Shipping More AI Code Than You Can Secure? Watch How to Control Remediation Debt

If your developers are using AI coding tools, you are probably already seeing the upside: faster development, more code, and less time spent on routine work. The harder part is what comes after. AI can also introduce open-source packages at a pace your security team was never built to handle. More dependencies mean more vulnerabilities to review, more remediation work, and a backlog that can

The article describes how AI coding tools accelerate development while automatically introducing more open‑source dependencies, creating a larger surface of third‑party components and vulnerabilities for security teams to manage. It highlights that this rapid increase in packages and transitive dependencies leads to accumulating remediation backlogs and difficulty keeping up with vulnerability review and patching. From a RealGround perspective, this reflects an AI supply chain risk pattern where AI-assisted development magnifies dependency sprawl, making software bills of materials (SBOMs), dependency governance, and automated risk triage essential. Organizations should implement structured AI supply chain controls—such as SBOM-driven monitoring, risk-based remediation workflows, and policy-driven use of AI coding tools—to keep remediation debt and exposure at an acceptable level.