Authority and Urgency in Social Engineering Attacks

Human Decision Risk

February 10, 2026

Humanix

What is feature engineering

In practice, feature engineering is both science and a bit of witchcraft. It often involves both iteration and experimentation to uncover hidden patterns and relationships within the data. For instance, a data scientist might transform raw sales data into features such as average purchase value, purchase frequency, or customer lifetime value, which can significantly boost the performance of a churn prediction model. By thoughtfully engineering features, practitioners can provide machine learning models with the most informative inputs, ultimately leading to better accuracy and more robust predictions.

What’s more?

Incorporate more and more data sources
Feature engineering platform

What is data engineering

As we mentioned above, feature engineering is certainly a subset of data engineering. It involves the ingestion of data from a source, applying a series of transformations, and making the final result available to be queried by a model for training purposes. You can construct feature engineering pipelines to resemble data engineering pipelines, having schedules, specific source and sink destinations, and availability for querying. However, this configuration would only really apply once you have surpassed the experimentation stage and determined a need for a consistent flow of new feature data.

What is feature engineering

1. Functions

Functionally, there is nothing to differentiate data vs features - data points (link). Where feature engineering and data engineering really differ is in the objectives and motivations for constructing the pipelines. In general, data engineering serves a broader, more unified purpose than feature engineering. Data engineering platforms are constructed to be flexible and universal, ingesting various types and sources of data into a unified storage location where any number of transformations and use cases can be applied. The intent of a well constructed fact table or gold layer in a data lake is to provide a single source of truth that answers many different questions, produces many reports, and can be consumed by many downstream customers.

2. Practise

And in practice, an organization’s data engineering team will be responsible for the curation and maintenance of all data pipelines, not just those that relate to machine learning. These pipelines may power BI dashboards used by C-Suite, auditing reports that feed payroll, or event logs that show a user’s history of actions within the application.
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Feature engineering, on the other hand, serves a specific purpose, finding the tailored inputs and columns that will generate the best predictive results for a machine learning model. Data scientists and machine learning engineers are not tasked with developing a universal data model that will ingest all data points throughout an organization, they just need to select, curate, and clean the data needed to power their models.

3. Machine learning

Now, as machine learning teams grow and begin to incorporate more and more data sources into their models, their feature engineering platform may start to resemble a larger data engineering platform in the tools and methodologies they employ. But, the intent is not to establish flexible data models that can be used throughout the organization - it is simply to power their machine learning models.

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The Compliance Formula: Authority Plus Urgency

Social engineers exploit a fundamental truth: authority and urgency create compliance pressure. When someone claiming executive status demands immediate action (”Give Gary access.”), employees must choose between security procedures or obeying leadership under time pressure. Attackers know which motivation wins.

Authority manipulation works because it exploits organizational hierarchy through impersonating executives, board members, or external authorities. Employees defer to high-status individuals, knowing that questioning authority risks career consequences. Attackers need only convincing credentials, project knowledge, or confident delivery.

Creating a sense of urgency narrows decision-making. Attackers create artificial deadlines, threaten consequences, or claim emergencies. Time pressure induces cognitive tunneling where employees solve immediate problems rather than considering context. Urgency increases fear and anxiety overrides security training when organizational cultures rewards action.

Combined, these tactics create maximum manipulation: the "executive" needing emergency wire transfers, the "IT director" requiring immediate password resets, the "auditor" demanding documents before deadlines.

Detection Patterns in Authority and Urgency Exploitation

Linguistic analysis reveals manipulation in real-time. Authority claims include title declarations ("This is the CFO"), name-dropping ("Mrs. Lee authorized this"), hierarchy references. Urgency markers include time constraints ("needed immediately"), consequence threats, pressure phrases ("just this once"). Natural language processing flags these patterns during communications.

Behavioral inconsistencies expose impersonation. Real executives know direct reports, understand internal processes, follow established procedures under pressure. Attackers lack contextual knowledge—the "CEO" unfamiliar with the CFO's name, the "IT director" unaware of change control. These gaps reveal deception despite convincing authority claims.

Timing anomalies suggest manipulation: after-hours urgent requests from executives working standard schedules, first-time callers claiming authority, urgent demands following failed authentication.

Verification resistance signals deception. Legitimate authority accepts security procedures. Attackers resist—claiming urgency prevents verification, refusing established authentication, showing anger when challenged.

Building Real-Time Detection and Response

Deploy conversation analysis tools identifying manipulation patterns during active communications. Modern system should flag authority invocations, urgency escalation, and verification avoidance in real-time.

Establish verification protocols applying regardless of claimed authority or urgency. Out-of-band confirmation, callback requirements, mandatory cooling-off periods prevent impulsive compliance.

Create behavioral baselines for sensitive positions and executive roles. When claimed authorities deviate from established communication patterns, enhanced verification automatically triggers.

Recommended Actions

Immediate steps: Alert on authority claims combined with urgency language. Require supervisor approval for urgent executive requests.

Cultural consideration: Make it safe for employees to verify authority without career consequences. No detection system works if employees fear challenging executives more than breaches.

Implementation resources:

Robert Cialdini's
NIST SP 800-50 on security awareness training
FBI IC3 Business Email Compromise reports
Human Threat Detection and Response platforms

Detecting Reconnaissance and Deceptive Pretexting Before Attacks Begin

Social engineering attacks begin with reconnaissance. Detect pretexting attempts before they become successful breaches.

Implementing Cross-Channel Correlation for Social Engineering Detection

Attackers coordinate across email, voice, and SMS. Single-channel monitoring can't detect these sophisticated campaigns.

How Your Employees' Good Instincts Can Become Security Vulnerabilities

Policy violations happen daily. Learn to distinguish helpful workarounds from malicious exploitation attempts.

Authority and Urgency in Social Engineering Attacks

What is feature engineering

What’s more?

What is data engineering

What is feature engineering

1. Functions

2. Practise

3. Machine learning

The Compliance Formula: Authority Plus Urgency

Detection Patterns in Authority and Urgency Exploitation

Building Real-Time Detection and Response

Recommended Actions

Implementation resources:

Detecting Reconnaissance and Deceptive Pretexting Before Attacks Begin

Implementing Cross-Channel Correlation for Social Engineering Detection

How Your Employees' Good Instincts Can Become Security Vulnerabilities

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