Enterprise developers are racing to deploy AI agents in 2024 and 2025, but a fundamental problem is emerging: the security infrastructure wasn't built for this. Traditional identity and access management systems operate on assumptions that break down when applied to autonomous agents that make thousands of decisions per minute, often in unpredictable sequences.
The challenge isn't just technical complexity. Organizations are discovering they have two populations of AI agents: sanctioned ones built through official channels, and a growing shadow fleet created by employees using readily available tools. This dual reality creates governance blind spots that traditional IAM vendors are struggling to address.
Why Traditional IAM Falls Short for Agents
The core issue is architectural. Conventional IAM systems were designed around a simple model: authenticate a user or machine once, grant permissions, and maintain that access state. This works when a human logs into an application or when a service account connects to a database.
AI agents operate differently. They execute long chains of actions at machine speed, with each step potentially requiring different permissions. An agent might read customer data, analyze it, draft a response, check inventory systems, and initiate a transaction—all within seconds. The access requirements change with each action, and the sequence itself may vary based on what the agent discovers along the way.
Locking down agents with static permissions either cripples their functionality or grants them excessive privileges that persist longer than needed. The result is a security model that oscillates between too restrictive and too permissive, with no middle ground.
The Runtime Authorization Approach
Swedish vendor Curity is taking a different path with its newly announced Access Intelligence platform. Rather than treating agents as enhanced users or service accounts, the system treats them as a distinct category requiring purpose-based access control.
The mechanism centers on OAuth tokens, but extends their role beyond simple authentication. Each token carries metadata about the agent's current intent and purpose. When an agent needs to perform an action, it requests a new token specific to that task. The system evaluates whether the requested action aligns with the agent's stated purpose before granting access.
This means an agent authorized to process customer refunds can't suddenly pivot to modifying user accounts, even if both actions use the same underlying API. The permissions exist only for the duration of the specific task, then expire. For high-risk operations like financial transfers, the system can inject a human approval step into the workflow.
Technical Implementation Details
Access Intelligence operates as a self-hosted microservice that intercepts every agent request. It integrates with Curity's existing Identity Server platform, which handles centralized token validation. This architecture allows developers to spin up new agents or APIs without manual registration—unvalidated agents are automatically isolated from production systems.
The approach differs from inline security tools like API gateways or web application firewalls, which inspect traffic but don't understand agent intent. It also contrasts with behavioral analysis systems that work out-of-band, detecting anomalies after the fact. Curity's model makes authorization decisions in real-time based on declared purpose rather than inferred behavior.
The Broader Security Landscape
Curity isn't alone in recognizing this gap. Major cloud identity providers including Okta, Ping Identity, and Microsoft's Entra ID are developing their own approaches to agent security. The variety of solutions reflects genuine uncertainty about which architectural patterns will prove most effective.
Current approaches fall into several categories. Inline tools like API gateways attempt to apply traditional security rules to agent traffic, but struggle with the dynamic nature of agent behavior. Behavioral analysis systems establish baselines and flag deviations, but operate reactively rather than preventively. Privilege access management vendors are being asked to extend their platforms to cover agents, though many lack clear answers about how to adapt their user-centric models.
The reality is that no single approach addresses all aspects of agent security. Organizations will likely need layered defenses that combine runtime authorization, behavioral monitoring, and traditional access controls. The question isn't which vendor to choose, but how to orchestrate multiple security layers into a coherent strategy.
What This Means for Enterprise Security Teams
The immediate implication is that security teams need to inventory their agent deployments now, before the problem becomes unmanageable. This includes both officially sanctioned agents and the shadow implementations that employees are creating with tools like ChatGPT's custom GPTs or Microsoft Copilot Studio.
Security architectures will need to evolve from perimeter-based models to ones that assume agents are operating inside the network with legitimate credentials. The focus shifts from preventing unauthorized access to ensuring that authorized agents can't exceed their intended scope. This requires rethinking how permissions are granted, monitored, and revoked.
Organizations should also prepare for agents to become a primary attack vector. As agents gain access to sensitive systems and data, they'll become attractive targets for prompt injection attacks, credential theft, and manipulation. Security teams need visibility into what agents are doing, not just what they're allowed to do.
The Path Forward
The agent security market is still forming, which means early adopters will face integration challenges and evolving standards. Curity's cofounder Jacob Ideskog acknowledges this reality, noting that Access Intelligence is designed to work alongside other security layers rather than replace them entirely.
The next 12 to 18 months will likely see rapid evolution in this space as vendors test different approaches and enterprises discover which patterns work in production environments. Organizations deploying agents today should prioritize flexibility in their security architecture, avoiding lock-in to any single vendor's approach until clearer patterns emerge.
