Why Data Security and Privacy Need to Start in Code
Briefly

Why Data Security and Privacy Need to Start in Code
"AI-assisted coding and AI app generation platforms have created an unprecedented surge in software development. Companies are now facing rapid growth in both the number of applications and the pace of change within those applications. Security and privacy teams are under significant pressure as the surface area they must cover is expanding quickly while their staffing levels remain largely unchanged."
"These solutions frequently miss hidden data flows to third party and AI integrations, and for the data sinks they do cover, they help detect risks but do not prevent them. The question is whether many of these issues can instead be prevented early. The answer is yes. Prevention is possible by embedding detection and governance controls directly into development. HoundDog.ai provides a privacy code scanner built for exactly this purpose."
"When sensitive data appears in logs, relying on DLP solutions is reactive, unreliable, and slow. Teams may spend weeks cleaning logs, identifying exposure across the systems that ingested them, and revising the code after the fact. These incidents often begin with simple developer oversights, such as using a tainted variable or printing an entire user object in a debug function. As engineering teams grow past 20 developers, keeping track of all code paths becomes difficult and these oversights become more frequent."
AI-assisted coding and app-generation platforms have driven rapid growth in application count and change velocity, expanding security and privacy surface area without proportional staffing increases. Existing data security and privacy solutions are largely reactive, often beginning from production data and missing hidden flows to third-party and AI integrations. Proactive prevention is achievable by embedding detection and governance controls directly into development workflows. A privacy-focused code scanner can identify and prevent common issues such as sensitive data exposure in logs and inaccuracies in data maps, reducing costly remediation and compliance risks.
Read at The Hacker News
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