Evaluating Access Against Live Signals
How Teams Put Context-Aware Models into Practice
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:
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.
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.
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 However, if this AI support bot has a stale token and therefore outdated permissions, it might end up sharing customer information that it wasn’t delegated to access.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.
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.
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.
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.
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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.
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.
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.
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.
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.
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).
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.
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.
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.
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.
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.
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.
Agents generate telemetry (query patterns, data volume, request timing) that can be scored against baselines. Sudden anomalies trigger re-evaluation.