A close analysis of a company’s IT environment reveals that shadow AI is no longer a fringe problem, but is everywhere. Rogue AI tools are being used across enterprises, often driven by weak policies and the current AI hype cycle.
The risks are real. Companies are at risk of reputational damage, compliance exposure, and potential revenue loss from shadow AI. Organizations that cannot manage and formally leverage the use of AI will struggle to remain competitive.
This poses increasing challenges for both enterprises and network teams, especially given the increasing complexity of modern infrastructure. Shadow AI is difficult to detect without detailed visualization and inspection. This article describes how organizations can detect shadow AI and reduce its impact.
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. Similar to shadow IT, this uncontrolled use poses serious risks such as data breaches, regulatory violations, and security gaps, especially when sensitive information is shared with unverified third-party platforms.
Unmanaged BYOD accelerates the adoption of shadow AI throughout your organization. These risks often go undetected until a dedicated team implements detailed visibility and monitoring.
This importance is not theoretical. It is already physical. According to IBM’s July 2025 report, one-fifth of organizations have experienced an AI-related breach, but only 37% have established policies to govern the use of AI or detect shadow AI activity.
This gap highlights the critical revelation that sensitive data, including personally identifiable information, can be compromised at any time, putting both trust and a company’s reputation at risk.
5 clues that there is a shadow AI in your network
Shadow AI is an invisible battleground for many companies. Although everything seems to be running smoothly on the network, hidden tools and unauthorized processes often operate quietly in the background without a dedicated team actively detecting them.
Below, we discuss the main indicators that indicate there is shadow AI in your network.
1. Shift outbound traffic to AI-related services
A common early signal that there is shadow AI in your network is a change in the way outbound traffic is distributed. Examples of changes include:
- Increased frequency of connections to external AI service endpoints.
- The number of POST requests is high compared to typical browsing patterns.
- Send payloads larger than standard SaaS or web activities.
In some environments, traffic may also represent periodic submissions of structured data such as JSON, or repeated interactions with inference or API endpoints, rather than static content.
What to do: Check your proxy or firewall logs for outgoing JSON payloads that contain unusually large text or input fields.
2. API traffic from unverified endpoints
Because the AI platform is used primarily through APIs, its usage blends into regular application traffic. Indicators for unmanaged endpoints include:
- API calls initiated by a user’s workstation, a lab environment, or an unmanaged host.
- An authentication token was observed outside the expected system or network zone.
- Direct outbound API communication that bypasses central services or gateways.
Analyzing network behavior can reveal usage of APIs that don’t map to known internal applications, or new external endpoints that emerge without prior integration records. These patterns often indicate decentralized or fraudulent API usage, especially in development-heavy environments.
What to do: Monitor outbound traffic for API keys or tokens that aren’t mapped to your organization’s authorized enterprise accounts.
3. Consistent non-interactive traffic behavior
Automated processes, including AI agents, tend to generate traffic that lacks the variability of human activity. Observable patterns include:
- Requests that occur at stable, predictable intervals.
- Activities that continue beyond normal business hours.
- Request size or similar data structure that repeats over time.
However, these characteristics are not unique to AI. Monitoring systems, backups, and scheduled jobs can generate similar traffic. The difference lies in whether the behavior matches the documented expected workload.
What to do: Improve network visibility and identify sources of activity. When network teams identify malicious traffic, they must mitigate that activity and regularly monitor network traffic for regular checks.
4. Rapid increase in OAuth permissions for efficiency apps
Organizations operate deeply in digital environments, with countless tools shaping the way IT teams work every day. While integration streamlines collaboration and eliminates redundant work, it comes with a security tradeoff.
Employees frequently authorize third-party applications to connect to their corporate Google Workspace or Microsoft 365 accounts via OAuth, often to summarize meetings or manage email. Shadow AI often enters through third-party platforms that are integrated with enterprise systems. Examples include:
- Connections to previously unknown external domains.
- Permanent communication after the initial authentication or authorization flow.
- Data exchange between internal services and external platforms without clear ownership.
Over time, unmanaged third-party integrations can increase dependence on external endpoints that are not tracked in your architecture or asset inventory. These patterns should be evaluated against approved service catalogs and known integration points.
What to do: Monitor your identity provider’s logs to find unverified third-party apps that request unnecessary permissions, such as email 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 transfer include:
- An outbound encrypted session continued with a larger than normal amount of data.
- Repeated transfers of payloads of similar size.
- The ratio of sent data to received data is unbalanced.
Because the content is encrypted, analysis relies on the amount, frequency, duration, destination patterns, and endpoint classification of traffic metadata. These signals do not confirm data confidentiality, but may indicate unmonitored movement of data to external services.
What to do: Identify anomalous traffic using metadata. If there is malicious traffic, reduce it by restricting access to the network.
Risks associated with shadow AI
Shadow AI is often discussed primarily from a governance and compliance perspective. However, it is important to recognize the risks at the network layer, where the real risks occur. All interactions with external AI services (prompts, file uploads, API calls, etc.) rely on outbound connectivity. If that connection is not tightly controlled or fully visible, it is actively traversing the network.
Some of the challenges that networks that use shadow AI can encounter include:
Data breach causes outbound traffic to become uncontrollable
Data breaches and loss of confidentiality are an increasing risk in the era of pervasive AI tools. With easy access to powerful platforms, employees can unknowingly include sensitive data in their prompts, exposing sensitive information and putting them at risk of reputational damage through unintentional disclosure to public AI systems.
The problem isn’t just that the data is being shared, but that it’s being sent to external endpoints that your organization may not have approved. This allows data to be sent directly from the endpoint, bypassing application-level controls. It is then embedded in an encrypted session to limit 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 gap in visibility and control over outbound traffic flows.
Compliance risks are related to the network perimeter
Regulatory requirements, such as data location and data processing rules, vary depending on where the data moves and how it is transmitted.
Shadow AI complicates this by potentially sending data to services hosted in unknown or non-compliant regions. Network paths to these services are often undocumented or restricted. Therefore, organizations have limited control over the amount or frequency of data they send.
Compliance risks occur when traffic crosses geographic or trust boundaries without enforcement. It also increases when there is no segmentation in the network or policies controlling which systems can communicate with the outside world. In other words, compliance is not just a policy issue, but also a network enforcement issue.
Untrusted integrations and shadow APIs
Many AI tools integrate through APIs or OAuth, effectively linking internal systems to external services. This can have the following consequences:
- Persistent outbound connectivity to third-party platforms.
- A new data exchange path that bypasses traditional application architectures.
- External services that indirectly access internal data flows.
If these integrations are not validated, they can increase the attack surface through external endpoints, potential misuse of API connections or tokens, or continuous data transfer channels that operate outside of standard monitoring.
This turns shadow AI into a source of uncontrollable network dependencies, allowing external systems to become part of the data path without proper oversight.
Shadow AI detection and mitigation
Organizations should start by increasing visibility across their networks and APIs to uncover fraudulent AI traffic and hidden system integrations. This is achieved by continuously analyzing DNS, proxies, and application logs to detect anomalous or unauthorized AI-related activity.
To detect and mitigate Shadow AI, network teams should prioritize the following best practices:
- Traffic visibility across DNS, proxies, and flow logs.
- Monitor outbound API activity.
- Behavioral detection of non-human traffic.
- Inspect encrypted traffic when possible.
- Enforce Zero Trust at the network edge.
- Output filtering and segmentation.
User awareness is also essential. Employees often deploy AI tools to improve productivity without fully understanding the security risks involved. Ongoing training and clear communication can help shape safer behaviors and ensure the use of AI within approved organizational boundaries.
Without network visibility, shadow AI can become an uncontrollable data pipeline operating in real time. Shadow AI is not discovered in reports or audits. It can be embedded in network traffic, APIs, and outbound connections. Network teams need to take ownership, continuously monitor, and increase visibility across all tiers of the infrastructure.
Verlaine Muhungu is a self-taught technology enthusiast, DevNet advocate, and aspiring Cisco Press author with a focus on network automation, penetration testing, and secure coding practices. He was recognized as Cisco’s top talent in sub-Saharan Africa at the 2016 NetRiders IT Skills Competition.
