AI Management

BigFix Inventory includes a ready-to-use dashboard that delivers a single view of AI solutions across your fleet, local AI model files and MCP servers - with inventory, usage, spend, and compliance against FedRAMP, ISO 42001, and the EU AI Act.

BigFix Inventory discovers and governs the AI footprint on your computers, including the surfaces that network-based discovery tools miss: locally-hosted LLM weight files, local AI software and Model Context Protocol connections configured in agentic AI clients.

Required permission: View Raw Data, View Endpoints, View License Metrics, View Contracts, Manage Catalogs

You can perform the following actions using the dashboard:

  • Classify discovered AI products against your organization's policy and the EU AI Act risk tiers (Unacceptable, High, Limited, Minimal).
  • Manage AI-related risk directly from the dashboard. Products carrying compliance obligations are flagged when those obligations haven't been activated in your program.
  • Track spend and enterprise plan coverage to identify uncontracted use of paid AI tools at scale.

AI Management dashboard widgets

Overview
The Overview tile shows the following:
  • Data Train Risk: The number of discovered AI Tool products which have High data train risk
  • FedRAMP Certified: The number of discovered AI Tool products which have FedRAMP certification class: High, Moderate, and Low
  • Total AI Spend: The sum of expenses in contracts having AI Tool category. Note: in some cases the value may be different than the sum of expenses in AI Inventory grid. For example, a contract has an AI product but its metric does not match the metric assign to discovered software (0 software installations under a contract but > 0 instances under a product in AI Inventory grid), or when a contract contains multiple products. Drill down to All Contracts report with the filter by AI Tool category.
  • Blocklisted AI Instances: This is the number of discovered software instances of software in AI Tool category, which have 'blocklist' tag on software component or software instance level.
  • AIPC Candidates: The number of computers which are not AI capable but have currently non-blocklisted software containing ‘Local LLM Runtime’ or ‘AI Automation Agent’ subcategory. ‘Not AI capable’ means a desktop computer with Intel or AMD processor not indicated as AI capable by the manufacturer, what is recognized by Processor Brand String.
Top 5 AI Categories
Shows the top five subcategories of AI Tool category per the number of discovered software instances. Click on the links to view the Software Classification with the respective filters.
EU AI Risk Level in AI Products

Discovered AI Tool products per assigned EU AI Risk Assessment and Managed values

Grids in the dashboard

AI Inventory
Every AI product discovered across managed endpoints, with instances count, expense, enterprise plan status, and blocklist counts.
AI Compliance
Per-product compliance attributes covering FedRAMP authorization class, data and vendor residency, training-data risk, EU AI Act risk classification with management status, and ISO 42001 certification.
  • Data Train Risk: Refers to multiple risks associated with providing data to learning models, may be Low/High, and it depends also on enterprise license usage. If there is a contract for the product then No, else it is retrieved from the software catalog data feed
  • FedRAMP Certified: Federal Risk and Authorization Management Program, federal-level standard for security assessment, providing a database with information about product's designation (class): B/C/D (Low/Moderate/High). This value is retrieved from the software catalog data feed. The values are: High, Moderate, Low, or Disallowed.
Local Models
It lists discovered locally stored AI model files.
MCP Servers

The detection mechanism relies on identifying MCP configuration files on the local disk and scanning for specific patterns within them. This approach enables the detection of potential MCP server usage by popular AI tools, such as Claude and ChatGPT, while also capturing the exact file paths. Detailed information regarding the detection and data-gathering processes for MCP servers can be found in a separate section.

See also, MCP Servers