Model Context Protocol is becoming the standard way for AI assistants to connect to the systems they need to be useful. OpenAI, Microsoft, Google, and a growing number of enterprise platforms have adopted MCP as the integration layer between AI models and real-world context. Today, we are extending TENET into that ecosystem with the TENET MCP server.
The TENET MCP server lets AI assistants query your Azure risk data directly. Instead of switching between tools to gather context, your AI assistant can pull live risk signals, identity exposure, compliance posture, and attack path data from TENET and use that information to help you understand, prioritize, and remediate issues, all within the workflow you are already in.
Why MCP matters for cloud security
AI assistants are becoming a core part of how engineers and security teams work. They help with code, investigation, root cause analysis, and operational decisions. But without access to real environment data, those assistants are limited to general knowledge. They cannot tell you what is actually exposed in your Azure tenant, which identities carry the most risk, or which finding should be fixed first.
MCP changes that by giving AI models a structured way to call tools, read data sources, and retrieve live context from external systems. When a security platform exposes an MCP server, the AI assistant can query it mid-conversation, use the results to shape its response, and guide the user through decisions grounded in real data rather than assumptions.
For TENET, this means your AI assistant now has access to the same risk intelligence your security team relies on.
What the TENET MCP server enables
Three capabilities define what the TENET MCP server makes possible.
Unified Azure risk context. The MCP server gives AI models a single access point to TENET's full risk picture, covering asset exposure, identity relationships, misconfiguration signals, anomaly indicators, compliance evidence, and attack path data. Rather than asking teams to copy findings into prompts or describe their environment manually, the AI assistant can retrieve that context directly.
Live posture answers. Security questions that previously required manual investigation, such as which resources are internet-exposed, which identities have standing privilege to sensitive data, or which misconfigurations are compounding an existing risk, can now be answered in seconds through a natural language prompt backed by live TENET data.
Remediation guidance in context. Because the AI assistant knows what TENET is seeing, it can move beyond identifying issues and help teams understand what to do about them. That includes explaining the risk, mapping the blast radius, identifying the responsible team, and suggesting the specific change that would close the exposure.
What you can do with it
Surface and fix Azure risks from your IDE
For engineering teams that use AI coding assistants, the TENET MCP server brings security context directly into the development workflow.
A developer working on an Azure-connected service can ask their AI assistant to check whether the workload they are building or modifying has any associated exposure in TENET. The assistant queries the MCP server, retrieves relevant findings, and surfaces the risk inline. If there is a misconfiguration, excessive permission, or a dependency on a resource with known issues, the developer sees it before the code ships.
That workflow looks like this in practice:
- Developer prompts the assistant to review the deployment config for security issues
- Assistant calls the TENET MCP server to retrieve risk signals for the relevant resources
- TENET returns exposure data, identity context, and misconfiguration findings
- Assistant explains the risk and recommends the specific fix
- Developer applies the change, and the assistant confirms the issue is resolved
No tab switching. No separate security review cycle. The risk intelligence arrives in the same environment where the fix happens.
Investigate cloud threats without leaving the terminal
Security analysts working in terminal-based or command-line AI environments can use the TENET MCP server to run real-time threat investigations.
Instead of querying multiple tools and manually connecting signals, the analyst can describe what they are seeing and let the assistant pull the relevant TENET data. If an anomaly alert fired on a workload, the assistant can retrieve the identity context behind that workload, map its permissions, check whether it has a path to sensitive resources, and help the analyst determine scope and containment steps in one conversation.
That investigation might follow this sequence:
- Analyst describes the anomaly and asks the assistant to investigate
- Assistant queries TENET for the affected resource's full risk profile
- TENET returns identity relationships, permission scope, blast radius, and connected assets
- Assistant identifies the most likely impact path and recommends immediate containment actions
- Analyst acts on the guidance with full environmental context already in hand
What previously took multiple tool queries and manual correlation now happens in a single conversation.
Ask anything about your Azure posture
For teams that want to use AI as a standing security assistant, the TENET MCP server enables conversational access to your full Azure risk posture.
Security leaders, architects, and risk owners can query the assistant the same way they would ask a knowledgeable colleague. Which resources are publicly exposed? Where does identity risk overlap with a compliance gap? Are there toxic combinations in the environment that should be escalated this week?
The assistant retrieves the data from TENET, synthesizes it, and returns an answer that reflects what is actually true in the environment, not a generalized answer based on training data.
That kind of always-available posture visibility changes how organizations consume security data. Rather than waiting for a weekly report or scheduling a review, teams can query their risk posture the moment a question arises.
How to get started
The TENET MCP server is available now. Setup requires adding TENET as an MCP source in your AI assistant's configuration, and authenticating with your TENET API credentials. Once connected, your assistant can begin querying TENET data immediately.
Full setup documentation is available in the TENET platform. For teams that want a guided walkthrough, contact your TENET account team or reach out through the demo request flow on this site.
What this represents
MCP is infrastructure for the next phase of AI-assisted security work. As AI assistants take on more operational tasks, the quality of their decisions depends directly on the quality of the context they can access.
The TENET MCP server is how Azure risk intelligence enters that layer. It means the AI your team already uses can now see what TENET sees, respond with data that reflects your real environment, and help your team move faster from detection to decision to remediation.
Cloud security at scale requires both the right signals and the ability to act on them quickly. The TENET MCP server is designed to close the gap between those two things.