Secrets management in the age of AI: Why NHI hygiene now defines system security

TL;DR

In July 2025, a founder using Replit’s vibe coding feature lost months of live data when the AI agent deleted a production database containing over 1,200 executive records. The model ignored explicit freeze instructions, fabricated test results, and tried to cover up the error. It had full write access but no scoped permissions and no separation between development and production data.

That single incident reflects a broader truth of AI-native workflows accelerating identity sprawl, and making secrets hygiene a mission‑critical layer of system security. In this article, we’ll look at how AI is changing secrets management, why non-human identity hygiene is now a core security concern, and what it takes to stay in control when machines are moving faster than people can follow.

What is non-human identity (NHI) hygiene?

Just like personal hygiene keeps your body clean to prevent infections, NHI hygiene keeps your machine identities clean to prevent security breaches. It’s the practice of managing machine identities, such as agents, services, containers, and models, so they don’t hold onto secrets they shouldn’t, access things they’re not supposed to, or leave behind credentials that others can misuse.

NHI hygiene means:

• Scoping: Limit which system gets which secret, when, and under what conditions.
• Lifecycle control: Rotate, expire, or destroy secrets per job, session, or environment.
• Auditability: Every access should be traceable back to a known agent, pipeline, or job.

In systems without proper NHI hygiene, authorized machines become indistinguishable from rogue ones, increasing exposure to cyber threats and misuse of credentials like API keys, digital certificates, or other sensitive access tokens. In secrets management, NHI hygiene means tightly scoping which identity gets access to which secret, for how long, under what conditions, and based on the system's specific access patterns.

How AI workloads changed access patterns

On the surface, secrets management and access patterns might seem like they haven’t changed much. However, AI and automation have completely changed the pace and pattern of how and when they need to happen. Here are some examples.

Agent-led execution breaks traditional access logic

AI applications often act on behalf of users but with the autonomy of full services. A simple prompt to an LLM can trigger actions like calling APIs, retrieving documents, or updating databases, all without the user issuing a direct command for each step.

That makes intent difficult to pin down. When an agent fetches a file from secure storage, is it fulfilling a clear user request, or is the model making a decision on its own? Without that clarity, accountability starts to blur. You can’t always tell whether a user had the right to perform an action or if the system crossed a line on its own. Traditional access logic assumes a stable boundary between users and systems, with clear intent, scoped roles, and predictable behavior. However, agent-led execution breaks that model.

AI workloads are ephemeral and scattered

Unlike traditional applications that run as long-lived services on stable infrastructure, AI systems are dynamic by nature. A training job might run across many machines in different locations, while an inference pipeline may need to scale up or down depending on demand. These tasks are often scheduled automatically, based on which machines have free resources. In environments like this, long-lived credentials and static configs quickly fall apart.

This same unpredictability also makes access control harder. With autonomous agents or LLMs making decisions on the fly, it’s hard to know in advance what data a model will access, how much compute it will need, or how long it’ll run. IAM systems tied to specific machines or user accounts don’t hold up under these conditions.

Secrets leak into places they shouldn’t

AI workloads deal with a lot of sensitive information, such as API keys, tokens, user data, and even proprietary knowledge. The problem is that these systems are also harder to contain. Secrets can leak into logs, prompts, or intermediate artifacts. Language models may memorize credentials if they appear in training or fine-tuning data. Since AI applications are built on multi-layered stacks (models, pipelines, vector stores, and orchestration layers), secrets are often passed around in ways that are difficult to track.

Understanding the risks is the first step. Solving them requires a different approach. Let's see how that works below.

How modern teams should protect machine identities

Protecting machine identities is now a critical risk mitigation strategy. Your team needs an established plan with strong metrics, regular testing, and clear ownership to avoid strategic management errors and reduce exposure across the system. You can stay ahead by following a few core principles.

Core principles for AI-native secrets management

Principle 1: Treat secrets as runtime contracts

Treat secrets like short-term agreements. Create them only when they're actually needed, limit them to a single task, and tie them to the exact system or job that will use them. Once the task is done, the secret needs to expire automatically. Don’t share secrets between agents, and don’t reuse them across different parts of your system. That’s how leaks happen.

If a model or tool gets access to something, it must be because the system knows exactly what it’s doing and for how long. Anything looser than that opens the door to mistakes or abuse. Use tools like Dopplerto generate task-specific secrets that automatically expire after use. That way, even if something goes wrong, the window for damage stays small.

Principle 2: Build auditable and policy-aware execution paths

Basic logs only tell you what happened. That’s not enough. If you want to keep your systems trustworthy, you also need to know why something was allowed to happen in the first place. That means capturing the full picture of who or what triggered the action, what policy allowed it, and what the system's state was at the time.

In AI workflows, agents can take actions you didn’t explicitly program, based on patterns they've learned or goals they’ve inferred. To stay in control, you need visibility into how decisions were made.

Principle 3: Enforce least privilege across everything

The principle of least privilege has always been a solid security practice, but in AI-driven systems, it becomes absolutely foundational. Every process, whether it is a container, agent, or model, should only have access to what it needs for the task at hand. Nothing more. Access should be based on who started the job, what it’s meant to do, what data it needs, and how long it’ll run. Broad or long-lived permissions create too much room for mistakes and unpredictable behavior. When each part of the system is tightly scoped to just its role, it’s much easier to keep things secure and in check.

How to make secrets work for AI systems

The first step in making secrets work for AI is following the principles from the previous section. But to actually apply those ideas at scale, you need the right infrastructure. Secrets management platforms like Doppler give you the foundation to do that. They manage how secrets are created, scoped, delivered, and revoked across dynamic systems where jobs, agents, and services are constantly spinning up and shutting down.

For example, say you're running a model training job in Kubernetes that needs to pull data from a private object store like S3 or GCS. Instead of baking access tokens into the container or managing them manually, you can use Doppler’s Kubernetes Operator to sync secrets directly from Doppler into your cluster with the code sample shown below.

This setup tells the Doppler Operator to sync secrets from the training config of the ai-platform project into a Kubernetes secret called object-store-credentials in the ai-jobs namespace. The operator checks Doppler every 60 seconds and updates the secret automatically if anything changes.

That line loads all key-value pairs from the synced secret as environment variables, so the training script can use them without hardcoding or manually passing tokens.

With this setup, you move from manually wiring secrets into AI jobs or agents to a system where secrets are delivered just in time, scoped to the specific workflow, and automatically kept in sync as jobs spin up and down. And that’s just the start. Doppler also gives you audit logs, access controls, and integrations that fit directly into your CI/CD and runtime environments.

Try a Doppler demo to see how secrets management helps enforce control and integrity in AI-native architectures.