This article critiques traditional policy-based data loss prevention (DLP) methods, arguing they can't adapt to the complexity of modern data. It introduces ORION, a solution that uses AI agents to provide context-aware detection of data exfiltration incidents, improving accuracy and reducing false positives. ORION learns organizational data patterns and integrates various data sources for comprehensive protection.

An exploration of a poorly designed web application reveals how it evolved over a decade from a standard report page to a dangerously open SQL interface, allowing users to execute arbitrary queries. This transformation was driven by continuous feature requests and inadequate security measures, ultimately leading to chaos and data loss. The author's experience highlights the risks of neglecting security in software development and the consequences of poor design decisions.

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