key Takeaways:
Data Security Posture Management (DSPM) is essential for visibility.
Data classification is crucial for effective data security.
AI can enhance data discovery and classification processes.
Data custodianship should involve those who understand the data.
Continuous monitoring is necessary for effective data protection.
A layered defense approach is necessary against emerging threats.
Data security is an ongoing process, not a one-time fix.
Chapters:
00:00 - Introduction to Data Security Management
02:55 - Understanding DSPM vs DLP
06:12 - The Role of AI in Data Security
08:57 - Data Classification and Metadata
11:48 - Data Custodianship and Responsibility
15:11 - Creating a Culture of Security
17:57 - The Future of Data Security Strategies
In 1988, Trend Micro was co-founded by Steve Chang, Jenny Chang, and Eva Chen to develop antivirus software—but it didn’t stop there. Over the last three decades, we’ve become a market leader in hybrid cloud security, network defense, small business security, and endpoint security. IT infrastructure continues to change, user behavior becomes riskier, and threats evolve. We continuously innovate to stay one step ahead of the bad guys. Our founders have set the tone, the culture, and our road to innovation – building a strong team of people who work together to help make your connected world safer to exchange digital information.
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