Developer Builds Self-Learning DLP Agent That Cuts Redundant LLM Alert Processing

A software developer has built a Data Loss Prevention (DLP) triage agent designed to eliminate repetitive, costly LLM processing of security alerts by giving the system autonomous memory. Traditional DLP tools treat every alert as new, forcing analysts to manually correct the same false positives repeatedly with no feedback loop to improve future decisions. The four-stage pipeline — covering metadata analysis, algorithmic checks, memory recall, and LLM triage — short-circuits processing as early as possible, with the memory stage returning cached verdicts in roughly 50 milliseconds at zero token cost. Each analyst decision is automatically distilled into a human-readable pattern file, allowing the system to recognize recurring scenarios without any manual rule-writing or model fine-tuning. The developer claims the approach significantly reduces token usage by reserving LLM processing only for genuinely novel alerts.