AI Agents: Beyond Visibility, Proactive and Contextual Enforcement Is Needed

Introduction

The security of artificial intelligence agents is undergoing rapid evolution. According to an analysis published on July 24, 2026, simply cataloging the agents active in an organization is no longer sufficient to ensure protection. While the initial wave of attention focused on adoption and then on discovery of these components, today the real challenge is effective control of their behavior. AI agents are not simple scripts: they reason, invoke APIs, access sensitive data, and make decisions without continuous human supervision. That is why security enforcement must go beyond visibility, integrating identity, intent, and lifecycle into a coherent governance model.

Technical Analysis

Traditional IAM (Identity and Access Management) systems were designed for human users and machines with predictable workflows. An AI agent, however, pursues dynamic objectives: it can trigger operational paths not anticipated by designers and combine permissions in novel ways. This polymorphic behavior makes static controls largely ineffective. The risk increases when agents share tokens, service accounts, or OAuth authorizations without any correlation between who created them, for what purpose, which systems they touch, and what their actual behavior is over time.

Today organizations use fragmented solutions: prompt filters, identity-level controls, inventory tools that map assets, identities, and permissions. But this data remains isolated in silos (cloud, SaaS, Infrastructure-as-Code) and does not allow an overall assessment. For effective enforcement, it is instead necessary to correlate multidimensional information: owner, consumers, identities used, declared intent, reachable resources, historical usage, and lifecycle stage. Only in this way can a perimeter of legitimate operation be defined and deviations detected in a timely manner.

Impact

The absence of a contextual enforcement model exposes organizations to concrete and severe risks. An agent can perform unauthorized actions on critical data or infrastructure, with possible operational damage and legal consequences. Moreover, it is difficult to determine whether a behavior is appropriate or to rapidly revoke access when conditions change. The phenomenon of "silent expansion" of tasks also emerges: over time, the agent takes on functions outside the original scope without anyone noticing. The false reassurance provided by a simple inventory aggravates the danger, because it leads to believing that the situation is under control while in reality the chain of responsibility is broken.

Mitigation

To address these critical issues, experts recommend shifting the focus from visibility to proactive enforcement. It is not enough to discover what an agent does: one must define in advance what it can do, under what circumstances, and for what purpose. This requires controls that act before, during, and after the execution of actions, not just after-the-fact remediation.

On a practical level, organizations should:

• Correlate the contexts of identity, intent, access, and behavior to build dynamic rules that reflect the actual operation of agents.
• Extend existing IAM policies with attributes related to intent and lifecycle (who created the agent, when it was last updated, which APIs it actually uses).
• Integrate currently separate tools (identity fabric, cloud security, API monitoring) into a single enforcement view.
• Apply recent guidelines that prescribe evaluating risks of privilege, authentication, accountability, and design before introducing agents into critical flows.

FAQ

1. Why is an inventory of AI agents not sufficient?

A simple list does not reveal how an agent operates or whether its actions remain consistent with the initial purpose. Without active, contextual control, an agent can deviate toward risky behaviors without the organization being aware, undermining security even when it is believed that everything has been mapped.

2. What are the main risks of uncontrolled AI agents?

Risks include privilege escalation, unauthorized operations on data and infrastructure, loss of action traceability (accountability), and progressive extension of tasks beyond the intended scope. Additionally, the unregulated sharing of agents and identities amplifies the attack surface and complicates access revocation.

3. How can a company implement effective enforcement for AI agents?

The company must define policies that link the agent's identity to its intent, reachable assets, and operational conditions (who, what, when, why). It must then apply controls in three phases: before execution (authorization validation), during (real-time monitoring and selective blocking), and after (forensic analysis and policy updating). It is essential to integrate data from IAM systems, cloud, SaaS, and infrastructure into a single governance platform, overcoming the current fragmentation.