Employees Are Deploying AI Faster Than Security Teams Can See It
For years, organizations have battled the challenge of Shadow IT: employees adopting unsanctioned applications and cloud services without IT approval.
Today, a far more significant challenge is emerging - Shadow AI.
Across every department, employees are embracing AI-powered tools to improve productivity, automate repetitive tasks, accelerate decision-making, and streamline workflows. Business units are deploying custom AI assistants. Developers are experimenting with autonomous agents. Employees are using public LLMs such as ChatGPT, Gemini, Claude, and other AI services to help them perform their daily work.
Most of this innovation is happening with good intentions.
Unfortunately, much of it is happening without the knowledge of IT, security, governance, or compliance teams.
As a result, many organizations struggle to answer fundamental questions:
- Which AI tools are currently being used across the organization?
- Which AI agents have been formally approved?
- Which employees or departments have created their own agents?
- Which enterprise systems, applications, and data sources are connected to these agents?
- Which external AI providers are employees using?
- Are these deployments compliant with organizational policies and emerging AI regulations?
Without answers to these questions, organizations cannot effectively understand or manage their AI risk posture.
Unlike traditional software, AI agents can process sensitive information, interact with enterprise systems, invoke tools, access databases, and autonomously perform tasks. This creates an entirely new category of risk that extends well beyond traditional Shadow IT concerns.
The first challenge of securing AI is visibility.
You cannot secure what you cannot see.
Four Shadow AI Scenarios Every CISO Should Understand
Most Shadow AI does not begin with malicious intent.
It begins with employees trying to become more productive.
The problem is that seemingly harmless AI adoption can rapidly create security, governance, and compliance challenges.
Scenario 1: The Employee Who Uploads Sensitive Data to a Public LLM
A salesperson uses ChatGPT to help create a customer proposal.
To receive better recommendations, they upload:
- Customer requirements
- Pricing information
- Competitive intelligence
- Internal strategy documents
- Contract language
The employee is simply trying to complete their work faster.
However, security teams may have no visibility into what information was shared, whether the AI provider is approved for that use case, or whether corporate data handling policies were violated.
What began as a productivity exercise may have become a data governance and compliance issue.
Scenario 2: The Department That Builds Its Own AI Agent
The marketing team creates a campaign-generation assistant.
HR deploys an AI assistant to answer employee questions.
Finance builds an agent to summarize reports.
Each deployment appears harmless when viewed independently.
However, security teams may suddenly find themselves managing dozens of AI agents spread across multiple business units, each connecting to different applications, datasets, and users.
Many of these deployments never undergo formal security, risk, or compliance reviews.
Scenario 3: The Agent That Receives Excessive Permissions
A developer creates an AI agent to automate routine business processes.
To simplify implementation, the agent receives access to:
- CRM systems
- Internal knowledge bases
- Email platforms
- Customer databases
- Internal APIs
- Collaboration tools
The agent performs its intended tasks successfully.
But excessive permissions dramatically expand potential risk exposure if the agent is misconfigured, manipulated, or behaves in unexpected ways.
In many cases, security teams may not even be aware that the agent exists.
Scenario 4: The Unauthorized Connection to External Tools and MCP Servers
A business unit deploys a new AI agent and connects it to third-party tools, APIs, databases, SaaS services, and MCP servers.
The goal is increased functionality and faster business outcomes.
The problem is that nobody performs a security review.
The organization now has little visibility into:
- Which external services are connected
- What data is exchanged
- Who approved the integrations
- Whether adequate security controls exist
- Whether the external services can be trusted
Every new integration expands the organization's AI attack surface and increases the potential pathways to sensitive enterprise resources.
How Organizations Should Address Shadow AI
Many organizations initially respond to Shadow AI by attempting to prevent or restrict AI adoption altogether. This approach rarely succeeds.
Employees will continue using AI because the business value is simply too compelling.
The objective should not be to stop AI adoption. The objective should be to enable AI innovation securely.
Organizations should focus on four foundational capabilities.
1. Continuous Discovery
Organizations must continuously identify:
- Public AI services
- Enterprise AI applications
- AI-enabled SaaS platforms
- AI agents
- MCP servers
- Developer-created assistants
- Browser-based AI tools
- Endpoint-based AI applications
Discovery cannot solely depend on voluntary disclosure.
Security teams require automated mechanisms that continuously identify AI activity wherever it appears.
2. Comprehensive Visibility
Discovery alone is not enough.
Organizations must understand:
- Who is using AI
- Which agents exist
- What systems they access
- What data they can reach
- Which tools they invoke
- Which external services they interact with
Visibility creates the foundation for governance and risk management.
3. Governance and Compliance
Organizations need formal AI governance programs that define:
- Approved AI providers
- Approved use cases
- Data handling requirements
- Risk management procedures
- Security controls
- Compliance requirements
As regulations such as the EU AI Act, ISO 42001, NIST AI RMF, GDPR, HIPAA and other industry-specific frameworks mature, organizations will increasingly be required to demonstrate governance over their AI ecosystems.
That is extremely difficult when large portions of AI activity remain unknown.
4. Runtime Security and Protection
Discovery and governance are only the beginning.
Organizations must also actively protect against:
- Prompt injection attacks
- Agent manipulation
- Excessive permissions abuse
- Data leakage attempts
- Unauthorized actions
- Malicious tool invocation
- Autonomous decision-making risks
This requires real-time visibility into agent behavior and the ability to detect and stop risky actions before they impact the business.
From Shadow AI Discovery to Runtime Protection: The Radware Approach
At Radware, we believe organizations need a complete lifecycle approach to Agentic AI security. Discovery alone is not enough.
Organizations must be able to discover, understand, govern, secure, and continuously monitor their AI ecosystems.
The Radware Agentic AI Protection was designed specifically to address these challenges.
Discover
Organizations cannot govern what they cannot see.
The Radware solution helps organizations identify sanctioned and unsanctioned AI deployments through integrations with identity and endpoint security ecosystems, providing visibility into AI agents, AI applications, users, and emerging Shadow AI activity across the enterprise.
Map
Discovery is only the beginning.
Radware helps organizations understand how AI operates by mapping relationships between:
- Users
- Agents
- MCP servers
- APIs
- Enterprise applications
- Data repositories
- External services
- Connected tools
This provides a comprehensive view of the organization's AI ecosystem and associated risk exposure.
Govern
Effective governance requires centralized visibility and control.
Radware enables organizations to identify high-risk AI deployments, monitor AI posture, establish governance policies, and better understand where AI-related risks exist throughout the enterprise.
Comply
Organizations face increasing pressure to demonstrate compliance with emerging AI regulations, standards, and governance frameworks.
Radware helps organizations document AI usage, assess risk posture, maintain visibility, and support compliance reporting initiatives.
Protect
The final and most critical step is protection. Visibility and governance help organizations understand risk. Protection helps them reduce it.
Radware’s Agentic AI Protection extends security beyond the standard LLM guardrails into runtime operations through intent-aware, behavioral security mechanisms designed specifically for agentic environments.
By continuously analyzing agent behavior and validating actions against intended objectives, organizations can identify and stop potentially harmful activities before they result in business impact.
Shadow AI Is Already Inside Your Organization
The question is no longer whether employees are using AI. They are!
The question is whether your organization knows where AI is being used, which agents exist, what data they can access, and what risks they introduce.
Shadow AI is rapidly becoming one of the largest blind spots facing modern enterprises.
Organizations that succeed in the agentic era will not be the ones that attempt to block AI adoption. They will be the organizations that can confidently discover, map, govern, comply, and protect their expanding AI ecosystems.
Because in Agentic AI security, the first challenge is visibility.
And visibility is where the journey begins.
Conclusion
Shadow AI is no longer a future concern. It is already present in most organizations, often operating beyond the visibility of IT and security teams.
As employees, departments, and developers increasingly adopt AI tools and agents on their own, organizations must establish the visibility, governance, compliance, and security controls required to manage this rapidly expanding ecosystem.
The first step is discovering what AI exists. The next is ensuring it operates securely.
Organizations that can successfully discover, govern, comply, and protect their AI environments will be best positioned to capture the benefits of Agentic AI while minimizing the associated risks.
To learn more about how Radware helps organizations discover and secure Shadow AI, read the Radware Agentic AI Protection Solution Brief: Download the Radware Agentic AI Protection Solution Brief
Artificial Intelligence is transforming the enterprise. The organizations that thrive will not be those that simply adopt AI faster, but those that can do so securely, responsibly, and with complete visibility into their AI ecosystem.