Authorization Resource Center

Last year, Anthropic released Model Context Protocol (MCP), a new protocol for AI processes like LLMs or AI Agents to predictably communicate with external resources. Previously, developers would ingest an API’s OpenAPI specification to LLMs. While descriptive, these specifications didn’t provide LLMs with adequate intention behind an API. Instead, MCP emerged, a protocol that organizes pre-packaged prompt templates, tools, and data of an external resource like Salesforce, a document repository, or another agentic system.

Additionally, authorization flows for MCP are distinct from a traditional API. Typically, APIs rely on signed credentials granted to a human (e.g. via email and password). However, AI agents or LLMs aren’t humans, yet still need to attain authorization without a human present. Hardcoding email and password information into an AI agent is a naive and risky approach—instead, we need a context-aware authorization approach for AI agents and MCP.

Today, we’ll dive into the best practices for authorization for MCP, detailing OAuth 2.1, Proof Key for Code Exchange, Dynamic Client Registration, and authorization frameworks. However, before getting into the details, let’s review how MCP actually works.

What is Model Context Protocol (MCP)

Model Context Protocol (MCP) is an open standard developed by Anthropic that standardizes how applications provide context to Large Language Models (LLMs). The common, potentially overused analogy for MCP is that it’s a “USB-C port for AI applications”; however, I prefer describing it as a cakemix box, describing what’s inside, how to use it, and the potential complex things to create with it.

MCP is distinct from API protocols, such as strictly descriptive protocols like OpenAPI. With an OpenAPI specification, AI agents must figure out what to do from scratch. With MCP, AI agents can explore the application’s purpose, common usage patterns, and tools that effectively bundle actions that would otherwise take multiple API calls. When humans interface with applications, they use an API, the API’s documentation, and step-by-step tutorials online. With MCP, AI agents can access all of that in one place.

How does MCP work?

MCP defines a client-server architecture. Each AI application acts as a host that runs multiple MCP clients. When an MCP host wants to connect to an MCP server (i.e. an external resource), it’ll create an MCP client to manage that relationship.

MCP servers expose three main components:

• Tools, specific functionalities akin to API routes that the AI can invoke
• Resources, specific files, or data that AI can access from integrated applications
• Prompts, pre-written instructions to assist in particular situations.

In other words, unlike traditional APIs that only explain what can be done, developers can use MCP to communicate the business context of their application, making it easier for AI systems to understand when and why to use specific tools.

How does MCP handle authorization?

While a few public MCP servers might not require authorization (such as a public resource like a WHOIS system), a large number of MCP servers need to authorize clients—either to rate-limit requests (e.g. a CC0 images repository) or to expose otherwise confidential data (e.g. Salesforce or Snowflake).

There are a few techniques to handle authorization. The simplest is to use API keys, ideally with a key management solution (e.g. Infisical) for safety. However, API keys have inherent limitations: they typically provide broad, service-level access rather than fine-grained, user-specific permissions. They're also only suitable for services that support API key authentication

Instead, most applications require a user-delegated authorization flow like OAuth 2.0. However, OAuth for AI Agents is more complex: agents aren’t humans, and manually storing credentials in an AI Agent’s codebase is dangerous.

To work around this, developers can use Dynamic Client Registration (DCR) to pre-authorize AI Agents identified with attributes. However, before diving into those details, it’s best to start with the basics: OAuth 2.1, MCP’s chosen standard for authorization.

What is OAuth 2.1?

OAuth 2.1 is a proposed IEEE specification that builds on OAuth 2.0 while addressing some security shortcomings. One of the critical components of OAuth 2.1 is that MCP clients and servers can delegate authorization, where a third-party service handles authorization on behalf of the MCP clients and server.

There are a few key distinctions between OAuth 2.1 and OAuth 2.0. In particular, there are three changes: mandating PKCE, Metadata Discovery, and DCR.

Let’s discuss each of these in-depth.

Proof Key for Code Exchange (PKCE)

Originally an optional extension for OAuth 2.0, PKCE adds an additional security layer during access token grants following submitted credentials.

Let’s remind ourselves of how OAuth 2.0 works:

1. The user provides valid credentials.
2. The authorization server grants the client server an authorization code.
3. The client can then trade that code for an access token.
4. With the access token, a server can access a protected resource.

However, this poses a vulnerability: if an attacker intercepts the authorization code grant, they can trade it for an access token!

To address this, PKCE creates an additional set of steps:

1. Before credentials are submitted, the client server generates a random string (a.k.a. a verifier) and a code challenge (derived from the verifier).
2. When the client requests the authorization code grant, the challenge is also submitted.
3. When the authorization code grant is submitted for an access token, the verifier is also included.
4. The access token is only dispatched if the verifier matches the code challenge.

Because the client never transmits the verifier over the wire until the access token request, the authorization server can confidently identify the same client server instead of an attacker.

Metadata Discovery

Because humans are not manually rigging connections—in this case, between MCP clients and MCP servers—there needs to be a deterministic system for an MCP client to determine which authorization server to connect to. The solution is for authorization servers to expose metadata informing the MCP client about themselves.

In particular, this apparatus is necessary for Dynamic Client Registration, where MCP clients automatically register with authorization servers without a human present.

Dynamic Client Registration

Dynamic Client Registration (DCR) is another protocol extension for OAuth 2.0 that is included with OAuth 2.1. With DCR, MCP clients automatically pre-register with new authorization servers without requiring a user to be present. Given that AI Agents often don’t know what resources they’ll need ahead of time, DCR allows MCP to request authorization servers that the user didn’t know when they originally instantiated the AI Agent

Beyond OAuth 2.1, how is authorization practically implemented?

OAuth 2.1 handles the problem of getting a valid access token for an MCP client. But authorization doesn’t stop at the token. Once an identity has been established, systems still need to determine what that identity is actually allowed to do.

This is where access frameworks come in. In modern systems, most approaches fall into three categories:

• Role-Based Access Control (RBAC), where permissions are grouped into roles like admin, editor, or viewer.
• Relationship-Based Access Control (ReBAC), where access depends on graph-like relationships between entities (e.g. user owns dataset, employee reports to manager).
• Attribute-Based Access Control (ABAC), where permissions might depend on any attributes of the user or resource, such as user identity, device, resource type, or request context.

Developers might combine these frameworks (sometimes referred to as “AnyBAC”) and implement policy-as-code engines such as Oso or Open Policy Agent (OPA) to manage enforcement.

For MCP specifically, OAuth 2.1 securely authorizes clients. Afterward, RBAC, ReBAC, or ABAC schemes define what resources an MCP client can touch, under what conditions, and how the system logs and audits actions. In other words, OAuth decides who gets in, while these frameworks decide what they can do once inside.

What is Oso?

Oso is a policy-as-code framework designed to help developers implement fine-grained authorization directly into their applications. Instead of scattering permission checks across code, Oso centralizes them into policies written in a declarative language called Polar. These policies can capture everything from simple role-based access controls (RBAC) to more complex, attribute- or relationship-based models (ABAC, ReBAC).

In practice, Oso acts as a decision engine. When an MCP client presents a token, Oso evaluates whether that token’s identity can perform a given action on a resource. For example, a Polar policy could state that only the owner of a document may edit it, or that access to sensitive data requires both the correct role and a trusted device.

If you are curious about Oso’s work, and how we’re automating least privilege for AI agents, learn more by clicking here.

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Introduction

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.

MCP and Authorization

Model Context Protocol (MCP) standardizes how applications provide context to AI Agents. MCP servers provides AI Agents with three main things:

• Tools: specific functions similar to API routes
• Resources: specific files or data that AI can access from integrated applications
• Prompts: pre-written instructions to assist in particular situations.

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.

The Unique Challenge of AI Agent Authorization

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:

• Access the right Salesforce data and Google Drive documents
• Query a production database for usage data
• Have access to request a microservice that handles programmatic email.

However, granting access to these multiple entities isn’t trivial. For example, incorrect settings could lead to:

• Overprivileged Access, where agents are given broad or blanket access that may feel like the easiest way to “just make it work.” However, if an agent goes off-script—whether from a bug, a malicious exploit, or a user prompt injection—the results can be detrimental. Graham Neray, CEO of Oso, recently framed this issue in a Forbes piece: “You can’t reason with an LLM about whether it should delete a file. You have to design hard rules that prevent it from doing so.”
• Data leakage, as AI agents are natural sharers. They love to gossip. Without guardrails, they can easily pass sensitive information to the wrong user, tool, or system, sometimes without realizing it.
• Unsafe actions. From making purchases to sending messages, agents may act on incomplete or manipulated instructions. Even well-trained models can “hallucinate” authority they don’t have, performing actions no one intended.

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.

Core Principles of AI Agent Authorization

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:

The Principle of Least Privilege

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.

Context Awareness

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).

Auditability

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.

Dynamic Policy Enforcement

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.

Approaches to Implementing Authorization for AI Agents

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.

• Role-Based Access Control (RBAC). Perhaps the most well-known access framework, RBAC groups permissions into predefined roles (such as admin, editor, viewer) and assigns them to agents or users. RBAC is simple and works well for basic, structured tasks. However, it is poorly suited for AI Agents as an AI Agent’s role isn’t predictable until after it has reasoned on what it needs to do and access; alternatively, granting greedy access to a role would violate the Principle of Least Privilege.
• Relationship-Based Access Control (ReBAC). ReBAC, an access framework that’s championed by Google, tailors access around relationships between entities. For example, a relationship might denote a user’s connection to a dataset, a project, or another person. This model is useful for agents that operate across multiple users or data graphs, where access depends on how entities are linked rather than static roles or attributes.
• Attribute-Based Access Control (ABAC). ABAC is the broadest framework,  beyond roles by factoring in attributes: who the user is, what device they’re on, the type of resource being accessed, the time of day, or even the intent of the request. This enables fine-grained, context-aware policies that adjust in real time, making it a strong fit for AI agents that operate in unpredictable environments.

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.

Patterns and Best Practices

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.

Independent, Inherited Access

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.

Just-in-Time Access

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 Controls

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

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.

Audit Alerts

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.

How do you implement authorization for AI Agents?

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.

How does MCP Authorization / OAuth 2.1 differ from Oso

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.

Multi-Agent Orchestration

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.

Building Trustworthy AI Agents with Oso

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.

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Today, the threat of misconfigured permissions is significantly greater. Why? Two words: artificial intelligence or AI.

Enterprise organizations have always cared about permissions in order to protect resources, meet compliance rules, and honor customer contracts. A user might be over-permissioned and gain access to [add example]. But once teams catch these mistakes, they quickly rectify them, and business moves on as usual.

Everything changes when you’re using AI. AI agents are accessing data, sending emails, and making changes. But AI agents aren’t humans—they’re faster and riskier. With humans, a mistake is a mistake. With AI, a single mistake can quickly cascade into a litany of mistakes. This is due to three traits:

1. Multi-System. Agents rarely query a single system. They assemble responses by pulling data from CRMs, file stores, and databases in parallel. This includes read and write access. If an agent makes a bad request for one piece of data, it can contaminate multiple data stores. Even worse, with write access an agent can carryout destructive actions, deleting or overwriting data.
2. Scale. An analyst might run five queries in an afternoon. An agent might issue thousands in seconds. We’ve long accepted some over-permissioning of humans, because humans are limited by time. But with an agent, even a little over-permissioning can snowball into a volume of exposure that security teams cannot reasonably review in time.
3. Blind Execution. Once an agent has a token, it keeps going until expiration. It does not ask whether the user has been off-boarded or whether the device posture has changed. The system “just works”. But that seamlessness conceals a gap. Each request may quietly bypass risk signals that a human would recognize.

Given these risks, I’d describe agents as powerful, but precarious entities. They amplify a user’s capacity, but they also accelerate the consequences of bad assumptions. The solution—which is more of a precaution than a cure—is context-aware permissions. Instead of binding an AI agent’s access to a static role, it double-checks every decision to the live state of the request. For example, a conventionally day-time access financial application might gate access if sudden requests are made at 3am.

As a player in the authorization space, we wanted to write a piece on this growing issue. In this article, we’ll take a look at how context-aware models work and what patterns are gaining adoption. Then, we’ll also go over some of the challenges to consider when these practices are implemented at scale.

Understanding the Risk

While context-aware permissions undoubtedly reduce risk in practice, what exactly is the risk? In other words, without these safeguards, what is the worst that can happen? The answer: a lot. Let’s walk through three examples.

Customer Data Exposure

An AI support bot might be tasked to pull information from a CRM to load it into another system (e.g. Snowflake) or dispatch emails. However, if this AI support bot has a stale token and therefore outdated permissions, it might end up sharing customer information that it wasn’t delegated to access. While perhaps benign in theory, this is dangerous in practice because it might violate customer data custody contracts, posing legal risks.

Information Misconfiguration

If an AI agent routinely pulls information from databases, but has mis-scoped access, then it might pull excessive information into a query that wasn’t supposed to be aggregated. For instance, imagine if an AI agent strictly is supposed to pull information about test accounts from a database, but poor access controls enable it to pull information about any accounts. Suddenly, the agent might leak customer data.

Uncontrolled Bulk Actions

An AI agent might be tasked to clean-up accounts that were strictly cataloged for deletion. However, if the agent has too broad access, the agent might accidentally delete every account because of the model’s non-deterministic nature (or, just as likely, a poorly worded prompt). More generally, if teams fail to control an AI agent, it could wipe out terabytes of information in minutes.

Evaluating Access Against Live Signals

Context-aware permissioning evaluates each request against signals. The authorization server draws these signals from the environment surrounding the request. For example, certain signals might mark a managed laptop with recent patches as a lesser risk profile than a personal smartphone on public Wi-Fi.

Network matters too. Traffic over a corporate VPN is treated differently than the same query routed through public wifi. Time, too, can shift risk scores. A lookup at 2 p.m. on a workday looks normal, but a sudden surge of queries at midnight can raise suspicion. In other words, context isn’t fixed. It moves with the user, the device, and the workload.

The responses could be just as dynamic. Instead of a binary yes/no, agents adapt to risk. In a low-risk context, results return in full. The same query, issued from a higher-risk environment, might be trimmed to read-only or have sensitive fields masked.

This adaptability is what keeps resilient AI systems going. Agents can run continuously across multiple sources without pausing for manual checks. Yet, their reach is always bounded by the live signals surrounding the request. Context-aware permissioning doesn’t just check who the user is. It also checks whether the system should grant access in this specific time, place, and condition.

How Teams Put Context-Aware Models into Practice

What makes context-aware permissioning challenging is the trade-offs. Every approach buys security at the cost of latency, complexity, or integration overhead. The patterns below demonstrate some common benefits of context-aware permissioning alongside their caveats.

Conditional Delegation with Context Scoping

Traditional delegations work on a simple premise: The agent inherits a human user’s identity and its scope of access stays fixed until the token expires. While a good start, this approach doesn’t factor the risk of human error or a user being over-permissioned.

Conditional delegation replaces that static inheritance with a dynamic exchange. Each time the agent presents a user token, a policy decision point (or PDP) evaluates the surrounding signals. Then, it issues a downstream credential trimmed to fit these conditions.

The effect is finer-grained control. A developer role may keep write privileges in staging, but if the same developer’s laptop starts to drift out of compliance, the PDP can automatically downgrade production access to read-only.

There is a catch, however: operational overhead. PDPs need real-time feeds from these downstream services. Teams face messy work when they stitch those signals together across their ecosystem.

Mid-Session Risk Re-Evaluation

Systems that rely on static tokens (e.g., JWTs) assume that an issuer’s status never changes during the token’s lifetime. But the reality is that a user may be off-boarded mid-shift or a device could fall out of compliance. The chances are (fairly) low, but the consequences can be damaging, such as a user accessing a bank account that they’ve been removed from.

Mid-session risk re-evaluation removes that blind spot by treating tokens as ephemeral. Systems modeled after Continuous Access Evaluation (CAE) don’t wait for expiration. Instead, they use revocation channels to terminate sessions when token permissions change.

The trade-off is latency and coordination. Each re-check adds a performance hit, and revocation requires tighter integration across downstream services. But for workloads where a single unauthorized request can expose extremely sensitive info—such as patient data in a healthcare application where access is ephemerally granted to care providers—the cost of stale permissions might be worth the cost of extra milliseconds.

Adaptive Responses

Most enterprises treat access as a binary switch: grant or deny. That rigidity often hinders AI agents that operate in workflows with many un-deterministic steps. A deny blocks all data, but it also stops the agent from moving forward.

Adaptive responses offer a middle ground. Instead of completely shutting the agent down, the system throttles request rates to slow any potential damage. Or, it routes results through human review before release. The agent keeps functioning, but with some guardrails.

The ability to degrade gracefully is especially valuable in AI systems where availability is a priority. Customer support bots or compliance review assistants can’t just error out on every elevated risk. By applying tiered responses, we are able to strike a balance to keep the system operational.

However, implementing this is quite complex. Policies need fine-grained enforcement, sometimes down to the field level. Additionally, transparency matters. Logs and audit trails must explain why the system masked a field or throttled a query so that security teams can reconstruct decisions months later.

Behavioral Context as Input

Additionally, an agent’s behavior is a signal. Agents leave trails of telemetry in the form of query patterns, download volume, request timing, and more. A sudden spike of bulk exports or simultaneous logins from distant regions suggests elevated risk.

Developers can account for this risk with behavior-based checks. Humans might take hours to manually exfiltrate a dataset. An agent can do the same in less than a second if left unchecked. When developers feed behavioral signals into the PDP, the system can automatically catch and respond to misuse without waiting for human review.

The hard part is calibration. Thresholds set too tightly flood users with re-authentication prompts while thresholds set too loosely let anomalies slip past. To reach higher-confidence decisions, most enterprises blend behavior scores with other context inputs (i.e., device posture, network, location).

Closing Thoughts

Context-aware permissions are straightforward in principle. You evaluate live signals, trim scope, and adapt to risk. In practice, adoption is harder. Every extra check adds latency. Fragmented systems send signals asynchronously. Developers must add extra checks for complex token exchange flows. And the system must log each masked field or throttled request clearly enough for security teams to explain six months later.

Still, the investment pays off for sensitive applications. Role-based access defines what a user should be able to do, but only context-aware permissions ensures those guarantees hold in the moment. It ties identity to the live conditions of a request to make AI agents more predictable.

That shift works best when authorization is centralized. Tools like Oso provide a control plane where policies are defined once and enforced consistently across apps and APIs. Instead of rewriting context checks for every service, teams can use Oso to manage them centrally.

If you would like to learn more, check out the LLM Authorization chapter in our Authorization Academy.

FAQ

What is context-aware permissioning?

It’s an access model where each request is evaluated against live conditions (i.e. device posture, network, behavior) rather than relying only on static roles assigned at login.

Why aren’t static roles enough for AI agents?

Static roles assume conditions don’t change mid-session. But agents run at machine speed, often across multiple systems. A stale token can keep working even after a user is off-boarded or a device falls out of compliance.

What’s the risk of using service accounts for agents?

Service accounts usually hold broad, long-lived privileges. When an agent runs under one, it bypasses user-specific roles and revocations, turning a single integration into a systemic exposure point.

What is mid-session risk re-evaluation?

It’s a mechanism where tokens are short-lived and continuously re-validated. If risk signals change (like a device falling out of compliance), systems can revoke sessions immediately rather than waiting for expiration.

What are adaptive responses?

Adaptive responses replace binary “grant or deny” outcomes with graduated actions. Instead of blocking an agent entirely, systems can redact sensitive fields, throttle request rates, or require human approval.

How does behavioral context factor into permissioning?

Agents generate telemetry (query patterns, data volume, request timing) that can be scored against baselines. Sudden anomalies trigger re-evaluation.