As the internet evolves, misconfigured permissions become a much bigger threat. Why? Because of two words: artificial intelligence—or, AI.
Enterprise organizations have always needed tight control over their systems. Permissions are necessary for protecting access to resources, as well as meeting compliance rules and customer obligations. An over-permissioned user would be able to access sensitive information; for example, manager-level permissions that let an employee access their entire team’s salary when they should only be able to check their own. Once teams spot such mistakes, they can correct it and move forward without much disruption.
That’s no longer true once AI enters the picture. AI agents do what humans do, from accessing data to sending emails. But unlike humans, they move thousands times faster—which means their mistakes move faster too. When a human makes a mistake, it’s just one mistake. When an AI agent makes a mistake, it can quickly snowball into thousands more. This is because of three characteristics:
- Multi-System. AI agents usually don’t operate with a single system. They pull and push data across CRMs, databases, and whatever other systems are needed. So if an agent makes a single bad request, it can spread incorrect information across multiple systems. With write access, the agent can accidentally execute destructive actions like deleting or overwritten critical data.
- Scale. A human analyst might only run five queries in an afternoon. An AI agent on the other hand, might execute thousands in just a few seconds. Over-permissioning of humans has been tolerable, because impact is limited by time. But the slightest over-permissioning of an agent can lead to a volume of mistakes before security teams can even react.
- Blind Execution. Once an AI agent gets a valid token, it can continue running until the token expires. It doesn’t check if the user has been deactivated or for any other context. Everything seems to “just work,” but that seamlessness hides a serious gap: each request can slip past risk signals that a human would recognize.
Because of these risks, agents as both powerful and dangerous. Not only do they increase a user’s capacity, they also amplify possible consequences. The solution (although it is really just a precaution) is context-aware permissions. Rather than grant an AI agent static permissions, the system verifies every action based on the live state of the request. For example, a financial application might prevent a sudden request at 3am if it is normally only used during the day.
In this article, we’ll dive into how context-aware models work, common patterns, good practices, and the challenges to consider at scale.
Understanding the Risk
Although context-aware permissions clearly help lower risk, what actually are the risks? Without these safeguards in place, what is the worst that could happen? The answer: a lot. Let’s look at three scenarios.
Customer Data Exposure
Consider an AI support bot that is tasked to retrieve data from a CRM and use it in another system (e.g. Snowflake) or to send emails. If this bot has a stale token which holds outdated permissions, it could unintentionally expose customer information that it is no longer authorized to access. While this may seem harmless in theory, it can dangerously violate customer data custody contracts and create legal liabilities.
Information Misconfiguration
If an AI agent regularly reads from databases, but has mis-scoped access, then it could accidentally pull more data than intended. For example, suppose an AI agent that is only meant to query a database with test accounts. With misconfigured permissions, an agent might pull information about actual production accounts instead. That agent might then inadvertently leak customer data.
Uncontrolled Bulk Actions
An AI agent could be assigned to clean-up accounts that have been marked for deletion, such as due to inactivity. But if the agent has broad access, then it might mistakenly delete all accounts because the model’s non-deterministic nature. Without proper controls, an unsupervised AI agent can easily wipe out terabytes of informations within minutes.
Evaluating Access Against Live Signals
Context-aware permissioning examines the contextual signals of each request by gathering environmental cues, such as device types or network security. For instance, an up-to-date company-managed laptop with would be considered lesser risk than a personal smartphone on public Wi-Fi.
Network conditions also play a role. A request made through a corporate VPN is different from the same request made through public Wi-Fi. Timing influences risk scores as well. A query in the middle of the work day is expected, and much more normal than a sudden spike in activity at midnight. In short, context fluid. It shifts with the user, device, and activity.
As such, the responses can be just as dynamic. Rather than a simple yes/no, agents adjust their behavior based on risk. In a trusted context, full results might be delivered without issue. But when conditions are riskier, the same query might be reduced to read-only or have sensitive details redacted.
This adaptability is what sustains resilient AI systems. Agents can operate across several sources without stopping for manual checks, yet their actions are still tightly governed by the live contextual signals of each request. Context-aware permissioning weighs identity beyond just the user—time, place, and conditions all matter.
How Teams Put Context-Aware Models into Practice
Context-aware permissioning becomes more difficult when considering its trade-offs. These strategies strengthen security but introduce drawbacks such as increased latency and system complexity. Tools such as Oso can help mitigate some of these issues, particularly to simplify developer effort. The following patterns highlight both the advantages and disadvantages of context-aware permissioning.
Conditional Delegation with Context Scoping
Conventional delegation models work on a simple principle: The agent assumes the identity of a human user and retains the defined access scope until the token expires. While a good baseline, this method overlooks the risk of an over-permissioned user or general human error.
On the other hand, conditional delegation transforms static inheritance into a dynamic evaluation process. Whenever the agent presents a user token, a policy decision point (PDP) assesses the surrounding signals and then generates a downstream credential scoped to fit those conditions.
The result is finer-grained control. For example, a developer might retain write access in staging, but if their laptop falls out of compliance, the PDP can adjust permissions to read-only.
The downside, however, is operational overhead. PDPs rely on real-time feeds from downstream services, which can get messy as developers try to stitch signals across a distributed ecosystem.
Mid-Session Risk Re-Evaluation
Static-token systems (e.g., JWTs) operate on the assumption that the issuer’s status won’t change during the token’s lifespan. In reality, an employee could be off-boarded mid-shift or a device could fall out of compliance. Although these situations are infrequent, the potential impact is severe. For instance, a user retaining access to a bank account they were removed from.
Re-evaluating risk during a session eliminates that blind spot by managing tokens as temporary. Systems modeled after Continuous Access Evaluation (CAE) principles don’t wait for tokens to expire. Instead, they use revocation channels to end sessions immediately whenever token permissions are updated.
The downside is added latency and coordination. Every re-evaluation incurs a performance cost, and revocation needs tighter integration across downstream services. For workloads where even a single unauthorized request could compromise highlight sensitive information, such as patient data in a healthcare app where access is temporarily granted to care providers, the trade-off between a few extra milliseconds is often justified.
Adaptive Responses
Most enterprises still manage access as a binary decision: grant or deny. This all-or-nothing approach does not work well with AI agents operating in workflows with adaptive steps. A request denial blocks data, but also halts the agent’s entire process.
Adaptive responses introduce a more flexible alternative. Rather than stopping the agent entirely, the system can either limit request rates or route the request to a human for review. The agent is able to continue operating, but with guardrails to limit potential damage.
This method of graceful fallbacks is particularly important in AI systems where uptime matters most. Customer support bots for instance can’t simply fail whenever a risk arises. By implementing tiered responses, the system maintains a balance between availability and safety.
However, putting adaptive responses into practice is far from simple. Policies require fine-grained enforcement, sometimes at the field level. Transparency is also important because security teams must be able to trace the system’s decisions (such as why it throttle a query) through comprehensive logs and audit trails.
Behavioral Context as Input
Even an agent’s own behavior can serve as a signal. Agents generate telemetry through query patterns, download volumes, request timing, and more. A sudden surge in a certain action or concurrent logins from different locations can indicate heightened risk.
Developers can mitigate this risk by incorporating behavior-based checks. While a human might take hours to extract a dataset, an unmonitored agent can complete the same task almost instantly/ By supplying the PDP with behavioral signals, the system can identify and counter misuse immediately without human intervention.
The real challenge here is calibration. If thresholds are too strict, users will be overwhelmed with re-authentication requests. If thresholds are too lenient, suspicious activity can slip by unnoticed. To improve decision accuracy, most enterprises combine behavior scores with other contextual signals (such as device or location).
Closing Thoughts
Context-aware permissions are simple in theory, but much harder in practice. Every time a live signal is evaluated, that additional check adds latency. Fragmented systems deliver signals asynchronously. Complex token exchange flows will require extra validations. And every masked field or throttled request must be accurately logged for security teams to analyze even months later.
Even so, the effort is worth it for sensitive applications. Role-based access determines what a user should be able to do, but it is context-aware permissions which ensure that those rules are actually being enforced correctly. By linking identity to the current conditions of every request, it makes AI agents’ behavior more predictable.
This approach is most effective when authorization is centralized. Platforms like Oso offer a unified control plan where policies are written once and consistently enforced across applications and APIs. Rather than implementing context checks independently for every service, teams can manage them in one central location using Oso.
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 that evaluates every request based on the current conditions, such as device and network, rather than depending on static roles.
Why aren’t static roles enough for AI agents?
Agents run at machine speed, sometimes across multiple systems simultaneously. Conditions might change mid-session, but static roles don’t account for that. This means a stale token can continue working even after the user it belongs to is off-boarded.
What’s the risk of using service accounts for agents?
Service accounts often carry broad, long-lived permissions. If an agent operates under such an account, it can bypass user-specific roles and revocations. This can turn a single integration into a system-side security exposure.
What is mid-session risk re-evaluation?
It’s a system where tokens are short-lived and constantly re-validated. If risk indicators signal change, such as a device falling out of compliance, sessions can be revoked instantly instead of waiting for the token to expire.
What are adaptive responses?
Adaptive responses move beyond simple “grant or deny” decisions with graduated actions. Rather than blocking an agent completely, systems can instead redact sensitive data or limit request rates.
How does behavioral context factor into permissioning?
Agents produce telemetry (query patterns, data volume, request timing) that can be compared to established baselines. Unexpected deviations can then trigger re-evaluation.
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