5 clues your network has shadow AI

What is shadow AI?

Shadow AI refers to the use of AI tools and models within an organization without approval or oversight from IT, security or compliance teams. Much like shadow IT, this uncontrolled usage introduces serious risks, such as data leakage, regulatory violations and security gaps, especially when sensitive information is shared with unverified third-party platforms.

Unmanaged BYOD accelerates the spread of shadow AI across organizations. These risks often remain undetected until dedicated teams implement deep visibility and monitoring.

The significance is not theoretical; it's already material. According to a July 2025 report from IBM, one in five organizations has experienced an AI-related breach, yet only 37% have established policies to govern AI usage or detect shadow AI activity.

This gap highlights a critical exposure that sensitive data, including personally identifiable information, can be compromised at any time, putting both trust and corporate reputation at risk.

5 clues your network has shadow AI

Shadow AI is an invisible battleground for many companies. While everything might appear to run smoothly across a network, hidden tools and unsanctioned processes are often operating quietly in the background, without dedicated teams actively detecting them.

The following discusses the top indicators that a network has shadow AI.

1. Shifts in outbound traffic toward AI-related services

A common early signal that a network has shadow AI is a change in how outbound traffic is distributed. Examples of changes include the following:

• Increased connection frequency to external AI service endpoints.
• A higher number of POST requests compared to typical browsing patterns.
• Larger outbound payloads than standard SaaS or web activity.

In some environments, traffic can also show regular transmission of structured data such as JSON, or repeated interactions with inference or API endpoints rather than static content.

What to do: Review your proxy or firewall logs for outbound JSON payloads that contain unusually large text or input fields.   

2. API traffic from unverified endpoints

AI platforms are primarily consumed through APIs, which makes their usage blend into normal application traffic. Indicators of an unmanaged endpoint include the following:

• API calls initiated by user workstations, lab environments or unmanaged hosts.
• Authentication tokens observed outside expected systems or network zones.
• Direct outbound API communication that bypasses centralized services or gateways.

An analysis of network behavior could reveal API usage that doesn't map to known internal applications, or new external endpoints appearing without prior integration records. These patterns often indicate decentralized or unauthorized API consumption, particularly in development-heavy environments.

What to do: Monitor outbound traffic for API keys or tokens that don't map to an organization's approved enterprise accounts.

3. Consistent, non-interactive traffic behavior

Automated processes, including AI agents, tend to produce traffic that lacks the variability of human activity. Observable patterns include the following:

• Requests occurring at steady, predictable intervals.
• Activity continuing beyond normal operating hours.
• Repeated request sizes or similar data structures over time.

That said, these characteristics are not exclusive to AI. Monitoring systems, backups and scheduled jobs can generate similar traffic. The distinction lies in whether the behavior aligns with documented and expected workloads.

What to do: Improve network visibility to identify the source of the activity. If network teams identify unauthorized traffic, they must mitigate the activity and regularly monitor network traffic to conduct periodic checks.

4. Spikes in OAuth permissions for efficiency apps

Organizations operate deeply in digital environments, with countless tools shaping how IT teams work every day. Integrations streamline collaboration and eliminate redundant effort, but they introduce a tradeoff: security.

Employees frequently authorize third-party applications to connect to corporate Google Workspace or Microsoft 365 accounts through OAuth, often to summarize meetings or manage email. Shadow AI frequently enters through third-party platforms that integrate with enterprise systems. Examples include the following:

• Connections to previously unknown external domains.
• Persistent communication following initial authentication or authorization flows.
• Data exchange between internal services and external platforms without clear ownership.

Over time, unmanaged third-party integrations can lead to increased reliance on external endpoints that aren't tracked in the architecture or asset inventories. These patterns should be evaluated against approved service catalogs and known integration points.

What to do: Monitor identity provider logs to find unverified third-party apps that request unnecessary permissions, such as mail read/write access or calendar control.

5. Increased encrypted outbound data transfer 

Most AI-related interactions occur over HTTPS, which limits direct visibility into payload content. Indicators of unmonitored outbound data transfers include the following:

• Sustained outbound encrypted sessions with higher-than-normal data volumes.
• Repeated transfers of similarly sized payloads.
• Disproportionate outbound-to-inbound data ratios.

Because the content is encrypted, analysis relies on traffic metadata volume, frequency and duration, as well as destination patterns and endpoint classification. These signals do not confirm data sensitivity but could indicate unmonitored data movement to external services.

What to do: Use metadata to identify unusual traffic. If any unauthorized traffic is present, mitigate it by restricting its access to the network.

Risks associated with shadow AI 

Shadow AI is often discussed mainly in terms of governance or compliance. However, it's critical to recognize the risks at the network layer, where the actual exposure occurs. Every interaction with an external AI service -- whether a prompt, file upload or API call -- relies on outbound connectivity. If that connectivity is not tightly controlled or fully visible, it's actively traversing the network.

Challenges that can occur in a network with shadow AI include the following:

Data leakage becomes uncontrolled outbound traffic

Data leakage and loss of confidentiality are growing risks in the age of widespread AI tools. With easy access to powerful platforms, employees could unknowingly include sensitive data in their prompts, exposing proprietary information and risking reputational damage through unintended disclosure to public AI systems.

The issue isn't just that data is shared, but that the data is transmitted to external endpoints that the organization might not approve. This enables data to bypass application-level controls by going directly from endpoints. It then embeds in encrypted sessions, which limits inspection.

Without proper egress filtering, DNS visibility or traffic analysis, sensitive information can move outside the network perimeter without triggering traditional alerts. In practice, this creates a visibility and control gap in outbound traffic flows.

Compliance exposure is tied to network boundaries

Regulatory requirements, such as data residency or data handling rules, depend on where data travels and how it is transmitted.

Shadow AI complicates this because data could be sent to services hosted in unknown or non-compliant regions. Network paths to these services are often undocumented or restricted; therefore, the organization has limited control over how much or how frequently data is transmitted.

Compliance risk emerges when traffic crosses geographic or trust boundaries without enforcement. It also increases when the network lacks segmentation or a policy controlling which systems can communicate externally. In other words, compliance is not just a policy issue -- it's a network enforcement problem.

Untrusted integrations and shadow APIs

Many AI tools integrate through APIs or OAuth, effectively linking internal systems to external services. This can result in the following:

• Persistent outbound connections to third-party platforms.
• New data exchange paths that bypass traditional application architectures.
• External services that gain indirect access to internal data flows.

If these integrations are not validated, they can increase attack surfaces through external endpoints, potential misuse of API connections or tokens, or continuous data transfer channels that operate outside standard monitoring

This transforms shadow AI into a source of uncontrolled network dependencies, where external systems become part of the data path without proper oversight.

Detecting and mitigating shadow AI

Organizations should start by strengthening visibility across networks and APIs to uncover unauthorized AI traffic and hidden system integrations. This is achieved through ongoing analysis of DNS, proxy and application logs to detect abnormal or unapproved AI-related activity.

To detect and mitigate shadow AI, network teams should prioritize the following best practices:

• Traffic visibility across DNS, proxy and flow logs.
• Monitoring outbound API activity.
• Behavioral detection of non-human traffic.
• Inspection of encrypted traffic where feasible.
• Zero-trust enforcement at the network edge.
• Egress filtering and segmentation.

User awareness is also essential. Employees often adopt AI tools to boost productivity without fully understanding the security risks involved. Continuous training and clear communication help shape safer behavior and ensure AI usage remains within approved organizational boundaries.

When a network lacks visibility, shadow AI could become an uncontrolled data pipeline operating in real time. Shadow AI isn't discovered in reports or audits; it could be embedded in the network's traffic, APIs and outbound connections. Network teams must take ownership, monitor continuously and enforce visibility across every layer of the infrastructure.

Verlaine Muhungu is a self-taught tech enthusiast, DevNet advocate and aspiring Cisco Press author, focused on network automation, penetration testing and secure coding practices. He was recognized as a Cisco top talent in sub-Saharan Africa during the 2016 NetRiders IT Skills Competition.