Key Takeaways

• Agentic AI demands a new security mindset. Unlike traditional AI tools, agentic AI acts autonomously across systems, making identity governance the critical line of defense.
• Unmanaged agent identities are the biggest gap. Most enterprises lack a consistent way to provision, track, and retire AI agent credentials, leaving agents operating with excessive permissions and no accountability trail.
• Prompt injection and agent-to-agent attacks are real threats. Malicious instructions can be embedded in the content agents process, and a single compromised agent can propagate bad actions across an entire multi-agent pipeline.
• You can’t govern what you can’t see. Shadow AI is already happening in most organizations, and it can’t be secured until it’s discovered.
• Existing IAM tools weren’t built for this. Human-centric identity services lack the ability to handle ephemeral agents, MCP-layer authorization, and end-to-end agentic workflow traceability at scale.
• Least-privilege access and full auditability are non-negotiable. Every agent should have scoped, task-specific access and leave a complete log of intent, identity, and outcome, just like the standard applied to any human user.

Agentic AI is no longer a future-state conversation. Across enterprises, AI agents are being deployed today to execute multi-step workflows, access APIs, query databases, and make decisions with minimal human intervention. The productivity gains are real. But so are the risks.

As organizations race to put agents to work, security and identity teams are confronting a hard truth: the identity frameworks built for human users were never designed for autonomous AI. Understanding agentic AI risks is now a foundational requirement for any enterprise that takes security seriously.

What is agentic AI, and why does it change the security equation?

Traditional AI models respond to prompts. Agentic AI autonomously act on them. An AI agent can be given a goal and then autonomously execute a chain of actions to accomplish it, calling tools, accessing systems, and handing off work to other agents along the way.

That autonomy is exactly what makes agentic AI valuable, and, in the same vein, it’s also what makes AI dangerous. When an agent can take action on behalf of a human across multiple systems, every step in that chain becomes a potential risk surface. And unlike a human employee, an agent doesn’t pause to ask whether something seems “off” or against organizational norms.

The core agentic AI risks enterprises face in 2026

1. Unmanaged agent identities

Every AI agent that interacts with enterprise systems needs an identity. Meaning, it needs a verifiable credential that says who or what it is, what it’s allowed to access, and on whose behalf it’s acting. Most enterprises today don’t have a consistent answer for how to provision, manage, or retire those identities.

The result is a growing population of agents operating with over-permissioned credentials, service accounts that were never designed for AI workloads, or worse, with no formal identity at all. Without identity governance applied to agents as rigorously as it’s applied to humans, you have no way to know what your agents are doing, or to stop them when something goes wrong.

2. Privilege escalation and over-permissioning

Agentic AI workflows often span multiple systems and APIs. To keep things simple, developers tend to grant agents broad permissions upfront just in case they need it later. But broad permissions are a liability. An agent with access to more resources than it needs for any given task is an agent that can cause disproportionate damage — through a bug, a misconfiguration, or a compromised credential.

The principle of least-privilege, which is granting only the access required for a specific task, is well understood in human IAM. Applying it consistently to agents, especially ephemeral ones that spin up and down dynamically, is a challenge that most identity stacks weren’t built to handle.

3. Prompt injection and agent manipulation

Prompt injection is one of the most insidious risks in agentic AI. An attacker can embed malicious instructions into content that an agent is likely to process, like a document, a web page, or an email, and the agent, following its instructions, executes those commands as if they came from a legitimate user.

Because agents often act without human review of each individual step, a successful prompt injection attack can trigger a chain of unauthorized actions before anyone notices. This risk is amplified in multi-agent architectures, where a compromised agent can pass manipulated instructions downstream to other agents in the workflow.

4. Lack of auditability and observability

When a human takes an action in an enterprise system, there’s typically a log. When an AI agent (or chain of agents) takes an action the trail becomes murkier. Which agent did what? Acting on whose behalf? With what authorization? At what point in the workflow did a decision get made?

Without end-to-end traceability across agentic workflows, security teams can’t answer these questions after the fact. That’s a compliance problem, an incident response problem, and increasingly, a regulatory problem as frameworks governing AI accountability continue to evolve.

5. Insecure agent-to-agent communication

Multi-agent architecture where agents delegate tasks to sub-agents, introduces a new category of risk: lateral movement. If one agent in a pipeline is compromised or operating outside its intended scope, it can pass malicious instructions or escalated permissions to downstream agents. Standard network security controls don’t account for this kind of intra-agent communication, and most identity systems don’t either.

Securing agent-to-agent interactions requires the same rigor applied to service-to-service communication: mutual authentication, scoped authorization, and logging at every handoff.

6. Shadow AI and ungoverned agent proliferation

Perhaps the most underappreciated agentic AI risk is the one you don’t know about. Business units are deploying AI agents without IT or security involvement. Developers are spinning up agents in test environments that quietly find their way to production. Third-party SaaS platforms are embedding agentic capabilities that inherit enterprise credentials.

This shadow AI problem mirrors the shadow IT challenges of the last decade, but the blast radius is larger because agents aren’t just storing information anymore, they can take action.

Why existing identity tools fall short

The core problem isn’t that security teams don’t understand these risks. It’s that the tools they have weren’t designed for them.

Traditional IAM platforms were built around human users: login events, session management, role assignments that change on a quarterly review cycle. AI agents operate differently. They can be spun up for a single task and then deprovisioned. They can act on behalf of multiple principals at once, and they communicate through APIs and MCP servers, not browser-based login flows.

Bolting agent security onto a human-centric identity stack is like trying to run a modern containerized application on infrastructure designed for mainframes. It can be made to work, but not reliably, and not at scale.

How Strata helps you manage agentic AI risks

Strata’s Maverics Platform was built to orchestrate identity across complex, heterogeneous environments, which now includes the identities of AI agents. With Strata, organizations can govern AI agents using the same identity rigor applied to human users, without ripping out existing infrastructure.

Task-scoped agent identity. Strata issues task-specific, short-lived tokens via OBO token exchange to prevent over-permissioned agents. Every agent gets a verifiable identity, tied to the human or system it represents.

Fine-grained authorization. Enforce least-privilege policies at the MCP server and API layer, ensuring agents only access what they need for the specific task at hand, and nothing more.

End-to-end traceability. Strata logs intent, context, identity, resource, and outcome at every step of an agentic workflow. When something goes wrong, you have a complete audit trail to work from.

Human-in-the-loop controls. For high-risk actions, Strata provides secure mechanisms to require human approval before an agent proceeds, keeping oversight where it matters.

The agent identity governance gap is already here. A recent CSA survey commissioned by Strata found that most organizations are knowingly deploying AI agents without the governance structures needed to manage them securely.

The good news: you don’t have to choose between moving fast on AI and maintaining control. With Strata, you can do both.

Ready to secure your AI agents?

See how Strata’s Identity Orchestration for AI Agents works in practice. Try the sandbox or get a demo to see how Maverics can bring governance to every agent in your environment.

Related resources:

• Securing Autonomous AI Agents: CSA Survey Report 2026
• Identity Orchestration for AI Agents
• Manage Identity for AI Agents – Use Case