What is AIDR? AI Detection and Response

What is AI detection and response?

AI detection and response (AIDR) is the practice of detecting, investigating, and responding to threats that target AI systems or originate from them, at runtime, while the AI is actually working. It is the AI-era counterpart to EDR: where endpoint detection and response watches processes on a machine, AIDR watches prompts, responses, and tool calls as they happen, across every model, agent, and MCP server an organization uses.

The category emerged because agentic AI changed the shape of the threat. A chatbot that only answers questions can leak data, which is bad enough. An agent that reads repositories, calls tools, browses the web, and executes commands can be manipulated into taking actions: exfiltrating a database through a tool call, running a command an attacker planted in a web page, or connecting to a rogue MCP server that impersonates a real one. These attacks unfold at machine speed, in seconds, inside encrypted traffic that traditional controls never open.

Legacy tools operate on either side of the action, never during it. Posture tools and governance frameworks work before: they inventory assets, set policy, and assess risk. Logging and forensics work after: they tell you what happened once it has happened. Neither can stop an agent mid-action, and with agents, mid-action is the only moment that matters. By the time a SIEM alert fires, the tool call has executed and the data is gone. AIDR closes that gap by putting detection and response at the moment of execution.

What AIDR requires

Three capabilities separate real AIDR from a rebranded dashboard. Drop any one of them and you are back to before-or-after security with a new name.

Runtime inspection. The system has to see the actual content of AI traffic as it flows: the prompt a user types, the context an agent attaches, the arguments of every tool call, and the response coming back. Metadata is not enough. A DNS log that says someone reached an AI provider cannot tell you whether the request carried a customer database or a lunch order.

Cross-surface visibility. AI does not run in one place. It runs in browser tabs, in desktop apps, in IDE assistants, in terminal CLIs, and behind MCP servers that connect agents to internal systems. A control that only watches the browser misses the coding agent streaming your repository to a model, and people route to whichever surface is unwatched. AIDR has to cover every surface on the same inspection path, with one policy.

Action at the point of execution. Detection without response is a faster way to read bad news. When inspection finds a secret in a prompt, a poisoned tool description, or a call to an unapproved model, the system must be positioned to do something about it in line: block the request, redact the sensitive spans, or quarantine the offending server, before the traffic leaves the machine.

AIDR vs adjacent categories

AIDR sits in a crowded shelf of AI security acronyms, and the boundaries matter when you are writing requirements. AI security posture management (AI-SPM) inventories and assesses AI assets before anything runs. AI firewalls and AI DLP inspect traffic inline, which makes them building blocks of AIDR rather than alternatives to it. AI governance platforms manage policy and compliance evidence, and enforce nothing by themselves.

The clean way to hold it: posture and governance describe and direct, AIDR observes and acts. Most organizations will end up with both halves, and the practical question is how many products that takes. We compare all five categories, what each does, and what each cannot do in the companion piece, AI security categories, compared.

AIDR without the platform tax

Because AIDR extends the detection-and-response idea, the instinct is to assume it must arrive the way EDR did: as one module of a large security platform, priced and deployed accordingly. That assumption deserves scrutiny before it costs you a year.

If runtime AI security only comes bundled with an endpoint platform, adopting it means an EDR migration: ripping out an agent that works, re-certifying your fleet, and retraining your SOC, to solve a problem that is orthogonal to malware. If it only comes bundled with a network stack, adopting it means a SASE rollout: months of routing every office and remote worker through a vendor cloud that now terminates and reads your most sensitive traffic. Either way you pay a platform tax, in time, in money, and in lock-in, for one capability: seeing and controlling AI at runtime.

There is a simpler architecture. AI traffic leaves from the endpoint, so the endpoint is where you can inspect all of it, browser, desktop, IDE, and CLI alike, without touching the rest of your stack. Cerbera takes this route: endpoint-first AIDR delivered as one transparent proxy, deployed and removed through the MDM you already run, in an afternoon. It coexists with your existing EDR, VPN, and DNS tooling instead of replacing any of it, and inspection happens locally on the device, so prompts are not shipped to a vendor cloud to be scanned. You get the AIDR capability without buying the platform around it.

How Cerbera maps to AIDR

Detection and response programs are usually described in three verbs: see, detect, respond. Here is how the Cerbera platform delivers each one for AI.

See. Discovery of every AI tool, model, agent, and MCP server in use across the organization, including the shadow AI nobody registered. Because the proxy sits on the endpoint, the inventory reflects what actually runs, not what was declared in a questionnaire.

Detect. Inspection of every prompt, response, and tool call at runtime. Detections cover secrets, source code, PII and regulated data leaving in prompts, prompt injection arriving in tool results, poisoned tool descriptions, and calls to unapproved models or unvetted MCP servers. Detection runs locally, so sensitive content is analyzed on the device it came from.

Respond. Action at the point of execution: block the request, redact the sensitive spans so work continues without the leak, or quarantine a rogue MCP server. Every event streams to your SIEM with full context, so security teams investigate AI incidents in the same workflow they use for everything else, and the same records serve as evidence for ISO 42001, the EU AI Act, and SOC 2.

That is the whole category in one deployment: see, detect, respond, at runtime, on every surface, without a platform migration.