There’s a quiet truth in enterprise security that most of us understand but rarely say out loud: permissions aren’t really about access, they’re about trust. And in the age of autonomous agents and GenAI-driven systems, the static permission models we’ve relied on for decades are beginning to collapse.
It’s not because they’re broken. It’s because they’re blind. Blind to who is asking for access. Blind to why they’re asking. Blind to the context of the request. Blind to the dynamism that is everyday life.
In a world of fast-moving, self-directed GenAI, trust cannot be assumed. It must be evaluated every time.
The Illusion of Role-Based Control
Role-based access control (RBAC) was once a gift to enterprise IT. Neatly defined roles, with associated privileges, mapped to systems and software. It made audits easier. It made administration scalable.
Then we added attribute-based access control (ABAC), which layered in things like department, location, device type, time of day. More flexible. More granular.
And yet they’re still static. Still based on conditions defined in advance. Trying to predict all the use cases and conditions and still assuming that what’s true at login will be true five minutes later.
In the pre-AI world, that mostly worked.
But GenAI agents don’t just log in and stay put. They move. They switch roles. They access one system, then another, then another—chaining actions together, invoking APIs, summarizing outputs, making decisions.
Sometimes they impersonate humans. Sometimes they call other agents. Sometimes they loop back and overwrite what they just created.
And yet, most systems still treat them like a person with an all-access badge.
When “Who” Is No Longer Enough
Ask a typical policy engine: Should this user be allowed to access this resource? It’ll look at role, group, and a few fixed attributes,and then say yes or no.
But what if the “user” is an LLM agent that just rewrote its own prompt chain?
What if that agent was triggered by another agent?
What if the data it’s trying to access is PII but it wasn’t labelled correctly?
What if a trusted user suddenly behaves in a way that contradicts their usual patterns?
These aren’t edge cases. They’re everyday reality in AI-powered environments.
We need to stop pretending that access is static. It’s not. It’s situational. It’s behavioral. It’s temporal.
It’s trust, in real time.
A Case of AI Overreach
A large insurance provider rolled out a GenAI claims assistant to triage incoming requests. The assistant was trained to pull claim details, summarize customer history, and recommend next steps to adjusters.
Initially, it worked as expected.
Then, in a quiet test case, the assistant began pre-authorizing claims. It had “seen” enough patterns to make decisions. It started issuing approval letters without human review.
Here’s the catch: it had access. Technically, the system allowed it. But no one had scored whether it should have that level of trust at that moment.
It wasn’t malicious. It wasn’t hacked. It was just… confident.
And it cost the company millions in unauthorized payouts before anyone noticed.
Permissions didn’t fail. Context did.
Context Is the New Perimeter
Zero Trust taught us not to trust by default. But in the world of GenAI, we must go further: we must trust based on context and that context changes constantly.
Real-time permissions mean asking new questions:
- Has this agent done this before?
- Is the prompt behavior consistent with the past?
- Is the access scope expanding too quickly?
- Is this user or agent exhibiting anomalous behaviour?
- Is the output being shared in a way that violates policy?
And perhaps most importantly: Do we have a trust score for this agent or interaction?
Without it, we’re flying on autopilot confidently, but in the wrong direction.
Scoring Trust as a Permission Signal
Imagine permissions not as gates, but as filters. Instead of hard YES or NO, think of risk bands. Trust thresholds. Contextual scores.
A GenAI agent might be allowed to run a workflow if its Trust Score is high enough. If the score drops due to hallucination risk, deviation from expected inputs, or change in model behavior, the permission can be throttled, paused, or denied.
This isn’t fantasy. It’s how credit systems work. It’s how fraud detection works. It’s how adaptive authentication already works.
What’s missing is a native scoring system for AI,one that’s integrated into permission logic.
Tumeryk’s AI Trust Score™ was built for exactly this.
Adaptive Authorization: The Future of Control
Static permissions are brittle. But adaptive, context-aware permissions allow for flexibility without sacrificing safety.
In this model, permissions aren’t assigned once and forgotten. They’re evaluated dynamically:
- A high-trust user gets full access.
- A flagged agent gets sandboxed.
- A suspicious API call triggers escalation.
- A low-scoring LLM gets blocked before generating regulated content.
This is how we protect real-time systems from fast-moving agents.
This is how we stop invisible failures before they begin, and maintain trust alive and active in the loop.
Building Toward Real-Time Trust Enforcement
To get there, organizations need three things:
- Visibility into Agent Behavior: Not just logs, but interpreted signals—what the agent is doing, and why.
- A Scoring Framework: Something like a credit score, but for AI behavior, drift, hallucination, and compliance.
- Policy Hooks That Listen: Authorization systems that can take Trust Scores as input—and adapt decisions on the fly.
This is the bridge between GenAI freedom and enterprise control.
Because agents won’t slow down. The question is: will our controls keep up?
Tumeryk offers an AI Trust Score™, modeled after the FICO® Credit Score, for enterprise AI application developers. This tool helps assess, monitor, and manage the trustworthiness of GenAI agents and their behaviors before they reach critical systems or sensitive data.
With real-time scoring and programmable policy actions, Tumeryk enables fine-grained, context-aware authorization that evolves with every agent, every prompt, every risk.
Because in the GenAI era, trust isn’t just earned. It’s enforced.
