They should stop treating automation as the primary decision point and instead classify the intent behind the interaction. That means using behavioural, contextual, and session signals to separate legitimate AI assistance from adversarial automation, then applying different response depths as trust changes during the journey.
Why agent-like users need a different fraud decision model
Fraud teams should not decide purely on whether a session is automated or human. An AI agent can present the same surface signals as a normal customer, so the more reliable question is whether the interaction behaves like a legitimate delegated assistant, a scripted abuse flow, or a compromised account. The control point is intent plus trust evidence, not UI appearance alone.
That means fraud logic needs to combine behavioural analysis, contextual history, device and session continuity, and transaction purpose. A low-friction assistant flow may be acceptable early in the journey, but the same pattern becomes suspicious if it changes destination accounts, accelerates volume, or starts touching sensitive actions that legitimate assistance would not normally need.
Teams also need to separate recognition from response. Classification can remain probabilistic while step-up controls, rate limits, and review thresholds vary by action. The goal is to avoid over-blocking useful automation while still treating agentic behaviour as a distinct trust class when the observed pattern no longer fits ordinary customer intent.
How to tell legitimate assistance from adversarial automation
In practice, the strongest discriminator is whether the session shows stable, explainable intent across the journey. Legitimate assistance usually has continuity: the same task, a consistent path, and actions that match prior behaviour or an explicit user request. Adversarial automation tends to optimise for speed, exploration, or repeated variation, especially when it probes limits, retries across many objects, or pivots after a challenge.
Security teams should look for signal clusters rather than any single indicator. Session age, authentication strength, device reputation, IP velocity, transaction novelty, and action sequencing matter more together than in isolation. A well-behaved agent may still look unfamiliar, but it should remain coherent. A risky one often mixes normal-looking navigation with abnormal action density or inconsistent purpose.
This is why behavioural baselines should be built around the interaction pattern, not just the actor label. If the flow is expected to involve human-in-the-loop assistance, the fraud model should preserve that possibility while still flagging signs of delegation abuse, account sharing, or automated probing. NHIMG’s AI Agent Authorisation Guide is useful here because it frames access decisions around task scope and per-action authority.
What changes in the fraud workflow when the user may be an agent
The main operational change is that fraud review must become progressive. Early-touch interactions can be scored with lighter friction, but high-impact steps should trigger stronger checks when the trust story weakens. That is especially important in flows that start as information gathering and later move into payments, account recovery, beneficiary changes, or withdrawal actions.
Security teams should also preserve attribution. If the same account can be used by a person, an assistant, or an external automation layer, the case record needs to show which signals were present at each decision point. That helps investigators distinguish normal delegation from abuse and prevents one bad session from poisoning the model for all future assistant-like traffic.
NHIMG’s AI Agent Observability, Audit and Incident Response Guide is relevant because fraud teams need auditable action trails, not just an initial login event. NHIMG’s Zero Trust for AI Agents also aligns well with this approach by reinforcing continuous verification and per-action enforcement.
Risk and Threat Considerations
Agent-like users create fraud risk because they can compress attack timelines while remaining visually similar to ordinary customers. The danger is not that the system is automated, but that adversarial automation can inherit trusted sessions, mimic legitimate browsing, and then escalate into high-value actions once basic checks are passed.
Failure mechanism: Static bot rules, channel reputation, or simple human-vs-machine classifiers miss the fact that the same session can shift from benign assistance to abuse. Attackers exploit that gap by chaining low-risk requests into sensitive ones, reusing authenticated sessions, or varying behaviour just enough to avoid threshold-based detection.
Impact: Teams may approve fraudulent transfers, account changes, or recovery actions that look ordinary in isolation. The resulting loss is often amplified by delayed detection, because the session appears legitimate until the trust boundary has already been crossed.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
OWASP Agentic AI Top 10 and MITRE ATT&CK address the attack and risk surface, while NIST SP 800-53 Rev 5 and NIST CSF 2.0 set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Agentic AI Top 10 | ASI03 — Identity & Privilege Abuse | Agent-like fraud flows hinge on misuse of delegated authority and trust. |
| Recommendation — Enforce per-action authorization and step-up checks when agent behavior changes trust level. | ||
| NIST SP 800-53 Rev 5 | AU-2 — Audit Events | Fraud decisions need detailed event logging to reconstruct intent and trust changes. |
| IA-5 — Authenticator Management | Assistant-like sessions depend on credential and session handling that can be abused. | |
| Recommendation — Log agent and customer actions with enough detail to reconstruct each fraud decision. Rotate and protect authenticators used in high-risk customer and agent sessions. | ||
| MITRE ATT&CK | T1078 — Valid Accounts | Adversarial automation often abuses legitimate accounts and sessions to blend in. |
| Recommendation — Hunt for fraud patterns that abuse valid accounts rather than obvious malware. | ||
| NIST CSF 2.0 | DE.CM-01 — Continuous Monitoring | Behavioural and contextual monitoring is central to distinguishing trusted assistance from abuse. |
| Recommendation — Monitor session behaviour continuously and escalate when trust signals deteriorate. | ||
Practitioner Guidance
What to verify: Check whether the decision model scores intent and session coherence, not just device, IP, or automation fingerprints. If an interaction can legitimately be assisted, the model should require stronger evidence before high-risk actions, not before every step.
Decision rule: If a session starts to behave like an agent, treat the next sensitive action as the control point. Use that moment to apply step-up verification, tighten rate limits, or send the case to review rather than trying to classify the entire session as good or bad upfront.
What good looks like: Investigators can see why a flow was allowed, why it was slowed, and which trust signals changed over time. That makes the fraud stack resilient to both normal automation and adversarial mimicry.
Practitioner takeaway: The best fraud controls do not ask whether the user is human or automated first, they ask whether the current action is still consistent with trusted intent.
Related resources from NHI Mgmt Group
- How should security teams handle authentication for autonomous AI agents that cannot log in or maintain sessions like human users?
- How should security teams handle AI-generated phishing that looks like normal business mail?
- How should security teams handle authentication when users, digital IDs, and AI agents share the same trust model?
- How should security teams handle alerting when AI agents can take actions that resemble normal user or tool activity?
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Reviewed and updated by the NHIMG editorial team on October 11, 2026.
NHI Mgmt Group — the #1 independent authority on Non-Human Identity, IAM, and Agentic AI security. nhimg.org