How to keep data secure when your dedicated team uses AI 

AI coding tools are becoming increasingly common in development workflows. For dedicated teams, secure use comes down to three things: what data enters AI tools, what systems they can access, and how AI-assisted code is reviewed before release. 

What data security risks come with AI-enabled development teams?

Sensitive data can leave managed environments

Verizon’s 2026 Data Breach Investigations Report found that regular AI use on corporate devices rose from 15% to 45% in a year. Of those users, 67% accessed AI services through non-corporate accounts, while source code was the most common type of information submitted to external GenAI tools*. 

For development teams, that can expose source code, credentials, API keys, technical documentation, and client information outside company-managed environments, where visibility and control are reduced. 

*Generative AI (GenAI) tools are artificial intelligence systems designed to create new content – such as text, images, audio, video, and code – based on prompts and patterns learned from massive datasets.  

AI tools can receive more access than they need

Modern coding assistants can edit files, execute commands, install packages, connect to external services, and interact directly with repositories and development environments. The risk increases when those tools inherit the same broad permissions as the developer using them. OWASP’s secure coding guidance for AI recommends limiting AI agents to the minimum files, credentials, commands, and network access required for each task. 

That means repository permissions, API scopes, credentials, and environment access all need to be deliberately controlled rather than inherited by default. 

AI-assisted code can introduce vulnerabilities

AI can accelerate development, but generated code still carries security risk. Veracode’s 2025 GenAI Code Security research tested more than 100 large language models across Java, Python, C#, and JavaScript and found that 45% of generated code samples failed security tests. 

AI-assisted code should therefore move through peer review, automated testing, QA, and security checks before it reaches production. 

How can development teams use AI more securely?

Use approved tools and managed accounts

Company and client work should stay inside approved AI platforms using company-managed accounts. Usage policies should also define which information can never be entered into AI tools, including credentials, API keys, restricted client information, and other sensitive data. 

Apply least-privilege access

AI integrations should only reach the repositories, files, APIs, and environments needed for the task. Production systems, sensitive credentials, and unrelated repositories should remain outside that scope unless there is a clear operational need and appropriate controls are in place. 

Keep existing review standards

AI-assisted code should meet the same standards as any other code entering production – peer review, automated testing, QA, vulnerability scanning, and security checks before release. Developers remain accountable for the work they approve and ship. 

Give developers practical guidance

Policies work best when employees know how to apply them day to day. Training should cover common situations such as handling sensitive information, assessing a new AI tool, responding to requests for broader access, and reporting a potential incident, alongside a clear approval and escalation path for anything outside existing guidance. 

How does Away Digital Teams guide employees on safe AI use?

Every employee works under a written AI Usage Policy that defines approved tools, data-handling requirements, and prohibited use. That policy is enforced through account-level controls rather than left to individual judgment – so AI activity involving company or client work stays inside an environment we can see into and support directly. 

The policy is backed by: 

• Ongoing training on safe and appropriate AI use

• Clear rules for sensitive information, including credentials, restricted data, and client material

• A named point of contact for questions and new use cases

• An approval process before new AI tools are introduced into client work

• Immediate incident reporting if information is shared incorrectly

For clients, that means AI use sits inside the same managed environment and accountability standards applied to the rest of their dedicated team – not a separate set of rules layered on top. 

What should you expect from an AI-enabled dedicated team?

As AI tools evolve, policies and access controls need to evolve with them. You should be able to see which AI tools your team uses, know how sensitive data is handled, understand what those tools can access, and expect every piece of AI-assisted work to meet your normal security standards. 

If you’re still deciding how a dedicated offshore team should be structured in the first place, building an AI-ready offshore workforce in Vietnam is a good place to start.