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02/08/2026 11:24 am
TL;DR: AI Detection and Response (AIDR) targets runtime threats in AI agents, LLM applications, and MCP servers by monitoring prompts, tool calls, and model behaviour, according to Akto. Traditional SIEM and EDR tooling miss the AI-specific telemetry that makes prompt injection, unauthorized tool use, and data leakage visible in production systems.
NHIMG editorial — based on content published by Akto: AI Detection and Response (AIDR): A Complete Guide to Securing AI Agents and LLM Applications
Questions worth separating out
Q: How should security teams monitor AI agents and MCP servers in production?
A: They should monitor the AI interaction layer directly by collecting prompts, responses, tool calls, and MCP traffic in one telemetry stream.
Q: Why do traditional SIEM and EDR tools miss AI threats?
A: They miss AI threats because those tools were built to observe infrastructure events, not AI behaviour.
Q: What signals show that an AI agent is operating outside its intended purpose?
A: Look for mismatches across identity, data, model behaviour, posture, and environment.
Practitioner guidance
- Build AI-native telemetry pipelines Capture prompts, model outputs, tool calls, MCP traffic, and agent-to-agent exchanges in the same investigation path so analysts can reconstruct behaviour end to end.
- Define behavioural baselines before production rollout Record expected tool usage, data access patterns, and call frequency for each AI agent or MCP server so drift can be flagged during live operation.
- Test mid-session containment controls Verify that the programme can block a risky tool call, quarantine an agent, revoke a session, or apply a guardrail while the interaction is still active.
What's in the full article
Akto's full blog post covers the operational detail this post intentionally leaves for the source:
- Step-by-step breakdown of the AIDR lifecycle from discovery through response and improvement
- Practical examples of runtime guardrails, session revocation, and agent quarantine in live workflows
- Comparative discussion of AIDR versus SIEM, EDR, XDR, and AI-SPM for production AI security
- Implementation-oriented use cases for detecting prompt injection, tool misuse, and sensitive data exposure
👉 Read Akto's guide to AI detection and response for AI agents and MCP servers →
AI detection and response for AI agents, LLMs, and MCP servers?
Explore further
02/08/2026 12:41 pm
AI runtime security is now a governance problem, not just a detection problem. Once agents can select tools, move across systems, and expose data in a live session, the control question changes from whether the environment is logged to whether the AI interaction is governable at all. Traditional monitoring does not understand AI intent, tool schemas, or prompt context, so the operational issue is not coverage alone but whether the security model can interpret the action chain. Practitioners should treat AIDR as part of identity and access governance for non-human execution.
A few things that frame the scale:
- 92% agree governing AI agents is critical to enterprise security, yet only 44% have implemented any policies to do so, according to AI Agents: The New Attack Surface report.
- Only 52% of companies can track and audit the data their AI agents access, leaving 48% with a complete blind spot for compliance and breach investigation.
A question worth separating out:
Q: When should organisations add response controls to AI detection?
A: As soon as AI systems can touch real data or external tools in production. If the team can only detect and investigate after the event, it has no containment capability. Response controls should exist before deployment so risky actions can be blocked during the session, not after harm spreads.
👉 Read our full editorial: AI detection and response is becoming essential for AI agent security
