All articles
Share

How Your Employees' Good Instincts Can Become Security Vulnerabilities

Human Decision Risk
February 10, 2026
Humanix
Title
SHARE
SHARE
SHARE

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

Image description

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.

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.

Why Human Behavior Becomes the Attack Vector

Social engineering succeeds by exploiting predictable human responses such as trust in authority, urgency under pressure, and cooperation with colleagues. Traditional security controls must monitor technical indicators while attackers manipulate human decision-making. Effective detection requires understanding cognitive patterns that create vulnerability and building systems that identify exploitation attempts in real-time.

Employees make thousands of micro-decisions daily - which requests to approve, which callers to assist, which emails to trust. Under cognitive load, our mind relies on shortcuts: recognizing patterns, trusting authority, cooperating with urgency. This mental efficiency enables productivity but creates exploitable vulnerabilities.

Social engineers recognize these patterns as system vulnerabilities. They know people trust familiar patterns, defer to authority under pressure, and prioritize helping over verification. When performance metrics demand speed, verification steps get skipped. Traditional security tools miss these attacks entirely: firewalls cannot detect emotional manipulation, and endpoint protection cannot flag authority exploitation.

Behavioral Patterns That Reveal Manipulation

Manipulation attempts create detectable signatures. Requests bypassing normal verification, especially justified by urgency or authority, indicate potential exploitation. Quarter-end deadlines, executive requests, and customer emergencies become vulnerable decision points.

Communication patterns expose manipulation. Multiple simultaneous contacts across channels overwhelm cognitive capacity. Sequential interactions building trust through small requests before major asks follow documented influence techniques. Unnecessary urgency, sympathy appeals, and fear indicators signal social engineering.

Timing anomalies suggest exploitation of operational rhythms. Attacks cluster around high-pressure periods when vigilance drops - attackers understand organizational psychology and time their manipulation accordingly.

Building Human-Aware Detection Systems

Effective detection monitors human communication with the same rigor applied to network traffic. Natural language processing identifies manipulation markers: urgency without justification, authority claims without verification, and emotional appeals in business context. Combined with behavioral anomalies, these linguistic patterns define exploitation attempts.

Context correlation strengthens detection. When unusual communication patterns coincide with authentication attempts, privilege requests, or payment processing, the combination signals active manipulation. Behavioral baselines by role and department distinguish legitimate urgency from manufactured pressure.

Real-time decision support helps employees under attack. Human Threat Detection and Response systems analyze ongoing conversations using NLP to detect urgency clustering, authority stacking, and emotional manipulation, prompting verification when exploitation patterns emerge. Rather than expecting humans to detect sophisticated deception alone, technology assists at key decision points in a process.

Critical insight. Focus detection on behavioral patterns, not individual mistakes. Create systems that support human decision-making under pressure rather than blaming human failure after compromise.

Attackers will continue exploiting human psychology because it's predictable and profitable. Organizations that monitor and support human behavior as carefully as technical infrastructure detect manipulation before it succeeds.

Recommended Actions

Immediate steps: Identify high-risk decision points where employees approve access, payments, or data sharing. Monitor communication patterns around these critical moments.

Implementation resources:

  • NIST SP 800-63 guidelines on social engineering resistance
  • Carnegie Mellon's Insider Threat Center behavioral indicators
  • Human factors analysis frameworks from aviation safety
  • Human Threat Detection and Response platforms for behavioral monitoring
  • Academic research on influence techniques and resistance training

Enter your work email and we'll reach out to schedule the demo

Oops! Something went wrong while submitting the form.