What does AIDR detect?
A support assistant may be allowed to read a customer ticket and write a summary. That does not mean it should send the summary outside your company. The user can be signed in, the device can be healthy, and the AI action can still cross a boundary.
AI detection and response looks for activity like this while an AI system is in use. Depending on the application and its integrations, it can help a security team spot:
- Risky inputs. A document asks an assistant to do something outside the task the user assigned. An alert flags the instruction for review; it does not prove the assistant followed it.
- Unexpected data movement. An assistant sends information to a tool, workspace, or recipient that is not approved for that type of data.
- Actions outside the agent’s role. A tool built to retrieve records asks to edit one. The important question is whether the request was merely proposed, attempted, denied, or carried out.
Coverage is not universal. An AI feature inside a third-party app may expose only a sign-in or a usage event, not the underlying prompt or tool action. Ask which events your team can actually see for each application.
An illustrative case
Did the summary leave the help desk?
A support lead asks an internal AI assistant to summarize a ticket for a shift handoff. The summary belongs in the help desk. Instead, the assistant requests permission to post it to an external workspace. Here is how a security team could handle the case.
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Identify the request
Match the assistant’s tool request to the ticket, the calling identity, the time, and the destination. An unusual destination is enough to investigate, even when the summary itself is not retained.
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Check what the control did
An enforced destination policy could deny the post before it leaves the help desk. A monitoring-only integration could alert the team but could not stop it. The case record should say which happened.
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Verify the outcome
Check the connector’s delivery record and, where available, the destination’s audit log. Keep “request denied” separate from “no data was delivered.” If the available records cannot establish delivery, leave that question open and assign someone to resolve it.
How is AIDR different from AMDR or EDR?
AIDR describes what you protect. AMDR describes how detection and response work gets done. The two can complement each other. Using AI to investigate an endpoint alert does not, by itself, give a team visibility into what an AI application did.
- AIDR: AI detection and response
- Looks at AI interactions as they happen. Its question: “What did the assistant request or do, and did that comply with policy?”
- EDR: endpoint detection and response
- Looks at activity on devices and servers. Its question: “What did this process or account do on the machine?” A device event can corroborate an AI case without revealing the agent’s original request.
- AI security posture management
- Maps AI assets, permissions, and configuration risks. Its question: “Could this agent write to an external workspace?” AIDR asks the follow-up: “Did it try, and what happened?”
- AMDR: agentic managed detection and response
- A managed service uses agents and human analysts to investigate and respond across connected systems. For the division of work, read What is AMDR?
Where should a security team start?
Pick one AI workflow with a named owner and an action that matters to the business. An assistant that reads support tickets and creates draft replies is a better starting point than an inventory of every AI tool employees have opened.
- Write down its boundary. Which records may it read? May it draft, send, or change anything? Identify the person who can approve an exception.
- Build a record you can investigate. Start with time, user or service identity, application, tool, destination, policy decision, and result. Decide whether any prompt content is necessary, who may see it, and when it expires.
- Agree on the handoff. Name the person who will check an alert after hours, who can pause the connector, and who can restore it. Review one example together before treating the workflow as covered.
If several teams own pieces of the same workflow, BitLyft’s guide to an integrated security operations center explains how monitoring, investigation, and response fit together.
Common questions about AIDR
Does AIDR automatically block unsafe AI actions?
Not always. Blocking requires a control that can intervene before the action completes and an approved policy telling it when to do so. An integration that only receives audit events can support an investigation after the fact.
Do we need AIDR if we use AI but do not build models?
You can have an AI security exposure without operating a model. Start with the assistants your people use, what information they can access, and whether they can act on your behalf. Then check which controls the application owner makes available.
Does AIDR require storing every prompt?
No. For the help-desk example, the identity, destination, decision, and delivery result may answer the first investigative questions. Where content is needed, limit collection, access, and retention. A security log should not become an unnecessary second copy of a customer record.
Further reading
For a vendor’s definition of the category, see CrowdStrike’s AIDR overview. Microsoft documents the difference between AI security posture management and threat protection for AI workloads. For guidance on deciding who owns and evaluates AI risk, see the NIST AI Risk Management Framework.
Who handles the alert when an AI tool crosses a line?
Bring one AI workflow to a conversation with BitLyft: what the tool can access, what it is allowed to do, and who owns it. Explore how BitLyft’s Agentic MDR service handles investigations across connected security tools, and which AI events your application would need to supply.