Modern social engineering has evolved well beyond traditional phishing emails. Today’s attacks increasingly rely on human-in-the-loop techniques, including direct interaction with help desks, executive impersonation using AI-generated voices, and the use of remote or intermediary systems to appear geographically and operationally legitimate.
These attacks routinely bypass traditional perimeter defenses such as firewalls, web application firewalls (WAFs), and IP-based controls. In many cases, attackers operate with valid credentials, access systems through real browsers, and deliberately mimic legitimate user behavior. As a result, network-centric and credential-centric security controls alone are insufficient.
The detectors described in this section are designed to identify the technical seams and inconsistencies that emerge when an attacker attempts to impersonate a legitimate user. Rather than focusing on a single signal, these detectors correlate browser behavior, device characteristics, execution environment, network routing, and organizational context. By cross-referencing real-time session data with authoritative sources such as identity provider history, HR records, and known user baselines, the system surfaces risk indicators that are difficult for attackers to fully replicate—even when authentication succeeds.
Detector Framework
The detection framework is organized into logical categories that reflect the different layers an attacker must control in order to convincingly impersonate a real user. Each detector contributes contextual risk signals, not binary verdicts, and is intended to be evaluated in combination with others.
Network and Location Signals
Network and location signals evaluate where access appears to originate and how traffic is routed to the platform. These detectors identify inconsistencies between reported location, routing infrastructure, and expected user geography.
Because social engineering attacks often rely on geographic plausibility to support impersonation pretexts, anomalies in network path or location frequently expose attempts to obscure true origin.
Detector | Primary Social Engineering Risk |
|---|---|
Use of Tor, proxies, or anonymization infrastructure to hide origin | |
Impersonation of remote workers or geographic alignment with a victim | |
Location mismatches relative to HR records, CVs, or identity provider history | |
Use of remote bridges or accomplice-hosted physical machines |
Endpoint
Endpoint signals focus on the device and execution environment used to access the platform. These detectors evaluate whether the hardware, operating system, browser, and media capabilities align with what is expected for the claimed user.
Attackers frequently rely on disposable, virtualized, or specially configured environments. While credentials can be stolen, reproducing a legitimate endpoint profile over time is significantly more difficult.
Detector | Primary Social Engineering Risk |
|---|---|
Use of disposable or isolated attack environments | |
Injection of AI-generated or pre-recorded audio into voice interactions | |
Session or identity hijacking from an unauthorized machine | |
Use of attack rigs or low-end hardware inconsistent with the user profile | |
Use of anti-detect or identity-spoofing browsers |
Behavior and Usage
Behavior and usage signals analyze how interactions are performed, independent of the content being submitted. These detectors identify automation, scripted workflows, and interaction patterns that deviate from normal human behavior.
In social engineering campaigns, behavioral anomalies often emerge when attackers attempt to scale activity, bypass verification steps, or accelerate access using tooling rather than manual interaction.
Detector | Primary Social Engineering Risk |
|---|---|
Automation of credential abuse, brute-force attempts, or MFA guessing | |
Deviations from established user habits, such as browser, OS, or configuration changes | |
Presence of attacker tooling such as debuggers or hacking extensions | |
Use of freshly created or wiped environments with no usage history |