Get visibility into shadow AI
Discover every AI tool, agent, and MCP server used across your enterprise. And whether they are approved.
Continuously monitors
The problem is not just AI use.
It is lack of visibility.
avg. U.S. data breach in 2025
AI-related security incidents are expensive.
IBM found that organizations with weak AI governance are much more exposed. 97% of organizations that reported an AI-related security incident lacked proper AI access controls.Security and IT teams cannot manually track:
- Every AI chat tool employees open
- Every AI desktop app installed
- Every AI IDE or coding assistant in use
- Every MCP server connected
- Every AI repo cloned or used
- Every internal or external AI agent deployed by teams
That creates real enterprise risk:
- Sensitive data sent to unapproved AI tools
- AI usage outside SSO and audit controls
- No clear inventory of approved vs unapproved tools
- No easy way to govern adoption
- No clear view of who is using what
- No proof for audits or internal reviews
You cannot govern the AI you cannot see.
Shadow AI is already inside your organization
Employees are already using AI at work
Many use personal or unapproved accounts
New AI tools appear faster than security teams can review them
AI usage now spans browsers, desktop apps, IDEs, agents, repos, and MCP servers
of knowledge workers use AI at work
of AI users bring their own AI tools to work
of workplace GenAI access happens through non-corporate accounts (LayerX)
distinct AI applications used by the average company (Harmonic, Q1 2025)
Fireraven AI Discovery
Runs on prem on employee computers to discover AI tools, AI agents, AI platforms, AI repos, MCP servers, and AI-enabled developer workflows across the enterprise.
Discover AI tools in use
- Detect browser-based AI tools
- Detect desktop AI apps
- Detect AI IDEs and coding assistants
- Detect AI repos and SDK usage
- Detect MCP servers and agent activity
- Detect new AI tools as they appear
See who used what
- User
- Device
- Team or business unit
- First seen
- Last seen
- Usage history & source
Separate approved from unapproved
- Mark tools as allowed, restricted, or forbidden
- Define rules by team, device, environment, or risk
- Alert when restricted tools are used
- Build a governed inventory of AI tools and agents
Build an audit-ready AI inventory
- Sanctioned vs unsanctioned AI usage
- Approved vs restricted tools
- History of detections and violations
- Inventory of AI tools, agents, and MCP servers
- Evidence for governance and audit prep
Get visibility into developer AI use
- Cursor, Claude Code, GitHub Copilot
- OpenAI Codex, Replit, Roo Code
- AI IDE workflows
- AI repos and agent tooling
See the analytics enterprises actually want
- Most used AI tools
- Usage by department
- Sanctioned vs unsanctioned usage
- Users of restricted tools
- New tools detected over time
- Policy violation trends
Use cases
Where Fireraven AI Discovery fits in your stack.
Shadow AI discovery
- Find unapproved AI tools across employee devices
- Detect AI usage outside approved workflows
- Uncover hidden AI sprawl across the organization
AI governance rollout
- Create an approved AI tool list
- Define what is allowed and what is not
- Maintain an audit-ready inventory of AI tools and agents
Developer AI visibility
- Monitor AI coding tools and AI IDE usage
- Track repo-based AI activity
- Understand where AI development tooling spreads
Agent and MCP visibility
- Identify agent-related tooling
- Detect MCP server usage
- Understand where agent ecosystems are appearing
Frequently asked questions
Shadow AI is the use of AI tools, models, agents, or platforms without formal IT or security approval or oversight. That includes chat tools, coding assistants, AI plugins, agents, and MCP servers.
Because teams can use AI outside approved controls, outside SSO, and outside audit visibility. That increases the risk of data exposure, compliance issues, and unmanaged AI sprawl.
They need continuous visibility across employee devices, browser activity, desktop apps, AI IDEs, coding tools, AI repos, and MCP servers. Manual tracking is not enough at enterprise scale. This is the gap Fireraven AI Discovery is built to solve.
Fireraven AI Discovery helps admins identify which AI tools are in use, who is using them, where they are running, and whether they are approved.
You need a product that continuously discovers AI tools, classifies them, records usage, and separates sanctioned from unsanctioned tools. Fireraven AI Discovery gives you that inventory.
You define which tools are allowed, restricted, or forbidden, then monitor usage and alert when policy is violated. Fireraven AI Discovery supports that workflow.
Yes. Fireraven AI Discovery is designed to give visibility into developer AI tools, AI IDE workflows, AI repos, and related agent tooling.
Yes. Fireraven AI Discovery helps identify MCP-related usage and gives visibility into where MCP servers and related agent ecosystems are appearing.
Because employees adopt new tools quickly, often through personal accounts or new workflows. Microsoft notes that the rapid growth of AI apps makes fixed allowlists and blocklists hard to maintain effectively.
Most used AI tools, sanctioned vs unsanctioned usage, usage by department, new tools detected, restricted tool usage, adoption trends over time, first seen / last seen, agent and MCP visibility, and users / teams with the highest AI exposure.
See every AI tool used across your enterprise
Discover shadow AI. Build a governed inventory. Know what is approved, what is not, and where action is needed.