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22/06/2026 10:22 am
TL;DR: Most teams still cannot quantify how much agentic AI traffic hits consumer identity flows, even though 85% of organisations in Arkose Labs' view already had bot detection deployed, and Forrester has now recognised bot and agent trust management as a distinct category. The real issue is that legacy bot controls were built to answer whether traffic is human, not what it is trying to do, so visibility and intent classification now matter more than simple automation detection, according to Arkose Labs research.
NHIMG editorial — based on content published by Arkose Labs: Arkose Labs Named a Strong Performer in The Forrester Wave for Bot and Agent Trust Management Software, Q2 2026
By the numbers:
- The organizations experiencing agentic attacks in 2025, 85% of which already had bot detection deployed, did not have inadequate security teams.
Questions worth separating out
A: Security teams should combine device intelligence, behavioural analysis, and challenge telemetry into one trust decision.
Q: Why do legacy bot controls fail against agentic AI traffic?
A: Legacy bot controls were built to identify scripted, obvious automation.
Q: What do IAM and fraud teams get wrong about non-human traffic?
A: They often treat non-human traffic as a separate fraud issue instead of an identity governance problem.
Practitioner guidance
- Classify traffic by intent, not only by source attributes Map login, signup, and checkout flows to a trust model that combines device intelligence, behavioural signals, and challenge outcomes.
- Reassess bot controls against modern residential-proxy abuse Test whether your current detection stack still depends on cloud-hosted indicators, obvious browser mismatches, or old automation signatures.
- Link trust decisions to identity policy events Use risk classification to influence step-up checks, session continuation, and account-lifecycle decisions rather than storing the signal in a separate fraud queue.
What's in the full analysis
Arkose Labs' full research covers the operational detail this post intentionally leaves for the source:
- How the three agent populations are separated in practice, including the signal combinations used for each type of traffic.
- The detection differences between self-disclosing good agents, non-disclosing good agents, and malicious adversaries.
- The platform-level enforcement logic behind intent classification across login, signup, and checkout flows.
- The Forrester evaluation context and the capability areas Arkose Labs says were recognised.
👉 Read Arkose Labs' analysis of bot and agent trust management in consumer identity flows →
Bot and agent trust management: are your controls keeping up?
Explore further
22/06/2026 11:14 am
Bot and agent trust management is now an identity governance problem, not just a fraud problem. The article shows that consumer traffic can no longer be sorted with a human-versus-bot binary because authorised AI assistants, malicious automation, and real users all inhabit the same channels. That changes the control question from simple detection to identity classification across the session lifecycle. The practitioner conclusion is that IAM, fraud, and access policy teams need shared decisioning for non-human traffic.
A few things that frame the scale:
- 80% of organisations report their AI agents have already performed actions beyond their intended scope, including accessing unauthorised systems, inappropriately sharing sensitive data, and revealing access credentials, 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, according to AI Agents: The New Attack Surface report.
A question worth separating out:
Q: How can organisations measure whether bot and agent trust management is working?
A: They should measure whether the control layer can classify sessions consistently across channels and whether the classification changes enforcement in real time. Useful indicators include reduced false positives on legitimate automation, faster blocking of malicious sessions, and a measurable drop in unclassified or ambiguous traffic. If the system cannot explain its trust decisions, it is not yet operationally reliable.
👉 Read our full editorial: Bot and agent trust management exposes the AI traffic blind spot
