Securing Shadow AI: How to Detect and Govern Unsanctioned AI Tools

Nearly 90% of organizations use AI in at least one business function as the number of employees that regularly use AI on their corporate devices has increased 3x year over year. But just 22% of individuals rely exclusively on tools provided by their employers – that gap between adoption and oversight is where shadow AI lives.  

As AI usage outpaces governance, nearly two-thirds of organizations don’t have the necessary policies to manage or detect shadow AI. The challenge for security leaders is twofold: uncovering unsanctioned AI usage in the first place, then implementing controls to effectively secure AI.  

We’ll provide a comprehensive overview of shadow AI, how it’s contributing to an evolving threat landscape, and what security leaders can do now to detect and govern AI across the environment.  

What Is Shadow AI?  

Shadow AI refers to the use of any AI tool or application without formal IT and security oversight or approval.  

Examples of shadow AI can include everything from unsanctioned generative AI platforms accessed on corporate devices and AI agents deployed by developers without security visibility to autonomous workflows embedded in third-party software and LLM integrations that live inside tools organizations already trust.  

Cyber pros aren’t strangers to the challenge of securing unseen and unsanctioned tech after years of contending with shadow IT, but the AI era has added urgency to this fundamentally familiar threat.  

AI Security Risks: Expanding Attack Surfaces, Lateral Movement, and Compliance Gaps 

Cyber risks related to AI vulnerabilities rank as organizations’ fastest growing concern, but most security teams don’t have the necessary capabilities to effectively control AI – traditional tools were designed for a different threat model. 

For example, application-layer controls see managed endpoints and known SaaS traffic routed through a proxy. They were built to govern what human users access through browsers and approved applications, not to see what AI agents access through APIs, what machine identities are doing across cloud workloads, or how AI capabilities embedded in sanctioned tools are behaving on the network. That enforcement blind spot is what makes shadow AI so risky, leaving security teams to manage:  

• Access path proliferation: Every unsanctioned AI tool creates new connections to corporate data, external APIs, and internal systems that are often undocumented, over-permissioned, and persistent. AI agents compound the problem as they accumulate permissions over time through policy drift, tool chaining, and expanding task scope, frequently without security team visibility.  
• Lateral movement exposure: Shadow AI tools and agents typically operate with implicit trust inside the network, contributing to a rapidly expanding AI attack surface. In environments where a single compromised host can reach 85% of internal systems in the first hop, over-privileged and under-monitored AI creates a new lateral movement highway adversaries can exploit without triggering alarms. 
• Cyber compliance gaps: Organizations subject to frameworks like NIS2, DORA, or CIS Benchmarks cannot demonstrate policy adherence for connections they don't know exist. In other words, shadow AI creates operational and regulatory exposure that only compounds over time as AI usage scales. 
• The collective result? Security incidents linked to AI usage are skyrocketing, with 57% of organizations already reporting an uptick – and the impact is quantifiable. The average cost of a data breach is $670,000 higher for organizations with high levels of shadow AI, and it takes 247 days to identify and contain a breach involving shadow AI.  

To effectively address AI security risks before they spiral into disruptive breaches, teams need both comprehensive visibility and network-layer control. 

How to Detect Shadow AI: Real-Time Network Visibility  

Most detection tools are calibrated to catch anomalies that shadow AI doesn’t produce. When an employee accessing an unsanctioned AI service looks like ordinary web traffic, organizations can’t rely on alerts and dashboards built for a different threat landscape to uncover shadow AI.  

Instead, security teams need continuous, live visibility into every asset and identity on the network, delivering the granular clarity to answer three key questions:  

1. Which SaaS AI destinations are users and devices reaching? If an organization has standardized on Copilot, nothing on the network should be reaching ChatGPT, Gemini, or other unapproved services. Real-time traffic visibility surfaces misalignment between AI policies and behavior, making enforcement possible the moment an unauthorized connection appears, not after a log review. 
2. Which AI agents are running and what are they connecting to? In many environments, AI agents are deployed by developers and business units without security team visibility, meaning there's no reliable inventory of what's running, what it's authenticating to, or what it can reach. Real-time visibility into asset-to-asset and identity-to-asset communication allows teams to identify and govern AI agents before they become a liability.  
3. Where does AI access exceed operational need? Even sanctioned AI can lead to hidden vulnerabilities – for example, an agent might be deployed for a narrow task but connecting to a CRM, a production database, and a set of external APIs. When the gap between operational need and real-world connectivity remains invisible, it can’t be mitigated. 

Visibility into every AI tool and agent across the environment is a critical starting point, but it must feed directly into control.  

Securing AI: 4-Step Framework for Visibility and Control 

Security teams understand the urgent need to detect shadow AI, but seeing the full scope of exposure is only valuable when that visibility drives enforcement. Rather than prioritizing shadow AI detection in a vacuum, leverage this four-step approach for translating real-time network visibility into enforceable controls that govern every AI tool, agent, and integration in the environment. 

1. Maintain a Real-Time Inventory of AI in Your Environment   

Start by implementing continuous network monitoring that maps every AI-related identity, tool, and connection in the environment through observed traffic rather than relying on declared inventories or deployment logs. This end-to-end visibility forces shadow AI into the spotlight, enabling security policies tailored to network realities rather than best guesses or point-in-time snapshots.  

2. Block Unsanctioned AI by Default  

Define an approved list of cloud AI services and automatically block any connection to a SaaS AI destination outside of that list. With network-layer enforcement, security teams can ensure policies are applied universally across users, devices, and workloads regardless of how they're connecting. 

3. Govern AI Agents with Least Privilege Policies 

Every AI agent running in your environment is a process with an identity – like any other identity, agents should only be able to reach what’s explicitly necessary. Apply identity-based access controls to every AI agent in the environment with permissions tightly scoped to operational need.  

4. Automate AI Security Policy Lifecycles  

As the network changes, security gaps can emerge if AI governance relies on manual upkeep. Automated policy lifecycle management – powered by a deterministic, human-on-the-loop engine – adapts controls as the environment evolves, enforcing access deterministically and continuously without creating long-term operational debt.  

Identify, Segment, and Contain AI with Zero Networks  

With Zero Networks, security teams can easily uncover shadow AI and gain true control over the AI running in their environments. Zero’s AI Segmentation capabilities deliver complete AI visibility, deterministic enforcement, and built-in control to transform AI from a risk into a governed, enforceable part of the environment:  

• SaaS AI Control governs which cloud AI services users and devices can access, automatically blocking every unapproved destination. 
• AI Agent Control applies the same identity-based least-privilege controls governing every user and device to every agent in the environment. 
• AI Lateral Movement Control eliminates the open network that compromised AI tools and agents would otherwise move through by enforcing granular least privilege access controls everywhere.  
• LLM Protection segments the model infrastructure at the network layer so only authorized systems can reach it. 

Learn how you can detect shadow AI and scale network-layer security policy enforcement to achieve true control with Zero Networks – request a demo.