These assistants are built on systems (AI Memory, Models) that need plaintext access to context in order to generate helpful output
AI coding assistants like GitHub Copilot, Cursor, and similar tools have quickly become indispensable for developers. With the ability to autocomplete code, suggest logic, and boost productivity, these assistants are now embedded in everyday workflows across engineering teams worldwide. But this convenience hides a growing, under-acknowledged security crisis: code exposure.Every interaction with these assistants involves sending context often entire files to remote servers. These files may include API keys, authentication credentials, proprietary algorithms, and critical business logic. Once transmitted, the data is decrypted and processed across various layers of infrastructure, many of which exist outside the enterprise’s direct control. Despite encryption-in-transit and vendor assurances around privacy, the truth is plain: this data becomes accessible to systems, vendors, and potentially, unintended actors.
This isn’t a bug, it’s the existing norm. These assistants are built on systems (AI Memory, Models) that need plaintext access to context in order to generate helpful output. This requirement opens a broad and poorly protected surface area. The promise that data won’t be stored or used for training is governed by internal policies that cannot be externally verified or technically enforced.Real-world cases have already demonstrated how proprietary information and credentials have unintentionally surfaced in model outputs. The problem is systemic. Whether through memorization by models, oversights in training data, or insecure integration with IDEs, code leakage is no longer theoretical.
This challenge has forced enterprises into an impossible trade-off: productivity versus protection. Some have chosen to restrict or ban AI coding tools altogether, especially in high-stakes industries like banking, defence, and healthcare. Others continue to use them, hoping policies and NDAs will be enough to contain risk. Neither path is sustainable.A new approach is needed, one that doesn’t rely on blind trust, policy disclaimers, or privacy settings buried in documentation. The future of secure AI-assisted development lies in a radically different paradigm: encryption-in-use.
Unlike traditional security methods that protect data at rest and in transit, encryption-in-use ensures data remains encrypted even while it’s being processed. With this approach, AI tools never see the raw code. The computations are performed directly on encrypted data, and only the developer can decrypt the results. There is no point in the pipeline — from transmission to inference where the system has access to the original input.This model changes the security equation entirely. Instead of relying on vendors to keep their word, encryption-in-use makes it mathematically impossible for them to access your data. It eliminates the need for legal assurances or technical workarounds. And it does so without sacrificing the developer experience or AI performance.
Historically, encryption-in-use specifically, Fully Homomorphic Encryption (FHE) — was considered too slow or computationally intensive for real-time applications. But recent innovations have changed that. When optimized for structured environments like software development workflows, FHE can now support secure, low-latency operations with minimal impact on speed and resource consumption. This opens the door for widespread adoption in live enterprise environments.This also simplifies compliance with regulations like GDPR, HIPAA, and industry-specific data protection laws, as exposure risk is drastically reduced.
Importantly, encryption-in-use also neutralizes insider threats and third-party access vulnerabilities. Since the data is never decrypted during processing, it’s impervious to interception even by those running or managing the AI systems. Whether the model is hosted on a public cloud, in a third-party tool, or within internal infrastructure, the risk profile remains controlled.The shift to secure AI doesn’t mean compromising speed or innovation. On the contrary, encryption-in-use empowers developers to explore AI more freely without needing to sanitize code or restrict access to internal repositories. It aligns technical progress with enterprise-grade security, rather than forcing a choice between the two.
With AI rapidly redefining how software is built, organizations need to act before reactive compliance becomes the only option. Regulatory landscapes are tightening, customer expectations are evolving, and adversaries are growing more sophisticated. Relying on status quo architectures and vendor assurances will soon no longer be acceptable.
AI coding assistants have reached a tipping point. Their value is proven but their vulnerabilities are now just as visible. The companies that move first to embed encryption-in-use will lead the next chapter of secure software innovation. They will be the ones that retain both developer velocity and stakeholder trust.
Because in an age where AI can write your next line of code, it shouldn’t come at the cost of exposing your last one.
By Pankaj Thappa, Co-Founder & CEO of Mirror Security
