Agentic AI is changing everything: Is your NHI strategy ready?

Agentic AI is no longer a future concept; it’s here, and it’s already changing how we get things done. Unlike traditional AI models that follow predefined scripts, AI agents can independently make decisions and take actions with little or no human input. However, this shift also comes with new security challenges, especially around identity.

How agentic AI interacts with non-human identities

Agentic AI is a system that can independently make decisions, take action, and adapt over time, all within an objective defined by a human or business. Compared to traditional automation or generative AI that waits for prompts, agentic AI actively pursues the defined tasks, evaluates outcomes, and adjusts its behavior without constant supervision.

The security risks agentic AI introduces

As AI agents take on more autonomy, they introduce new security risks that traditional systems were never designed to handle. Let’s explore some of them below.

Identity sprawl

Identity sprawl refers to the uncontrolled growth of digital identities, especially NHIs, across systems and environments. With agentic AI, this happens fast. Every time an AI agent performs a task (e.g., triggering APIs or launching a new service), it may generate or request a new credential.

Within such settings, one poorly scoped agent can create dozens of NHIs in a day, many with broad or unnecessary permissions. And, without proper lifecycle management, these identities pile up and remain active long after they're needed, often resulting in a bloated identity surface that's easy to exploit, hard to audit, and nearly impossible to clean up manually.

Privilege escalation

Autonomous agents often act based on perceived needs. If they’re given the ability to request broader permissions or, worse, assign them, they could unintentionally sidestep the principle of least privilege.

Think of an AI agent that’s been trained to“unblock deployment issues.” It fails to deploy due to a permissions error, so it looks for a workaround. If not constrained, it might escalate privileges and assign itself the missing role, which means it now has access outside its intended boundaries.

Lack of oversight

AI agents can operate in the background 24/7 and across different environments, making it very challenging to track their visibility.

Without continuous monitoring and real-time observability, you may have agents running critical processes that no one knows exist. All it would take is one misconfigured script or unexpected LLM behavior to leak your credentials or interact with unverified third-party services.

Is your NHI strategy ready for agentic AI?

All the risks highlighted in the previous section are challenges associated with autonomy at scale, and they will only multiply as agentic AI becomes more integrated into core systems. The question is whether your current NHI strategy is ready to face this new reality. Here’s how to assess where you stand.

Inventory management

Do you have a real-time view of all NHIs across your environment, including those created by AI agents? That means knowing what identities exist, who or what created them, when, for what purpose, and what systems they touch. Most organizations can’t answer that because traditional identity and access management(IAM) tools were built to track users and long-lived service accounts, not thousands of ephemeral or autonomous identities.

If your inventory lacks context and correlation, it’s just a spreadsheet, not a defense layer. You need discovery tools that show metadata like creation time, access history, and connections between NHIs and their parent processes or agents.

Access controls

Are your access policies adaptable enough to keep up with AI technologies and agent-driven behavior? Agentic AI doesn’t follow traditional workflows; it improvises and scales dynamically. That means static IAM roles, hardcoded secrets, and over-permissioned service accounts are liabilities, especially when AI agents can access sensitive data.

Many organizations assume that existing service accounts and predefined roles are enough, but that model breaks down fast when agents start making decisions and chaining actions across systems. You need to assess whether your access model reflects actual usage patterns and whether it can catch over-permissioned identities before they become a liability.

Lifecycle management

How do you create, rotate, and retire NHIs? Are there processes for decommissioning identities when agents are shut down or replaced?

With agentic AI, lifecycle events don’t just happen at deployment time; they can happen every hour. If your deprovisioning logic isn’t automated and tightly coupled to activity monitoring, those credentials live long after the agent’s job is done.

If your current NHI practices fall short in any of these areas, it’s time to rethink your approach.

Building a NHI strategy that can handle autonomy

Your NHI strategy needs a serious upgrade to keep up with agentic AI. Here’s what that means in practice.

Dynamic access management

Move beyond static policies. Implement just-in-time access, short-lived credentials, and context-aware permissions. AI agents should be assigned permissions that dynamically match their tasks, i.e., adding and removing access precisely when needed.

You can also introduce policy-as-code frameworks to define the conditions under which certain actions are permitted. For example,“only allow AI agents to create NHIs during business hours and only within pre-approved namespaces.” Guardrails like this are essential.

Automated discovery and monitoring

Use tools that continuously scan for new NHIs and map them to the systems or agents that created them. Real-time visibility into what identities exist and what they’re doing is critical.Additionally, look for platforms that integrate with CI/CD pipelines, cloud APIs, and LLM orchestration layers rather than only traditional IAM directories.

Strong governance frameworks

Define clear ownership, i.e., who’s responsible for provisioning, reviewing, and decommissioning non-human identities. Your policies should reflect not just technical requirements but also your organization’s stance on AI ethics, compliance, and risk.

Furthermore, you should establish dedicated groups focused on NHI governance if you're a security leader. These teams should own the trust boundaries around machine identities, set access standards, and ensure continued oversight as usage scales. Without that accountability layer, things will slip through the cracks fast.

What’s coming next

As agentic AI continues to scale, your NHI footprint will grow exponentially faster and in ways that are hard to predict. To stay ahead, your security teams need to start investing in scalable identity infrastructure and more automated threat response built for non-human activity.

Also, regulatory pressure won’t be far behind. You should expect new requirements focused on transparency and control over autonomous decision-making. Soon, logging actions won't be enough; you’ll need to trace them back to the AI agent that made the call and explain why it happened.

You’re already behind if your NHI strategy still treats machine identity as an afterthought. Agentic AI is accelerating identity sprawl, and the attack surface is only getting wider. Use tools like Doppler to centralize and secure secrets for non-human identities. Want to see it in action? Explore how Doppler can simplify NHI security in environments powered by autonomous systems.