AI agents are already here, managing multi-step workflows, querying data, and automating complex tasks across systems. They act fast, make decisions and work autonomously within the bounds we set.

What are the bounds though?

How are the agents interacting with the troves of enterprise data, customer records, employee details. Human users are granted access to data, while human intelligence, security policies and laws determine what we can do with that information (and we undergo training to make sure we understand).  

So while agents operate within boundaries we define, those boundaries rarely include intent. Permissions define what an agent can access, but not always what it should do with that access. When context shifts or workflows expand, agent actions produce unintended consequences.

How agent behavior creates new security challenges

Consider a typical workflow: an AI agent is tasked with pulling the latest data, updating records, or generating reports. Access is scoped, permissions are assigned, and the agent executes as expected.

But the environment is rarely static. A new dataset becomes available, a connected system exposes additional functionality, or downstream processes change state. The agent continues to operate, making the next logical choice within its permissions. For instance, a sales agent updating a customer record might pull every active opportunity related to that account to provide context. While all access is fully authorized, this can unintentionally touch far more customer data than necessary, increasing exposure without any malicious intent.

AI agents do not exceed their permissions - but when controls are too broad or context is missing, compliant actions can still produce unintended consequences. Agents may pull data that is accessible but not strictly necessary, invoke tools or APIs to complete subtasks efficiently, chain tasks across multiple systems, or incorporate new inputs in ways not anticipated by human designers. Each individual action is fully authorized, yet together they can create gaps that traditional access control cannot address.

Why traditional access control isn’t enough

Most enterprises rely on role-based access control (RBAC), which assumes predictable, human-centric workflows. A role is assigned, a set of permissions granted, and the system assumes safety will follow.

AI agents operate differently. They execute multi-step, context-dependent workflows that rely on real-time inputs, changing data, and dynamic system states. Each action may interact with multiple systems and datasets in ways human designers did not anticipate. In fact, given all the possible things an Agent could potentially do, using roles would involve creating a large number of pre-configured/”hard-coded” roles (potentially leading to Role explosion before even starting), and then switching between roles, somehow, at runtime depending on the Agent task at hand. This is rather impossible to implement and would lead to a rather unmanageable system.

Static roles can’t answer questions like:

• Which data is appropriate for this specific task?

• Does the agent’s action align with organizational intent?

• Is the current context safe for this operation?
• Which two (or more) agents can collaborate for a given task?

Without dynamic, context-aware authorization and adaptive guardrails, even fully compliant agents can produce outcomes that are risky.

Guardrails must be adaptive

The solution lies in designing deterministic rules and adaptive guardrails that reason about every action in real time. This is where granular access control comes into play - instead of simply checking whether an agent can access a dataset or API, guardrails evaluate whether an action should occur, factoring in task relevance, data sensitivity, system state, and workflow context. By making permissions temporary and task-specific, the system limits persistent exposure across systems. Adaptive authorization and integrated trust scoring can further strengthen decisions by assessing the reliability of data sources and agent actions before execution.

Embedding this reasoning into the system - and logging every decision with full provenance - gives organizations traceability and accountability for each agent action. Multi-step workflows can run autonomously while remaining fully aligned with enterprise policies, regulatory requirements, and business intent.

Dynamic coordination across agents ensures that interactions between models, APIs, and systems follow the same rules, preventing unintended privilege escalation or exposure. The result is a consistent, policy-enforced environment, even as agents scale and act at machine speed.

Realizing safe, high-speed AI

With the right structures in place, enterprises can let AI agents act autonomously without sacrificing control. Context-aware guardrails, temporary task-specific permissions, and trust-based decisioning ensure every action is purposeful, auditable, and aligned with organizational intent.

When these elements are in place, AI agents can become more than just autonomous tools - they can become trusted partners in driving enterprise decisions.

Discover how to make AI a reliable, safe extension of your operations.