According to Gartner® ^[1][2], “Gartner’s 2025 survey reveals that while 50% of IT leaders believe they have sufficient governance, 84% admit they need stronger technical controls to secure AI agents.”

The appeal of AI Agents is that their automation and decision-making capabilities can be leveraged even by non-technical users. Unfortunately, that is also why they are so risky. Growing particularly popular among non-technical users, who benefit from automation of complex tasks and powerful workflows, the easy accessibility of AI agents is also catalyzing their governance uncertainties.

A significant portion of the workforce using these agents lacks a deep understanding of how they function, what data they access, or what their outputs can trigger. This “unstructured empowerment” leads to inconsistent usage, hidden dependencies, and inadvertent exposure of sensitive data. As a result, organizations face a growing gap between AI adoption and AI control.

Therefore, let’s try to understand the risk that these agents pose, how they can be made visible to risk admins for appropriate governance efforts, and how the Truyo AI Agent Use Case Discovery feature can help

The Chaos Behind AI Agent Sprawl 

The biggest challenge with AI Agents is that there are so many of them. Their uncontrolled proliferation across multiple platforms, including SaaS tools, copilots, APIs, and no-code environments, often comes without centralized oversight.

• Visibility: A primary governance risk is the lack of comprehensive visibility into all active AI agents. Many are created outside formal IT processes using no-code tools or embedded copilots, leading to “shadow AI.”
• Privacy Risk: AI agents often access, process, and transmit sensitive data, including PII, financial records, or proprietary information. Without proper data mapping and controls, this can lead to unintended data leakage through prompts, outputs, or third-party integrations.
• Identity Risks: When agents are not treated as governed identities, they may inherit excessive or inappropriate permissions. Weak IAM practices can allow agents to access systems or data beyond their intended scope, increasing the blast radius of misuse or compromise.
• Third-Party Risk: AI agents are highly sensitive to inputs, making them vulnerable to prompt injection, manipulation, or unintended usage patterns. Without prompt monitoring and guardrails, users may inadvertently trigger unsafe behaviors, leading to harmful outputs, data exposure, or policy violations.
• Compliance Risk: Without robust logging and audit trails, organizations cannot trace agent actions, data usage, or decision pathways. This lack of traceability makes it difficult to demonstrate compliance with regulations or investigate incidents.

Scaling AI Agents Without Losing Control 

It all comes down to introducing systems that can treat AI agents as accountable tools rather than opaque and fragmented programs. Here’s how businesses can ensure scalable AI agent adoption while balancing innovation with control.

1. Discovery & Visibility

Discover agents
Organizations today operate in fragmented AI ecosystems—agents exist across SaaS apps, internal tools, APIs, and employee-built workflows. Relying on self-reporting creates blind spots because many agents are spun up informally. Automated discovery ensures every agent is identified through system-level observation (APIs, logs, integrations), giving organizations a complete and unbiased view. This is foundational—without discovery, governance is inherently incomplete.

Inventory
Once discovered, agents must be continuously tracked in a centralized inventory. This is not a static list, but a real-time system of record capturing agent metadata—purpose, owner, model used, configurations, and activity. A dynamic inventory allows security, compliance, and business teams to answer critical questions instantly: What agents exist? What are they doing? Who owns them? Without this, governance becomes reactive and fragmented.

Shadow AI detection
The democratization of AI means non-technical users can easily create agents using no-code tools or copilots. While empowering, this introduces unmanaged risk. Shadow AI detection surfaces these unsanctioned agents by identifying anomalous or unregistered activity. This capability is essential to prevent data leakage, policy violations, and regulatory exposure caused by well-intentioned but ungoverned innovation.

2. Context & Understanding

Use case classification
Knowing an agent exists is not enough—you need to understand why it exists. Use case classification translates raw agent activity into structured business functions (e.g., customer support automation, data analysis, code generation). This makes AI understandable to non-technical stakeholders and allows organizations to align governance controls with intent. It also enables prioritization—some use cases inherently carry a higher risk than others.

Model identification
Agents often rely on underlying models that may change dynamically or be abstracted away by platforms. Automatically identifying which models are being used—including third-party dependencies—helps organizations assess risk related to data handling, bias, and compliance. It also ensures visibility into where data is being sent, which is critical for regulatory requirements like data residency and vendor accountability.

Prompt monitoring
Policies based on assumed usage often fail because real-world behavior diverges. Capturing actual prompts provides direct insight into how agents are being used in practice. This reveals misuse, sensitive data exposure, or unintended behaviors that would otherwise go unnoticed. Prompt monitoring bridges the gap between design intent and operational reality, enabling more accurate governance and faster intervention.

3. Data & Access Governance

Data mapping
AI agents interact with multiple data sources—structured databases, documents, APIs, and user inputs. Data mapping tracks what data is accessed, processed, and potentially retained or used for training. This visibility is critical for identifying exposure of sensitive data such as PII, financial information, or intellectual property. Without it, organizations cannot confidently meet privacy or data protection obligations.

IAM (Identity & Access Management)
Agents should be treated as first-class digital identities, not just tools. Assigning them identities allows organizations to enforce authentication, authorization, and least-privilege access. This ensures agents only access what they need to perform their function, reducing the blast radius of potential misuse or compromise. It also enables accountability—every action can be traced back to a controlled identity.

Information governance
Beyond access control, organizations need policy-driven safeguards on how data is used. Information governance enforces rules such as masking sensitive data, restricting data transfer, or preventing use of regulated datasets. This ensures that even if an agent has access, it cannot misuse that data. These controls are essential for maintaining compliance with regulations like GDPR, HIPAA, and emerging AI governance standards.

4. Risk & Control Enforcement

Risk scoring
Not all agents pose equal risk. Risk scoring automatically evaluates agent behavior, data usage, and context to assign a risk level. For example, an agent handling public FAQs is lower risk than one processing financial data. By quantifying risk, organizations can prioritize oversight and remediation efforts, ensuring that governance resources are applied where they have the greatest impact.

Policy enforcement
Defining policies is meaningless without enforcement. Policy enforcement ensures that governance rules are actively applied to agent behavior in real time. This could include blocking certain data access, restricting external API calls, or preventing use of unapproved models. Enforcement transforms governance from a passive framework into an active control system that shapes how agents operate.

Guardrails
Guardrails act as preventative controls that stop unsafe or non-compliant actions before they occur. These include filtering harmful prompts, preventing sensitive data exposure, and restricting high-risk actions. Guardrails are especially critical in autonomous or semi-autonomous agents, where decisions are made without human intervention. They ensure safety without requiring constant manual oversight.

Prioritization
In large environments, organizations may have hundreds or thousands of agents. Prioritization ensures focus on the agents that matter most—those with high risk, high impact, or high usage. By combining risk scoring with contextual insights (e.g., data sensitivity, business criticality), organizations can allocate resources effectively and avoid being overwhelmed by volume.

5. Continuous Assurance & Oversight

Monitoring
AI systems are dynamic—they evolve based on new data, updates, and user interactions. Continuous monitoring tracks agent behavior, outputs, and changes over time. This enables early detection of drift, misuse, or emerging risks. Without continuous monitoring, organizations are effectively blind to how their AI systems behave after deployment.

Auditing
Regulatory and internal accountability require traceability. Auditing ensures that every agent action, decision, and data interaction is logged and retrievable. These logs provide evidence for compliance, support forensic investigations, and enable transparency. Strong audit capabilities are increasingly becoming a regulatory expectation for AI systems.

Feedback loops
Governance must improve over time. Feedback loops use insights from incidents, audits, and performance metrics to refine policies, controls, and models. This creates a learning system where governance becomes more effective as the organization gains experience. Without feedback loops, governance remains static and quickly becomes outdated.

Scalability
AI adoption is accelerating, and governance must scale accordingly. Manual processes cannot keep up with thousands of agents across multiple platforms. Scalable governance frameworks ensure consistent application of policies, controls, and monitoring regardless of volume. This is critical for sustaining innovation while maintaining control in enterprise environments.

Truyo AI Agent Use Case Discovery

Truyo’s AI Agent Use Case Discovery enables organizations to cut through the opacity of AI adoption by automatically identifying where and how agents are being used across the enterprise. Instead of relying on assumptions or self-reporting, it captures real agent activity, translates it into clear business use cases, and surfaces hidden risks tied to data access, models, and behavior.

• Continuous visibility into active AI agents across the enterprise
• Scalable governance frameworks capable of managing thousands of agents
• Reduced reliance on manual AI use case reporting through automated discovery
• Automated translation of technical metadata into contextual risk assessments
• Prioritized and defensible approaches to risk management
• A shift from reactive oversight to proactive AI governance

From Agent Sprawl to Accountable AI

AI agents are redefining how work gets done, but without the right governance foundation, their rapid proliferation can just as quickly introduce systemic risk. The path forward is not to slow adoption, but to operationalize control, embedding visibility, context, data governance, and continuous oversight into every layer of the agent lifecycle. Organizations that succeed will be those that treat AI agents as accountable, observable, and governed entities—not black-box tools.

Truyo’s AI Agent Use Case Discovery helps organizations take the first critical step by automatically uncovering how agents are being used across the enterprise.

References

[1] Source: Gartner Report, Act Now: Take These 5 Steps for AI Agent Assurance, By Avivah Litan, Max Goss, etc., January 2026.

[2] Gartner is a trademark of Gartner, Inc. and/or its affiliates.