A striking recent survey found that over two-thirds of enterprise workers cannot identify actions taken by AI agents operating within their organizations. The root cause: inadequate access controls allow agents to perform operations that human users would never attempt, and most logging systems aren’t configured to capture agentic activity at the granularity required for accountability.
The Visibility Gap
Traditional IT logging follows a human-centric model. A database query, API call, or configuration change is logged as a system event, but the identity performing the action is either absent (in the case of scheduled scripts) or illegible (a service account with a generic name). When AI agents operate under service accounts or shared API keys, the audit trail provides no mechanism to distinguish agent-initiated actions from legitimate administrative operations.
Lax Access Controls Enable This Blindness
The survey attributes this visibility crisis to two systemic issues:
- Over-Privileged Agent Accounts: Agents are granted broad roles to avoid operational friction, but these roles grant permissions far beyond what any individual agent legitimately needs
- Inadequate Logging Scope: Organizations log successful API calls but not the full context of agent-initiated decisions, reasoning chains, or permission paths explored
Reimagining Agent Visibility
Addressing this requires a shift from IT-centric logging to identity-centric logging. Each agent must have a discrete, cryptographically unique identity. Every action must be logged with full context: the agent’s name, its intended purpose, the business justification for the action, and the result. This level of granularity enables security teams to answer the critical question: What is this agent actually doing, and why?
Source: IT Pro