What are the challenges of implementing DLP in large enterprises, such as the complexity of managing policies across many departments, locations, and types of sensitive data?

How can DLP solutions be integrated with data classification tools, ensuring that sensitive data is automatically classified and protected as it is stored, processed, or transmitted?

What are the best practices for responding to data leak incidents, such as having an incident response plan, conducting a root cause analysis, and notifying affected parties as required?

How can DLP assist in achieving compliance with the General Data Protection Regulation (GDPR) by enforcing policies that prevent unauthorized access to personal data and ensuring data protection across all platforms?

What are the key features to look for in a DLP solution, including data classification, real-time monitoring, policy enforcement, and integration with other security tools like SIEM or encryption?

How can organizations handle false positives in DLP systems, reducing unnecessary alerts and fine-tuning policies to improve accuracy and minimize disruptions to legitimate activities?

What are the limitations of DLP solutions, such as the inability to protect against all types of data loss or challenges with encrypted data, and how can these be addressed?

What are the best practices for data classification in the context of DLP, ensuring that data is appropriately labeled based on its sensitivity level and protecting it accordingly?

How can DLP integrate with existing Security Information and Event Management (SIEM) systems, providing real-time monitoring and correlation of data loss events with security incidents?

How can DLP solutions protect against data leaks through instant messaging applications, monitoring and blocking the transmission of sensitive data through platforms like Slack or Microsoft Teams?