Check for an active call at moments where social engineering is most likely to matter, such as OTP entry, password resets, transfers, beneficiary changes, and account recovery. A call is not automatically fraud, so use it as a contextual signal. Pair it with step-up authentication, short confirmation delays, manual review, or warning prompts to slow the attacker without blocking legitimate users.
Why This Matters for Security Teams
Active call signals matter because they can indicate that a user is being coached in real time, which is exactly when fraud and social engineering tend to succeed. During sensitive mobile actions, an incoming or ongoing call may be benign, but it can also coincide with OTP harvesting, screen-sharing, credential reset abuse, or pressure to approve a transaction. The signal is useful precisely because it adds context, not certainty. That distinction aligns with NIST Cybersecurity Framework 2.0, which emphasises risk-based protection and detection rather than binary trust decisions.
Fraud teams often get this wrong by treating call presence as a standalone rule. That creates avoidable friction for legitimate users and still misses coached fraud when the attacker uses a second device or delays the call until after the critical step. The better approach is to treat the call state as one signal in a broader risk model that includes device change, velocity, behavioural anomalies, beneficiary novelty, and step-up outcomes. In practice, many security teams encounter call-linked social engineering only after a transfer or recovery flow has already been completed, rather than through intentional friction at the point of highest risk.
How It Works in Practice
Active call signals should be collected at the exact moments where user guidance is most dangerous: OTP entry, password reset, new payee setup, high-value transfer, and account recovery. The signal is then scored alongside other risk indicators so that the system can choose a proportionate response. That response might be a short delay, an extra confirmation prompt, a stronger authentication challenge, or a manual review queue. Used well, the signal slows the attacker without forcing every user into the same high-friction path.
Operationally, teams should define what counts as a call signal, how fresh it must be, and how long it remains relevant. A live call during a beneficiary change may justify friction; a call that ended minutes earlier may carry less weight. The goal is to avoid hard coding a single assumption into production logic. Current guidance suggests these controls should be integrated with broader access and transaction controls described in NIST SP 800-53 Rev. 5 Security and Privacy Controls, especially where organisations need to justify authentication, monitoring, and user-notification decisions.
- Use active call status as a contextual risk input, not as proof of fraud.
- Trigger the signal only at moments of material exposure, not across all app activity.
- Combine the signal with device trust, recent credential changes, and transaction novelty.
- Apply responses that slow the attacker but preserve legitimate completion paths.
- Log the signal and the outcome so fraud analysts can tune thresholds over time.
This guidance tends to break down in environments where telephony metadata is incomplete, privacy constraints limit signal collection, or the mobile app cannot reliably distinguish a genuine call from background call state changes.
Common Variations and Edge Cases
Tighter call-based controls often increase friction and customer support load, requiring organisations to balance fraud reduction against completion rates and accessibility. There is no universal standard for exactly how much weight an active call should carry, so teams should treat thresholds as a local risk decision rather than a fixed industry rule. The strongest implementations usually allow for customer segment differences, transaction value bands, and jurisdictional privacy requirements.
Edge cases matter. A call can be a family member, a call centre, or a fraudster coaching the user. A user may also be on a legitimate call during a travel booking, bill payment, or emergency transfer. In those cases, the right response is often soft friction, not denial. Best practice is evolving toward adaptive measures that preserve legitimate intent while still interrupting manipulation. For teams building a broader fraud operating model, the NIST Cybersecurity Framework 2.0 remains a useful anchor for linking detection logic to response and recovery decisions.
Fraud teams should also be careful about overfitting the signal to one attack pattern. If the rule is too visible, attackers will simply move the call earlier or switch to text-based coaching. The practical objective is not to detect every call, but to make high-risk mobile actions harder to complete under live social engineering pressure.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
NIST CSF 2.0 and NIST SP 800-53 Rev 5 set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| NIST CSF 2.0 | DE.CM | Active call signals are monitoring data used to detect suspicious user conditions. |
| NIST SP 800-53 Rev 5 | IA-2 | Sensitive actions should still rely on strong authentication, not call presence alone. |
Feed call-state signals into detection logic and tune alerts around risky transaction moments.
Related resources from NHI Mgmt Group
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Reviewed and updated by the NHIMG editorial team on August 26, 2026.
NHI Mgmt Group — the #1 independent authority on Non-Human Identity, IAM, and Agentic AI security. nhimg.org
