They should do it as soon as developers are using AI coding agents that can read workspace files. At that point, file-based secrets are no longer just a commit risk. They are a context exposure risk, and the right response is to move delivery into the runtime path.
Why This Matters for Security Teams
.env files were designed for developer convenience, not for environments where code is inspected, mirrored, and executed by autonomous tooling. Once AI coding agents can read workspace files, a secrets file becomes part of the agent’s context window and execution surface, which changes the risk profile from accidental commit exposure to active runtime exposure. That is why current guidance from the NIST Cybersecurity Framework 2.0 aligns better with runtime control than file-based trust.
NHI Management Group research shows how fragile secret handling already is: 96% of organisations store secrets outside of secrets managers in vulnerable locations including code, config files, and CI/CD tools. That pattern becomes more dangerous when an AI agent can scan, summarise, copy, or chain secrets into downstream tools without ever “committing” them. The operational question is not whether a secret file is checked into git, but whether it is exposed to any tool that can read the workspace and act on it.
Security teams often underestimate this transition because they still think in source-control terms, while the real exposure now occurs before the code ever leaves the desktop. In practice, many teams encounter secret leakage only after an agent has already parsed the file and propagated it into logs, prompts, or tool calls.
How It Works in Practice
Replacing .env with runtime injection means the application receives secrets only when it starts or when a task begins, rather than storing them in a persistent file beside source code. The most mature pattern is to keep the secret source in a manager or broker, then inject short-lived values into the process environment, container, sidecar, or workload identity flow at execution time. For agentic workloads, this is especially important because the agent may request tools dynamically and its access pattern is not stable enough for static file-based assumptions.
The control objective is to reduce both dwell time and exposure scope. Runtime injection works best when combined with workload identity, per-task credential issuance, and automatic revocation. In other words, the application proves what it is, then receives only the secret needed for that job, only for the time needed. That is consistent with the direction of NHI Management Group research on large-scale cloud compromise, where credential exposure often turns into broad access because static secrets are reusable across environments.
- Use runtime injection for secrets that are needed at startup, for API calls, or for agent tool access.
- Prefer short-lived tokens over static passwords, keys, or certificates.
- Bind injection to workload identity and policy checks, not to local file presence.
- Prevent secrets from being written back to disk, logs, prompts, or debug output.
- Rotate or revoke immediately after task completion when feasible.
Teams should treat .env as a development convenience only, and only in tightly controlled local workflows. These controls tend to break down in shared development environments where agents, build tools, and browser-based IDEs can all read the same workspace without a clean trust boundary.
Common Variations and Edge Cases
Tighter runtime injection often increases delivery complexity, requiring organisations to balance stronger containment against developer friction and platform maturity. The tradeoff is real: moving secrets out of files reduces exposure, but it also introduces dependency on orchestration, identity plumbing, and operational discipline.
There is no universal standard for every environment yet. For local prototyping, a .env file may still be acceptable if it contains only non-sensitive placeholders or throwaway values. For production, shared staging, CI/CD, and agent-enabled workspaces, best practice is evolving toward runtime injection and ephemeral secrets. The key distinction is whether the file can be read by anything that should not retain or reuse the credential.
Edge cases matter. Offline development, legacy frameworks that expect file-based configuration, and systems that cannot easily consume injected variables may require transitional controls such as encrypted local secret stores or wrapper scripts. Even then, the safer approach is to keep secrets out of the repository and out of the workspace tree wherever possible. If an AI agent can inspect the directory, assume the file is already part of the trust boundary.
For organisations formalising this shift, the practical trigger is not a calendar date but a capability change: the moment autonomous tooling can access the workspace, file-based secrets stop being a low-risk convenience and become an avoidable exposure path.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
OWASP Non-Human Identity Top 10 and OWASP Agentic AI Top 10 address the attack and risk surface, while NIST AI RMF set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Non-Human Identity Top 10 | NHI-03 | Covers secret lifecycle weaknesses that make .env files risky. |
| OWASP Agentic AI Top 10 | A2 | Agent workspace access creates context exposure beyond classic secret leakage. |
| NIST AI RMF | Addresses governance for autonomous systems that change exposure patterns. |
Limit agent access to runtime-only secrets and prevent workspace files from containing reusable credentials.
Related resources from NHI Mgmt Group
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Reviewed and updated by the NHIMG editorial team on July 8, 2026.
NHI Mgmt Group — the #1 independent authority on Non-Human Identity, IAM, and Agentic AI security. nhimg.org