
This is the second part of a series on SentinelOne’s Prompt Security solution. In the first article, I wrote about Shadow AI among employees; today, we’re shifting our focus to development teams, where the stakes are different: not personal data, but code, confidential information, and the company’s intellectual property.
AI in IDEs is the norm today, not just a novelty
By 2026, the debate over „whether to use AI for coding” had practically died down; today, a typical commercial project utilizes several layers of automation: from code-line suggestions, through context-aware chat across the entire repository, to semi-automatic refactoring. GitHub Copilot and Cursor are now everyday work tools, not just add-ons for enthusiasts.
The problem is that the better these tools „understand” the project, the more context they incorporate into the query—related files, change history, and sometimes the entire repository structure. It is precisely this context that can be the source of a leak.
Where does control actually disappear?
⬩ Secret API keys pasted into public models during debugging without realizing that they could end up outside the organization and be lost forever
⬩ Personal information in sample test data, logs, or code comments
⬩ Intellectual Property – the company’s business logic and algorithms sent to external model providers
⬩ Lack of visibility – the security team doesn't know which teams are actually working with which AI tools.
Cybersecurity experts in Poland emphasize that these are not isolated incidents, but rather part of everyday life for development teams that simply want to deliver code faster.
≫ How does Prompt Security protect developers?
In addition to protecting sensitive data, the platform’s second pillar focuses on three other areas:
- Protection of Classified Information – automatic real-time code review that prevents the leakage of confidential information, personal data, and IP before it reaches the AI model
- Full visibility – insight into how AI is used in the development process and the detection of privacy breaches in real time, rather than after the fact
- Extensive integration – Integration with thousands of AI tools and coding assistants, including GitHub Copilot and Cursor, supporting nearly 30 programming languages—without requiring the team to change its technology stack.
Safety Without Compromising the Band's Performance
The key point is that protection runs in the background, at the level of interaction with the model—developers continue to use their favorite tools at their own pace, and the company can be confident that trade secrets and sensitive data do not leave the organization uncontrolled.
What's next?
In the next installment of the series: how to protect your own in-house AI applications against prompt injection and data leaks.
Want to see how your development teams are using AI today?
Contact us—we'll help you assess your risks and determine the scope of your Prompt Security implementation. Schedule a demo:


