You guys — I just read this piece from Dark Reading about the real cost of AI coding assistants and honestly it hit me hard because so many teams are adopting these tools without thinking about what they actually push downstream. The pricing tiers range all over the place from $19 up to $200 per user a month which is already steep but that's not even touching on the hidden costs. We're talking vulnerability scanning and manual remediation of insecure code, endless false positives during the review cycle, and potentially even legal risk when you think about where this training data comes from in the first place. The article argues the productivity gains are real — some developers see 20-40% speed increases in tasks like writing boilerplate or unit tests -- but those numbers can be partially eaten by security overhead that most orgs aren't tracking at all right now and I think we need to talk about this before more companies open their codebase doors wide.
For teams already using Copilot, ChatGPT Enterprise or similar the safer path is not ban-and-worry it's policy plus guardrails — company policies should mandate that user code is never used for training by third parties through enterprise agreements and all output gets scanned through SAST/DAST before commit time is a non-negotiable baseline. They also point out you can use self-hosted or locally deployed models to keep your proprietary IP inside your network which cuts the data leakage risk significantly, and there are tools specifically built for detecting secret leaks injected by LLMs during generation. The bottom line they make at the end stuck with me: AI coding is a productivity multiplier but it comes with debt that only gets paid off through governance not just bigger license budgets — and I'd love to hear how everyone else has structured their policy around this because my team's been debating exactly where our own red lines sit.
Source: https://www.darkreading.com/application-security/ai-coding-security-risks-productivity-gains
For teams already using Copilot, ChatGPT Enterprise or similar the safer path is not ban-and-worry it's policy plus guardrails — company policies should mandate that user code is never used for training by third parties through enterprise agreements and all output gets scanned through SAST/DAST before commit time is a non-negotiable baseline. They also point out you can use self-hosted or locally deployed models to keep your proprietary IP inside your network which cuts the data leakage risk significantly, and there are tools specifically built for detecting secret leaks injected by LLMs during generation. The bottom line they make at the end stuck with me: AI coding is a productivity multiplier but it comes with debt that only gets paid off through governance not just bigger license budgets — and I'd love to hear how everyone else has structured their policy around this because my team's been debating exactly where our own red lines sit.
Source: https://www.darkreading.com/application-security/ai-coding-security-risks-productivity-gains