What is Shadow AI?
In cybersecurity and business, shadow AI refers to the unauthorized use of unapproved AI tools by employees within a company. Shadow AI happens when employees use unsanctioned artificial intelligence apps (like personal ChatGPT or Midjourney accounts) to perform work without the IT department's knowledge or approval. This presents major security and compliance risks, as sensitive corporate data is frequently entered into external AI platforms that may use it for model training.
The core risk with Shadow AI is that it operates outside of the organization's approved security and compliance frameworks. As a result, sensitive data might be processed or stored in external systems without adequate protection. This can lead to regulatory violations, data leaks, or the unintentional exposure of proprietary information. Organizations must recognize that Shadow AI represents a significant security and compliance blind spot.
This is part of a series of articles about AI security.
In this article:
Easy Access to Generative AI Tools
Generative AI tools, such as large language models and image generators, are widely available through public platforms and APIs. Employees can access these tools with minimal technical expertise, often requiring just a web browser or a simple sign-up process. This broad availability removes barriers that once restricted advanced analytics and automation to specialized teams or approved vendors.
The convenience and low entry cost of generative AI tools encourage employees to experiment and integrate them into their workflows without consulting IT. In many cases, these tools promise immediate productivity gains, making them attractive to users under pressure to deliver results quickly. The result is a rapid increase in unsanctioned AI adoption that often escapes organizational oversight.
Pressure to Improve Productivity
Today's business environment is characterized by intense competition and constant demands for efficiency. Employees and managers face pressure to find new ways to simplify processes, reduce manual effort, and deliver outcomes faster. AI tools, especially those that automate content creation, analysis, or decision-making, offer shortcuts to meet these expectations.
This pressure often leads employees to bypass formal approval processes, especially when internal IT teams are slow to evaluate or roll out new solutions. The desire to keep up with industry peers or outperform competitors can drive workers to adopt AI tools independently, even if doing so introduces risks or conflicts with company policy. Shadow AI is fueled by a broader culture of innovation and urgency.
Employees Wanting to Cut Individual Efforts and/or Improve Own Wellness
Many employees adopt AI tools to reduce repetitive work, shorten long tasks, and lower daily workload. Generating first drafts, summarizing meetings, writing code, or analyzing data can save significant time. These efficiency gains allow employees to focus on higher-value work or simply reduce the stress associated with heavy workloads and tight deadlines.
Personal motivation can also contribute to Shadow AI adoption. Employees may use AI to improve work-life balance, avoid overtime, or manage increasing responsibilities without requesting additional resources. When approved AI tools are unavailable or lack needed capabilities, workers often turn to public AI services on their own, even if doing so violates organizational policies or introduces security and compliance risks.
Slow Internal AI Approval Processes
In many organizations, the process to evaluate, approve, and deploy new AI tools is slow and bureaucratic. Security reviews, legal assessments, and integration planning can take weeks or months. These delays frustrate employees who see immediate value in new AI technologies and do not want to wait for approval.
As a result, teams may bypass official channels and implement AI solutions themselves. This approach may solve short-term productivity issues but creates long-term risks related to data privacy, security, and compliance. The gap between the pace of technological change and internal governance processes drives the spread of Shadow AI.
Shadow AI is a subset of shadow IT, but there are important differences between the two:
- Shadow IT refers to any technology, hardware, software, or services, used within an organization without IT department approval.
- Shadow AI focuses on AI-related tools, such as generative models, chatbots, and automated decision-making systems adopted without oversight.
While shadow IT has long been a challenge, the risks associated with Shadow AI are often more severe. AI tools can process sensitive data, generate business-critical content, and influence decision-making at scale. The potential impact of unsanctioned AI use includes data breaches, compliance violations, and reputational damage, making it critical for organizations to address Shadow AI as a distinct issue.
Dror Zelber
Dror Zelber is a 30-year veteran of the high-tech industry. His primary focus is on security, networking and mobility solutions. He holds a bachelor's degree in computer science and an MBA with a major in marketing.
Tips from the expert:
In my experience, here are tips that can help you better manage and reduce the risks of Shadow AI:
1. Treat AI providers like third-party vendors: Don't evaluate AI tools only from a functionality perspective. Review where prompts are stored, whether data is used for model training, subprocessors involved, geographic data residency, retention periods, and whether "zero data retention" or enterprise privacy guarantees actually apply to your use case.
2. Create data-aware AI guardrails instead of blanket bans: Organizations that prohibit all public AI often drive Shadow AI further underground. Instead, define which data classifications (e.g., Public, Internal, Confidential, Restricted) are permitted in AI prompts, and enforce the policy with technical controls rather than relying solely on user awareness.
3. Monitor outbound AI traffic by prompt volume, not just destinations: Employees frequently access AI through browser extensions, plugins, embedded SaaS features, and APIs that don't obviously appear as "AI." Look for unusual increases in outbound text, document uploads, or API calls to detect hidden AI adoption.
4. Secure AI browser extensions separately: Browser extensions often receive permissions to read every page users visit, including corporate applications. Regularly audit installed extensions because they can capture sensitive content before DLP, CASB, or network controls ever inspect it.
5. Use honeytokens to detect unauthorized AI usage: Place fake API keys, documents, or credentials in controlled locations. If these appear in AI-generated responses, external repositories, or monitoring systems, you gain early evidence that sensitive information has left approved environments.
1. Sensitive Data Leakage Through Prompts and File Uploads
When employees use public AI tools for work, they may input sensitive company information as prompts or upload confidential files for processing. These actions can transmit proprietary data to external servers controlled by third-party providers, often without adequate safeguards or data deletion guarantees. The risk increases because many generative AI platforms retain user input to improve their models.
Impact:
Organizations may have no visibility into what data is being shared or how it is stored, creating compliance and privacy risks. In regulated industries, such leaks can result in violations of laws like GDPR or HIPAA. Even outside regulatory frameworks, the exposure of sensitive information can undermine trust, damage customer relationships, and lead to financial consequences.
Related content: Learn how attackers weaponize model inputs in our article on prompt injection.
2. Exposure of Intellectual Property and Source Code
Developers and engineers sometimes use AI coding assistants or public large language models to generate, review, or debug source code. If proprietary code or design documents are submitted to these tools, intellectual property could be stored, reused, or disclosed by the AI provider. This risk is not always obvious to users, especially when terms of service or data retention practices are unclear.
Impact:
The exposure of source code and other intellectual property can result in loss of competitive advantage or legal disputes over ownership and originality. Organizations must educate employees about the risks of sharing sensitive assets with external AI tools and implement technical controls to prevent unauthorized data transfer.
3. Account Takeover and Credential Theft
Many AI platforms require users to create accounts, which may be linked to corporate email addresses or single sign-on (SSO) systems. If employees use weak or reused passwords, or if the AI service is compromised, attackers could gain access to these accounts. Phishing attacks targeting users of AI tools are increasing, with malicious actors creating fake login pages or distributing trojanized AI applications.
Impact:
Account takeover can lead to unauthorized access to sensitive data, manipulation of AI-generated outputs, or lateral movement into other corporate systems if credentials are reused. Organizations need strong identity and access controls and user education to reduce the risks of credential reuse and phishing related to Shadow AI adoption.
4. Malicious Bot and Automated Abuse
Shadow AI can also be used to automate attacks or abuse web applications. Employees or attackers may deploy unsanctioned AI bots to scrape data, brute-force authentication, or probe for vulnerabilities without the knowledge of IT or security teams. These activities can generate automated traffic, potentially overwhelming systems or exposing weaknesses in application security.
Impact:
Public AI platforms may also be exploited by attackers to launch large-scale campaigns, such as generating phishing emails or automating social engineering. The combination of Shadow AI and malicious automation creates a complex threat landscape that requires monitoring and defense.
Employees Using Public AI Chatbots for Work
One common form of Shadow AI involves employees using public AI chatbots, such as ChatGPT or Gemini, to draft emails, summarize documents, or brainstorm ideas. While these tools offer productivity benefits, employees may not understand the risks of sharing sensitive business information with third-party platforms. Data submitted to these chatbots can be stored and analyzed by the provider.
Organizations often lack visibility into how frequently these tools are used or what data is being shared. This creates blind spots in data governance and increases the risk of accidental information leakage.
Example:
A marketing employee uploads an unreleased product plan to a public chatbot and asks it to create campaign copy. The document contains pricing details and launch dates, which are transmitted to an external service without approval.
Developers Using Unapproved AI Coding Assistants
Developers often use AI coding assistants, like GitHub Copilot or Tabnine, to speed up programming tasks and reduce repetitive work. When used without approval, these tools may process proprietary source code, design documents, or configuration files. This can lead to unintended sharing of intellectual property with external vendors.
The use of unapproved AI coding assistants also bypasses internal security reviews that assess data handling, privacy, and compliance risks. As a result, organizations may lose control over development assets or face intellectual property and licensing issues. Proper vetting and monitoring are necessary to reduce these risks.
Example:
A developer pastes a proprietary authentication module into an unapproved coding assistant to debug an error. The code includes internal API endpoints and security logic that the organization did not authorize for external processing.
Teams Deploying AI Agents Without Security Review
Some teams deploy AI-powered agents or automation tools to handle customer inquiries, process transactions, or perform other business functions. When implemented without a security review, they may introduce vulnerabilities, mishandle sensitive data, or operate with excessive permissions. Without oversight, it is difficult to ensure compliance with company policies or industry regulations.
The lack of a security review means risks, such as data leakage, privilege escalation, or integration flaws, may go undetected until a breach occurs. Organizations should establish processes for evaluating and approving AI agents to align deployments with security best practices and governance requirements.
Example:
A customer support team deploys an AI agent that can issue refunds and update customer records. Because the agent was not reviewed, it receives broader permissions than necessary and approves transactions outside the team's policy limits.
Connecting AI Tools to Corporate Data Sources
Another example of Shadow AI occurs when employees or teams connect third-party AI tools directly to corporate data sources, such as databases, CRMs, or document repositories. This integration can provide insights and automation capabilities but may expose sensitive data to unvetted external services. Without proper security controls, these connections can serve as entry points for data exfiltration or unauthorized access.
Such integrations often bypass IT monitoring and data loss prevention (DLP) tools, making it difficult to detect or respond to suspicious activity. The risk increases when API keys or credentials are shared insecurely or stored in plain text. Organizations need strict control over data access and must ensure that only approved AI tools connect to critical business systems.
Example:
A sales team connects a third-party AI assistant to the company CRM using a shared API key. The tool can access all customer records, including contacts outside the team's region, and the connection is not monitored by IT.
Here are some of the ways that organizations can improve their security in the face of Shadow AI.
1. Gain Visibility Into AI Applications and APIs
Organizations cannot manage Shadow AI if they do not know where it exists. The first step is to identify AI applications, browser-based tools, APIs, and AI-enabled SaaS services accessed across the environment. Network monitoring, secure web gateways, CASBs, and endpoint telemetry can help uncover unauthorized AI usage and reveal which users and teams rely on these services.
Visibility should extend beyond applications to the data flowing into them. Organizations should monitor prompt submissions, file uploads, and API calls involving sensitive information. This allows security teams to assess risk, prioritize remediation, and decide which AI tools should be approved, restricted, or blocked.
Key actions:
- Monitor network, browser, endpoint, and API activity for AI service usage.
- Identify users, departments, and workflows relying on unapproved tools.
- Inspect prompts, uploads, and API calls for sensitive data exposure.
- Classify discovered services as approved, restricted, or blocked.
- Review AI usage reports regularly to detect new tools.
2. Inventory All AI-Connected Assets
Maintain an up-to-date inventory of all systems that use or integrate with AI services. This includes internally developed AI applications, third-party AI platforms, browser extensions, automation tools, AI agents, and integrations with corporate data sources. A centralized inventory provides a clear picture of where AI is used and which business processes depend on it.
Each asset should be classified according to its purpose, data access, ownership, and risk level. Regular reviews help identify outdated integrations, unused API keys, and unauthorized deployments that increase the organization's attack surface. An accurate inventory also supports compliance audits and incident response.
Key actions:
- Catalog AI applications, agents, browser extensions, APIs, and integrations.
- Record each asset's owner, purpose, data access, and deployment location.
- Assign a risk rating based on permissions and data sensitivity.
- Track API keys, service accounts, and connected data sources.
- Remove unused integrations and revoke outdated credentials.
3. Protect Web Applications and APIs Consistently
Many AI services rely on web applications and APIs to exchange data, making these interfaces attractive targets for attackers. Organizations should apply consistent security controls across traditional applications and AI-enabled services, including authentication, input validation, encryption, and rate limiting.
Web application firewalls (WAFs) and API security solutions can help detect malicious requests, block exploitation attempts, and prevent unauthorized access. Continuous monitoring ensures that new AI-enabled endpoints do not become unprotected entry points into the organization's environment.
Key actions:
- Apply authentication, encryption, input validation, and rate limiting.
- Place AI-enabled endpoints behind WAF and API security controls.
- Test APIs for common vulnerabilities and authorization flaws.
- Monitor requests for abuse, injection attempts, and unusual traffic.
- Include new AI endpoints in existing security testing processes.
4. Enforce Strong Identity and Access Controls
Access to AI tools and services should follow the principle of least privilege. Users, applications, and AI agents should receive only the permissions required to perform their tasks. Access should be reviewed regularly to remove unnecessary privileges.
Organizations should require multi-factor authentication (MFA), implement single sign-on (SSO) where appropriate, and enforce strong password policies. Monitoring privileged accounts and service credentials helps prevent unauthorized access to AI platforms and reduces the risk of credential theft or account takeover.
Key actions:
- Require MFA and SSO for approved AI platforms.
- Assign users and agents only the permissions they need.
- Review privileged access and service accounts regularly.
- Rotate API keys and store credentials in approved secrets managers.
- Revoke access when users change roles or tools are retired.
5. Monitor Automated and AI-Generated Traffic
AI-powered applications and agents often generate automated traffic that differs from normal user activity. Organizations should monitor this traffic to distinguish legitimate automation from malicious bots, credential stuffing, scraping, or other abuse. Behavioral analytics and anomaly detection can help identify suspicious patterns.
Monitoring should include both inbound and outbound activity. This helps detect compromised AI agents, unauthorized automation, or excessive data transfers that could indicate data exfiltration.
Key actions:
- Establish baselines for expected agent and automation behavior.
- Detect unusual request volumes, access patterns, and data transfers.
- Separate legitimate automation from scraping, credential abuse, and bots.
- Monitor both inbound requests and outbound connections.
- Alert security teams when agents deviate from approved behavior.
6. Protect AI Services Against DDoS Attacks
AI services often depend on APIs and compute-intensive workloads that can be disrupted by distributed denial-of-service (DDoS) attacks. Attackers may overwhelm AI endpoints with large volumes of traffic, preventing legitimate users from accessing business functions. Organizations should deploy DDoS protection for public-facing applications and the APIs that support AI workloads.
Effective protection combines traffic filtering, rate limiting, load balancing, and automated mitigation capabilities. Regular testing of DDoS response procedures ensures that AI-powered services remain available during attacks and reduces operational disruption.
Key actions:
- Deploy DDoS protection for public AI applications and APIs.
- Apply rate limits and request quotas to expensive AI endpoints.
- Use load balancing and autoscaling to absorb traffic spikes.
- Block malicious traffic through automated filtering and mitigation.
- Test response plans and failover procedures regularly.
As employees and teams adopt AI on their own, much of that unsanctioned activity now takes the form of autonomous agents that act on data and systems without oversight. Radware Agentic AI Protection is built to harness the full power of AI agents while protecting against their risks. Because attackers can manipulate agents to access information, leak data, and create legal and brand exposure, Radware monitors and blocks agent-targeted manipulation and attacks without affecting the agentic AI experience, automatically identifying intent and responding in real time. By mapping, monitoring, and integrating with all types of AI agents, it helps organizations stay productive, compliant, and secure even as AI adoption spreads beyond IT's line of sight.
Key capabilities of Radware Agentic AI Protection:
- Full agent discovery and mapping: Continuously discovers every type of agent and tool across environments and maps their connections and dependencies, giving teams visibility into the entire agent ecosystem, its interactions, and rich metadata on agent configuration and usage.
- Intent-based behavioral protection: Maintains runtime monitoring of agent actions and intents to detect and mitigate malicious activity, applying guardrails that respond in real time to threats such as indirect prompt injection, jailbreaking, and supply chain attacks—independently rated highly effective, blocking up to 95.7% of indirect prompt injection attacks.
- LLM guards and MCP tool control: Builds guardrails that validate prompts and block prompt injection, jailbreaks, and unsafe outputs, and allows or blocks tools per agent to stop agents from taking goal-divergent actions.
- Continuous AI security posture management: Identifies and scores risks across agents and tools throughout the agent lifecycle and across SaaS, homegrown, and end-user devices, using a full execution Risk Graph and an Interactive Connection Map to trace risk to its source.
- Protection for developer-hosted agents: Monitors, detects, and secures AI agents on the client side, including Claude Code agents running on developer endpoints.
- Audit-ready compliance reporting: Generates posture management reports that demonstrate compliance with global standard requirements.
To see how Radware helps you safely adopt AI agents while keeping sensitive data and critical systems under control, learn more about Radware Agentic AI Protection.