AI detection and response (AIDR) is a young, loosely defined category: monitoring AI systems and agents for misuse, data leaks and unsafe actions, and responding when something goes wrong. A small team can start in 90 days by inventorying AI use, logging model and tool activity, and writing response steps.
What is AIDR?
There is no single standard definition, and vendors use the term differently. In practice it covers four jobs: knowing where AI is used, recording what models and agents are asked and do, detecting unsafe patterns, and responding. This plan treats it as a set of practices that a small team can run before it buys a product.
Days 1 to 20: inventory
List every place AI is used: chat tools staff use, coding assistants, agents with tool access, models embedded in your product and third party AI features in SaaS. Interview teams, and check expense records and single sign on logs for AI services.
For each use record owner, data it sees, actions it can take and credentials it holds. This often reveals agents running on a developer's personal token. Rank by what could go wrong: an assistant that only drafts text is less urgent than an agent that can change production data.
Days 21 to 45: see what happens
You cannot detect what you do not record. For each high risk use, log inputs, outputs, tool calls and the identity involved, subject to privacy and legal review. Decide retention and who may read the logs, since prompts can hold sensitive data.
Use a shared language for risk. OWASP's Top 10 for LLM Applications includes Excessive Agency, where a system has more capability than its task needs, and its Top 10 for Agentic Applications for 2026 covers risks in systems that plan and act. Map your inventory to those lists to decide what to watch for.
For agents that call tools, check how they authenticate. The MCP security guidance describes token passthrough and over broad scopes as risks. Replace shared tokens with per agent credentials and narrow scopes where you can.
Days 46 to 65: detect
Write a short list of detections you can run with the logs you now have. Constructed examples, to be adapted: an agent calling a tool it has never used before; a spike in volume of data returned; secrets appearing in prompts or outputs; an agent acting outside its usual hours; repeated denied calls.
Start with rules and thresholds, review hits weekly and tune. Keep it small: five detections that someone reads beat thirty that nobody does. Do not claim model based detections are reliable until you have tested them on your own cases.
Days 66 to 80: respond
Write a one page response plan for AI incidents. For each type, such as data sent to an unapproved service or an agent taking an unwanted action, define who decides, what can be stopped (revoke the agent's credential, disable a tool, block a destination) and what evidence to keep. NIST SP 800-61 Rev. 3 gives a general frame that ties response to wider risk management, and it applies here.
Run a tabletop exercise with a constructed scenario: an agent with ticket access begins sending customer data to an external address. Time how long it takes to identify the agent, find its owner and cut its access.
Days 81 to 90: review and decide
Report what you found: number of AI uses, how many had owners, how many ran on shared credentials, detections fired and the tabletop results. Decide whether products would help. If so, run a trial with your own scenarios and ask what the product records, what it can block, where data goes and how you exit.
What to measure
Share of AI uses with an owner. Share of agents with their own identity. Time to revoke an agent in a drill. Number of detection hits reviewed within a set time.
Common traps
Buying a product before inventory. Logging prompts without a privacy review. Detections nobody reads. Treating the category label as a guarantee of coverage.
Where Safeguard fits
AIDR as a category is not something we sell. For agents in software delivery and over MCP, Safeguard's agent identity tracks which agent holds what access, MCP Guardrail checks calls against policy, and gates enforce rules at build and release. These prevent and record. They do not replace a dedicated monitoring program.
What to do
- Inventory every AI use with owner, data and credentials.
- Log inputs, outputs and tool calls for the riskiest ones, after privacy review.
- Give agents their own credentials with narrow scopes.
- Run five simple detections and review them weekly.
- Write a one page response plan and rehearse it.
- Decide on tools after day 90, using your own scenarios.
Quick check
Is AIDR a standard category?
Not yet. There is no single agreed definition, so ask any vendor exactly what it records, detects and can block.
What is the first step?
Inventory where AI is used, who owns it, what data it sees and what credentials it holds.
Sources
- OWASP Top 10 for LLM Applications
- OWASP Top 10 for Agentic Applications for 2026
- MCP security guidance
- NIST SP 800-61 Rev. 3
Last reviewed: 18 October 2026.
