Key points
- New AI Infrastructure Indicator Lists provide IP addresses, domains, and CIDR ranges for AI services.
- The lists include general AI tools, coding assistants, and Chinese-domiciled services.
- Customers with Cyber Operations licenses receive the lists at no additional cost.
The short version
- Recorded Future introduces AI Infrastructure Indicator Lists to help teams detect shadow AI.
- The datasets contain curated indicators with risk scores and analyst comments.
- The lists aim to close governance gaps for unsanctioned AI tool usage.
The details
Recorded Future announced the launch of AI Infrastructure Indicator Lists, which are curated datasets designed to help security teams identify and monitor AI-related network traffic. According to the company, these lists assist organizations in enforcing policy and preventing data loss from unsanctioned tools. The new lists are included free of charge for Recorded Future customers who hold a Cyber Operations license.
The indicator lists provide specific data points such as IP addresses, CIDR ranges, and domains. Each entry includes a Risk Score and a comment field that attributes the service to a specific provider. Recorded Future offers two distinct lists: a general AI infrastructure list and a separate list for Chinese AI infrastructure. These datasets cover a wide range of tools, including general-purpose assistants, coding helpers, voice and video AI tools, and autonomous agents.
Each indicator follows a consistent structure that maps cleanly to existing security tooling. The format includes the entity name, risk score, and an analyst comment that serves as an answer key. Comments detail the provider, the last verification date, and the association method used to attribute the indicator. For example, an entry might list Anthropic with a verification date and a note about live JSON attribution. This structure allows the data to drop directly into SIEM lookups, firewall imports, and threat intelligence platforms.
The context
Organizations are increasingly confronting the risks of shadow AI, which refers to unsanctioned tool use that bypasses procurement and security reviews. Recorded Future notes that artificial intelligence has become enterprise infrastructure, yet controls often fail to see autonomous behavior or data exposure. The company states that blind spots in AI governance are increasing as security teams prioritize daily threats over emerging AI risks.
The adoption of agentic AI is accelerating, with agents operating across interconnected systems. This expansion gives threat actors more opportunities to exploit vulnerabilities at the organizational level and across supply chains. Recorded Future points to OpenClaw, a free open-source AI assistant, as an example of a tool that saw explosive adoption with little organizational oversight. Such scenarios highlight the difficulty of governing tools that security teams may not know exist.
Recorded Future reports that by 2027, an estimated 75% of employees are expected to adopt or build technology outside IT governance. As autonomous processes become more ubiquitous, defenders need to track and manage access permissions for these tools in the same way they manage permissions for human users. The company emphasizes that the hidden risks of abundant AI are often overshadowed by other security priorities, leading to accrued security debt.
What to watch
Security teams should monitor for unauthorized AI traffic using the new indicator lists. Recorded Future advises organizations to discover before they block, suggesting that visibility is the highest-value first move. Teams are encouraged to run the full list against 30 to 90 days of historical DNS and proxy logs to baseline what their environment already reaches. This approach helps identify existing shadow AI usage before implementing enforcement controls.
Background: AI governance
AI governance establishes the technical and procedural controls required to manage risk throughout the model lifecycle. It defines who can deploy models, how data is validated, and how decisions are audited. This framework prevents drift, ensures compliance, and maintains trust in automated systems.
Read the full guide: AI Governance: 8 Questions Answered for Technical Teams
What to do and how to stay safe: Recorded Future
- Review historical DNS and proxy logs to identify existing connections to AI infrastructure.
- Integrate curated indicator lists into SIEM and firewall rules for better visibility.
- Establish baseline metrics for AI tool usage before enforcing blocking policies.
- Monitor for autonomous agent activity that may bypass standard user permission controls.
Step-by-step guide: Shadow AI Mistakes That Put Corporate Data at Risk
General security guidance from the TechSec Headlines newsroom. It is not confirmed advice from the organisations named in this story.
Frequently asked questions
What is included in the AI Infrastructure Indicator Lists?
The lists include IP addresses, CIDR ranges, and domains for various AI tools, along with risk scores and analyst comments.
Who can access these indicator lists?
Recorded Future customers with a Cyber Operations license receive the AI Infrastructure Indicator Lists for free.
Why are these lists important for security teams?
They help teams identify shadow AI, enforce governance policies, and prevent data loss from unsanctioned tools.
