The Unique Challenge of AI Agent Authorization
Core Principles of AI Agent Authorization
Approaches to Implementing Authorization for AI Agents
How do you implement authorization for AI Agents?
Today, developers use AI agents for sensitive workflows. Agents offer quick automation—for example, an AI agent can process 1,000 invoices in minutes, a task that would take a human hours. But these tasks involve protected information that’s usually inaccessible to humans unless they pass authorization checks. These same rules need to apply to AI agents.
For agents specifically, authorization defines what an agent can do, what data it can see, and where it needs a human check-in. Without it, even the most helpful AI agent can quickly turn into a liability, as a misplaced permission could expose private data, leak trade secrets, or trigger financial transactions. Even worse, AI agents can, unlike humans, do hundreds of these destructive actions in a minute.
Today, we want to dive into the specific challenges of extending authorization to AI agents, including the frameworks and strategies to tackle them. But first, let’s discuss MCP: a component that specifies security requirements for agentic authorization.
Model Context Protocol (MCP) standardizes how applications provide context to AI Agents. MCP servers provides AI Agents with three main things:
Notably, MCP opts for OAuth 2.1—a more secure specification than the massively popular OAuth 2.0 standard—for authorization. With Proof Key for Code Exchange (PKCE) and Dynamic Client Registration (DCR), OAuth 2.1 allows AI Agents to authorize themselves securely and automatically without requiring a user to be present.
AI agents don’t behave like traditional applications, and that’s exactly what makes them powerful. It’s also what makes them risky. A conventional app usually follows a set of fixed routes and predictable API calls. In contrast, AI agents act dynamically and non-deterministically, using pre-built tools, querying APIs, and pushing changes to data sources to solve problems.
For instance, a team might task an AI agent with saving customer accounts that show churn risk. This might involve the AI agent (i) interfacing with the Salesforce MCP instance to access customer records, (ii) querying the database that has usage data, (iii) querying the company playbook from Google Drive, (iv) scoring accounts for churnability, and then (v) sending an email to the customer success manager assigned to each churn risk. In this scenario, the AI agent needs to:
However, granting access to these multiple entities isn’t trivial. For example, incorrect settings could lead to:
The bigger issue is that most organizations aren’t ready for this shift. By mid-2025, more than 80% of companies used AI agents in some form, yet fewer than half had comprehensive governance in place to manage their access and permissions. Agent-specific authorization cannot be an afterthought, given how destructive agents could be.
If AI agents are going to operate safely inside businesses, their access needs to follow a few timeless security principles. This boils down to four principles in particular:
Developers should only grant agents permissions they truly need for the task at hand—nothing more, nothing less. Broad or “just in case” access is one of the most common root causes of AI-related security incidents. By narrowing permissions, you limit the blast radius if something goes wrong.
Developers should avoid static authorization. Authorization servers should consider the identity of the agent (and its user), the time of day, the device in use, and even the intent behind a request to determine whether an action is allowed. Context-aware policies adapt in real time, making it much harder for misuse to slip through the cracks.
Being context aware is different from the access model (e.g., RBAC, ABAC, etc.). Instead, being context-aware is a matter of how those access models are used (e.g., what roles or attributes to assign to an identity).
Developers should log every action an agent takes, especially those touching sensitive data or systems. These records need to be detailed enough to support investigations, trace errors, and satisfy compliance requirements. Audit trails aren’t just paperwork; they’re the practical backbone of accountability.
Agents don’t operate in static environments, and their authorization rules shouldn’t either. One-size-fits-all policies fall apart in fast-changing, unpredictable scenarios. Dynamic enforcement, such as time-bound permissions or conditional approvals, lets policies adapt as conditions change, ensuring agents stay within safe boundaries without grinding productivity to a halt.
The principles of authorization are constraints on an authorization strategy. However, they do not denote what that strategy specifically should be. Instead, that’s the role of an access framework. There are various access frameworks, and rarely is one model comprehensive on its own. That said, these frameworks serve as a good starting point on how to shape access.
Most modern systems usually rely on RBAC, ABAC, ReBAC, or some combination of the three.
Implementing this framework can be done in various ways. The most robust is to use a policy-as-code system, where developers define policies in declarative languages like Oso’s Polar or OPA’s Rego. This approach makes rules transparent, testable, and enforceable programmatically across agents. It also improves consistency and makes it easier to audit and evolve policies as agent behavior changes.
Even with strong authorization models in place, security comes down to execution. The way you apply these models determines whether your AI agents remain safe and trustworthy. Since AI agents behave in dynamic and often unpredictable ways, organizations need practical guardrails that extend beyond abstract policy. A few patterns are emerging as especially valuable for keeping agents productive while staying firmly within safe boundaries.
Agents should be assigned unique, non-shared identities so that every action can be properly audited and traced. However, these identities should never surpass the constraints levied on the users that are deploying them. Otherwise, users could use agents to bypass authorization. In fact, the opposite is required: humans are frequently over-permissioned, and given the additional hazard that AI agents risk, their access should be independently scrutinized.
Given that agents are often ephemeral and not actively working, developers could configure their roles to be “just-in-time” access, where access programmatically is revoked once the agent completes its role. This prevents their credentials from being used by an attacker.
Human-in-the-loop is where explicit approval from a user or administrator gatekeeps the agent from proceeding. This is ideal for sensitive or high-stakes operations, such as financial transactions or data sharing.
Time-bounded access constrains access to a specific, recurring interval of time. For example, before AI agents, time-bounded access could restrict engineers from pushing code outside of business hours in their designated time zone. It’s distinct from just-in-time access that is conditioned on events; time-bounded is strictly a function of time.
For AI agents, time-bounded access could prevent undesirable actions outside of a known window. For example, an AI agent that sends sales emails should only be allowed to send sales emails during 8am to 5pm on weekdays—otherwise, it poses the risk of bothering a recipient in the middle of the night. Or, operational hours for sensitive tasks in certain industries (e.g. finance) might be the strict period where agents can operate with full staff oversight.
Developers should stream logs of agent actions to a central logging solution. This solution should dispatch alerts in case of detected anomalies. Given the potential destructive nature of AI agents, alerts should immediately be escalated to the agent’s owner and other managerial staff.
Remember that AI agents are just programs that are guided by AI. Accordingly, their access is safeguarded in the same capacity that any other running instance is safeguarded.
There are many systems for actually coding access. One of these is Oso, where authorization-related data is ingested as a series of Facts into Oso Cloud, and policies are written in a bespoke language called Oso Polar. With Polar, access can be represented in any framework—RBAC, ABAC, ReBAC, or AnyBAC. Then, using Oso’s SDKs, accessing external integrations, data services, and microservices is gated by a call to Oso’s authorizeAPI.
Alternatively, for companies looking to deploy only managed agents, they could instead use a tool like Google Agentspace or Credal, where agents are managed by the service with integrations (and permissions, by extension) baked in.
Authorization is quite complicated, and it’s easy to conflate the purpose of OAuth 2.1 from a policy engine like Oso. OAuth 2.1 is concerned with getting a valid access token. The token is attached to an identity; Oso determines if the tool that’s requested is actually allowed access by the identity.
In other words, OAuth decides “who is this and what may they do.” Oso answers, “For this specific actor and this specific resource, this action (or no action) is permitted.
Permissions and access become more complicated when dealing with multi-agent workflows. Multi-agent workflows involve multiple agents working together to accomplish something. For example, an agent might be focused on writing code, but might delegate SVG generation tasks to a design-focused agent instead.
Authorization is complex for multi-agent workflows because two different agents might have different access grants. Additionally, because agents have stored context in memory, this draws the risk of data leaking from one agent to another. Accordingly, multiple agents need to be orchestrated so that permissions either align or the system wipes access to align with whatever agent is interacting with.
In short: multi-agent orchestration unlocks more complex workflows, but requires more attention to avoid permission violation.
AI agents are powerful enablers of productivity, but they also introduce risks if they aren’t governed with the same rigor as human operators. The lesson is clear: authorization must be treated as a first-class design principle. That means applying least privilege, sandboxing agents, logging every sensitive action, and leaning on policy-driven controls that can adapt as agents gain autonomy.
This is where solutions like Oso Cloud can help. Instead of building complex authorization systems from scratch, Oso Cloud provides a managed platform for fine-grained, policy-as-code authorization. With built-in support for RBAC, ReBAC, and ABAC patterns, plus a global infrastructure for speed and reliability, Oso Cloud gives organizations the guardrails they need to deploy AI agents safely—without slowing down innovation.
Getting started with Oso Cloud is free. If you want to learn more or have questions about authorization, you can schedule a 1×1 with an Oso engineer. Or learn about our automated least privilege for agents product today.