By early 2026, the novelty phase of AI agents has officially ended. What began as excitement around automation has quietly evolved into a looming security risk across modern SaaS environments.  

This shift was evident at the World Economic Forum, where executives discussed the future of AI. Notably, their concerns were no longer about hype or a potential bubble. Instead, the conversation focused on security. As Raj Sharma, EY’s global managing partner of growth and innovation, explained, organizations are not talking enough about the security implications of AI agents — particularly how they are managed throughout their lifecycle. 

Security experts sounded these warning bells months earlier. They pointed out that AI capabilities are advancing faster than the security controls meant to govern them. Despite their growing capabilities, many agents remain poorly monitored, loosely governed, and overly trusted. 

The consequences are already visible. According to research from SailPoint, eight in ten organizations report that their AI agents have taken unintended actions, such as accessing unauthorized systems, sharing inappropriate data, or downloading sensitive information. What’s even more concerning is that nearly a quarter of respondents say their agents have been manipulated into revealing access credentials. 

Ofer Klein, CEO and cofounder of Reco, explained that the reason AI agents introduce such significant security risks is that they can independently interact with identities, data, and systems — often leaving businesses with limited visibility into what those agents are actually doing. 

Despite these risks, adoption continues to grow. The same SailPoint research reveals a striking paradox: while 96% of technology professionals see AI agents as a growing security risk, 98% of organizations still plan to expand their use to maintain a competitive advantage.  

The AI Agent Visibility Gap and Sprawl 

A growing visibility gap is emerging between what AI agents are actually running inside organizations and what security teams believe they own. That gap is said to be where the next wave of enterprise security incidents is likely to originate.  

According to a survey of 600 CIOs, out of 87% of companies that have AI agents embedded in critical systems, only 25% report having full visibility into all agents currently operating in production. 

This lack of oversight quickly shows up in the fundamentals of the agents’ security. Many organizations rely on authentication methods designed for a different era of non-human identities. For instance, some use static API keys, some rely on username-and-password combinations, while others depend on shared service accounts. These persistent credentials create long-lived access pathways — precisely the kind of access model that becomes risky when autonomous systems operate continuously across multiple platforms. 

This visibility problem runs deeper than authentication. Nearly 80% of organizations deploying autonomous AI cannot confidently say what their agents are doing or who is responsible for them. 

This lack of visibility is exactly what allows AI agent sprawl to emerge. 

Without this basic visibility, organizations cannot answer fundamental governance questions like: 

• We Which agents exist 

• Where they run 

• What systems they access; and 

• Who approved them 

Much like API sprawl or the shadow IT era, this pattern starts with small, independent deployments. Marketing teams build agents for content generation, sales deploy agents for lead scoring, and finance automates invoice processing. Each solution works in isolation. Yet over time, agents multiply without centralized oversight. 

Unlike shadow IT, however, AI agent sprawl evolves faster and is harder to detect. With low-code and no-code tools making it easy for any department to create agents, organizations often discover too late that dozens — or even hundreds — of autonomous systems are already operating across their SaaS environments. 

Why Not Use Traditional SaaS Security Tools? 

Well, traditional SaaS security tools were designed for environments where humans interact directly with applications. However, the introduction of autonomous AI agents disrupts this model. AI agents often operate with permissions far broader than those granted to individual users, allowing them to span multiple systems and workflows.  

As a result, when users interact with these agents, they no longer access systems directly. Instead, they submit requests that the agent executes on their behalf, and those actions run under the agent’s identity rather than the user’s. 

This shift breaks the fundamentals of traditional access control and models, which brings significant agent security implications. 

Identity Access Management (IAM), for example, usually uses the user’s identity to decide what they can do. But when an AI agent acts, authorization is evaluated against the agent’s privileges, not the requester’s.  

Consequently, a user with limited permissions can indirectly trigger actions or retrieve data they would not normally be allowed to access. Because logs and audit trails record the agent as the actor, these activities can occur without clear attribution or policy enforcement. 

Human-In-The-Loop Alone is Not Enough 

Many organizations are turning to human-in-the-loop (HITL) to mitigate these risks. This typically requires human validation before agents can access sensitive data, make system changes, approve financial transactions, or grant permissions.  

While rational, this approach is more a symptom than a full strategy: it compensates for weak visibility rather than addressing the underlying governance gap. 

HITL introduces a bottleneck that slows adoption and cannot scale across hundreds of autonomous agents. It also lacks mechanisms for out-of-band liveness checks or consent approvals, leaving organizations exposed to unchecked agent activity. 

Efficient AI Agent Sprawl Solutions 

To effectively manage AI agent sprawl, organizations need a structured approach that combines visibility, access control, and risk management. The following solutions outline how to discover, govern, and secure AI agents as they scale across modern SaaS environments. 

1. Comprehensive AI Agent Inventory

The first step toward controlling AI agent sprawl is achieving complete visibility. Organizations need a single pane of glass that provides a unified view of every agent operating across their environment.  

Whether agents are built on platforms like Amazon Bedrock, Google Vertex AI, or Azure AI and use frameworks such as LangChain, CrewAI, or AutoGen, they should all be catalogued in a centralized agent catalog.  

This catalog acts as an authoritative inventory that continuously discovers and tracks agents across environments. It should identify who owns each agent, where it runs, what systems it connects to, and how it authenticates. 

2. Access and Permission Mapping

AI agents should begin with limited privileges. This is because agents interact with tools, APIs, and internal data sources through automated workflows; therefore, clear boundaries are essential to prevent unintended actions or data exposure.  

Every agent should also receive its own unique identity with permissions scoped to its specific function rather than inheriting access from the deploying user. From there, organizations can apply structured controls such as scoped permissions tied to particular business systems, time-bound credentials that automatically expire, and least-privilege policies that restrict unnecessary access. 

3. Risk Identification, Prioritization & Response

Next, organizations should classify agents into risk tiers based on the sensitivity of the data they access and the potential impact of their decisions. Remediation should then be prioritized using automated risk scoring. This scoring combines dynamic access analysis to detect overprivileged or inactive agents, anomalies, or weak authentication, and breach-likelihood analysis of vendors connected to these agents. 

Reco is one example of a platform addressing this challenge. It inventories all AI agents in an environment and maps their access, permissions, connections, and overall risk posture. This visibility allows security teams to decide which agents should be sanctioned, restricted, or blocked before they introduce risk. The platform also provides guided remediation workflows that help organizations respond quickly to security issues. For example, teams can revoke excessive permissions, disable unauthorized agents, or trigger automated responses through existing security workflows and ticketing systems. 

Embed Governance & Visibility Early to Move Faster With AI Agents 

As AI-driven automation scales to thousands of SaaS applications, enterprises face a growing security blind spot. The solution isn’t slowing adoption; it’s embedding governance and observability from the start. By centralizing agent management on a platform like Reco Security, with full visibility and controls, organizations can deploy agents confidently, accelerate innovation, and scale.