How to improve visibility across the enterprise AI ecosystem

The gap between enterprise AI adoption and governance is increasingly difficult for security leaders to ignore. Cisco’s 2025 Cybersecurity Readiness Index found that 60% of organizations are unaware of the specific requests employees make to GenAI tools. That lack of visibility makes it more difficult to monitor data movement, enforce policies, and understand what tools or agents are operating across the enterprise.

The issue is structural, not cultural. Organizations built their monitoring tools to track traditional software, and these systems were never designed to detect how AI moves through a network in the first place. Standard discovery tools can identify a software subscription, but often miss AI usage patterns entirely.

When employees bypass official channels to use AI tools, IT loses visibility into where sensitive company data is actually going. This structural gap poses a real operational risk, as data flows to destinations that the security team cannot monitor or control.

Shadow AI refers to employees using AI tools and applications without explicit approval from the organization. This differs from traditional shadow IT because fraudulent software subscriptions remain visible to standard discovery tools in a way that using AI often does not. Each dimension of shadow AI carries a different risk profile and requires a different type of response.

A common version of this risk occurs when an employee pastes a document or set of data into a public chatbot to save time on a routine task. This behavior is rarely malicious. It reflects that ordinary workers are looking for the most convenient tool available, not an intention to circumvent safety protocols.

This risk lies in the tools that a company has already approved, as the original security review predates the AI ​​features added to the platform later. Integrated capabilities are harder to detect than using standalone tools because the traffic appears identical to normal platform activity from a monitoring standpoint.

Agents act within a system rather than simply answering a question, which sets them apart from most assistants. Monitoring lags far behind implementation because agents are often brought in quickly to resolve an immediate workflow issue without a formal review process. An agent acting with unsupervised access can affect systems and data at a speed that no human review process can match, making this an urgent risk to address.

The failure is architectural rather than a matter of insufficient effort or budget. Traditional security tools were built to track known software in expected locations, which is at odds with how AI capabilities actually move through an organization.

A tool created to catalog applications has no reliable way to classify or control the behavior of an AI agent acting within one. The monitoring systems most companies rely on simply lack the framework to capture patterns of AI activity, creating visibility gaps that grow as AI adoption accelerates.

Organizations must adopt specific strategies to close the visibility gap and regain control of AI activity across the enterprise. The following approaches provide the foundation for protecting AI ecosystems.

A one-time audit is not enough because new AI tools and features are continually added rather than following a predictable schedule. Security teams must apply the same discipline used for cloud workloads, where every asset is tracked as a matter of routine and not just after an incident.

A live inventory allows security teams to maintain a current picture against which to measure new activity, rather than rebuilding it after something goes wrong. This continuous approach ensures that the organization knows what AI capabilities are available in the environment at any given time.

Effectively protecting AI requires applying it to the problem, as human review alone cannot keep pace with the volume and speed of the task. Platforms that use advanced behavioral analytics can help security teams identify unusual AI activity without relying solely on known attack signatures.

Darktrace provides an example of a platform built around this approach. The company has been a pioneer in AI since 2013, before the most recent wave of AI-branded security tools. Its platform uses multi-layer AI to provide visibility into the on-premises network, cloud applications, email, OT systems, and endpoints.

What makes this approach unique is that there is no starting point or prior assumptions about what a threat looks like. The technology learns every device, user and interaction, developing an understanding of normal behavior from what it observes. This allows you to detect and match subtle behavioral anomalies that indicate a threat, while other security solutions attempt to predefine what constitutes a threat based on attack patterns observed in the past.

No system or agent should be granted access based on assumed trust rather than a specific, verified need. AI agents can act through data, tools and applications. Because of this, each agent should receive only the minimum access required for their specific task.

Properly scoped access limits the damage that can be caused by an undetected compromise or malfunction. This principle becomes especially important in AI environments where agents operate at machine speed and can propagate problems faster than human operators can respond.

Visibility remains the critical step in ensuring AI innovation across the enterprise. Organizations that deploy advanced threat detection and continuous discovery platforms position themselves to effectively protect their AI ecosystems. IT leaders should prioritize platforms that provide comprehensive coverage across AI touchpoints and use behavioral analytics instead of signature-based detection to identify threats in real time.