As organizations increasingly adopt AI tools for automation, analytics, and decision-making, protecting sensitive data before AI processing has become a critical security and compliance requirement. Many enterprises handle confidential information such as customer records, financial data, healthcare details, and internal business documents that should not be directly exposed to external AI models or third-party platforms. I’m looking for best practices and recommended deployment architectures for securing sensitive data before sending it to AI or LLM-based systems. Some areas I’m interested in include: Data anonymization and masking techniques Tokenization or redaction of personally identifiable information (PII) Secure AI gateway or proxy architectures Splunk monitoring for AI data access and compliance events Logging and auditing AI interactions On-prem vs cloud AI deployment considerations Integration with enterprise security tools and SIEM platforms GDPR, HIPAA, or AI governance compliance strategies We are exploring ways to build a privacy-first AI workflow where sensitive information is filtered or anonymized before AI analysis while still maintaining useful output quality. I would appreciate recommendations, architecture examples, Splunk integrations, or real-world implementation experiences from the community regarding secure enterprise AI deployments and compliance monitoring.         ... View more