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In todayu2019s digital landscape, data breaches are more sophisticated than ever. Traditional DLP (Data Loss Prevention) solutions often fall short when dealing with complex threats and dynamic user behavior. This presentation explores how AI and Machine Learning are revolutionizing DLP security by enabling real-time threat detection, intelligent data classification, and proactive response mechanisms. Learn how these technologies enhance traditional DLP systems, reduce false positives, and adapt to evolving risks across cloud and hybrid environments. Discover use cases, benefits, and challenges as
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Data breaches are evolving—so must our defenses. Traditional DLP security relies on static rules and patterns. AI and Machine Learning (ML) offer adaptive, intelligent protection.
Data Loss Prevention (DLP) ensures sensitive data isn't leaked, misused, or lost. Focus areas: Data in motion (network traffic) Data at rest (databases) Data in use (endpoints) DLP Security combines policy enforcement, monitoring, and alerts.
AI identifies anomalies in user behavior (e.g., unusual data transfers). ML continuously learns patterns to detect unknown threats. Enables real-time decisions, reducing false positives.
LARANA, INC. Smarter classification of sensitive data Proactive threat detection Automated policy adjustments Scalable to cloud and hybrid environments
Flagging insider threats by spotting unusual access times Preventing data exfiltration via unapproved apps or channels Detecting misuse of confidential files on endpoints
Requires quality data to train ML models Must balance privacy and monitoring Integration with existing security infrastructure is essential
AI and ML make DLP Security more intelligent, efficient, and adaptive. As threats grow more complex, so should our defenses. Smart DLP isn’t the future—it’s the now.
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