Data Loss Prevention (DLP) has been a cornerstone of enterprise security for over two decades, yet its foundationaltechnology-regular expression (regex) pattern matching, keyword blocklists, and exact-match fingerprinting-wasdesigned for an era of structured, predictable data flows. The explosion of unstructured data, GenAI-poweredworkflows, and shadow AI adoption has exposed the fundamental limitations of pattern-based DLP: industry datashows that legacy DLP systems achieve 5–25% accuracy on unstructured content classification, generate false positiverates exceeding 40% on complex data types, and provide zero visibility into GenAI prompt-based data exfiltrationchannels. This paper introduces the paradigm of AI-Native DLP-a fundamental architectural shift from regex-basedcontent inspection to LLM-driven semantic understanding for enterprise data exfiltration detection. We present acomprehensive analysis comparing three generations of DLP technology across seven data categories and sevenexfiltration channels, demonstrating that LLM-driven semantic inspection achieves 82–98% detection accuracy acrossall content types (compared to 8–96% for regex), reduces false positive rates from 37–42% to 3.5–5% over twelvemonths of production deployment, and extends coverage to previously undetectable channels including GenAIprompts, browser-based paste operations, and paraphrased confidential data. We evaluate the architectural patterns,latency characteristics, cost implications, enterprise deployment challenges, regulatory compliance alignment, insiderthreat detection capabilities, LLM model selection trade-offs, and shadow AI governance of AI-native DLP, andpresent a maturity model for organizations transitioning from legacy to semantic-first data protection. Our analysisdraws on published performance data from Nightfall AI, Lakera, Cyera, Concentric AI, Cloudflare AI Gateway, andMicrosoft Purview, alongside academic research on LLM-based content classification and the OWASP frameworkfor LLM application security