How Voice-Based Attacks Bypass Your Security Controls

Strategic Security Gaps

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 Voice Channel Security Gap

Voice communication occurs in a security blindspot. While organizations meticulously scan emails and monitor network traffic, phone conversations go unexamined. Call logs capture who called when but miss the critical element of what was said and how it was said.

Social engineering can hide between these lines, with the email-voice security gap creating the perfect attack vector.

Social engineers exploit this blindspot through sophisticated manipulation. They create false urgency requiring immediate password resets. They impersonate executives demanding special access. They pose as vendors needing system credentials.

Each attack succeeds because no security tool analyzes the conversation itself, where deception occurs.

Voice calls demand immediate responses, preventing verification. Skilled attackers read hesitation, adapt instantly, and pivot between pretexts until they find what works.

Detecting Deception in Real-Time Conversations

Voice attacks create detectable patterns: policy violation requests. Emergency password resets, MFA bypasses, and privilege escalations often come wrapped in elaborate explanations why normal procedures cannot apply.

Identity inconsistencies reveal pretexting attempts. When claimed executives unfamiliar with basic company terminology, or supposed vendors cannot reference actual contract details, you're likely hearing rehearsed scripts rather than legitimate callers. Attackers possess surface knowledge from reconnaissance but lack deeper organizational understanding that emerges through natural conversation.

Behavioral patterns expose manipulation tactics. Resistance to callback verification, claims that standard authentication is unavailable, or disconnections when transferred to supervisors all indicate deception. Voice stress analysis and conversational cues can detect emotional manipulation—artificial urgency, fear appeals, or sympathy are designed to bypass rational evaluation.

Building Real-Time Voice Detection

Implement call recording for security-sensitive functions: IT help desk, service desk, legal, finance. Converting speech to text for analysis, allows for both real-time detection and post-incident investigation. This foundational visibility transforms voice from a blindspot into a monitored channel.

Deploy conversation analysis tools that evaluate both what is said and how it's said. Modern Human Threat Detection and Response platforms must analyze linguistic patterns, emotional indicators, and request anomalies during active calls. These systems alert security teams when conversations exhibit deception markers, enabling intervention before access is granted.

Integrate voice intelligence with existing security tools. SIEM correlation should immediately flag calls preceding authentication failures or suspicious logins. This integration reveals coordinated attacks invisible to isolated monitoring.

Organizations monitor every packet crossing their network but ignore conversations granting network access. Real-time voice analysis closes this gap, detecting social engineering where it actually happens—in human conversation.

Recommended Actions

Immediate steps: Enable call recording for IT support and help desk. Create basic SIEM alerts when calls precede authentication anomalies.

Implementation resources:

NIST SP 800-53 controls for voice communication security
SANS Voice Over IP security white papers and resources
Human Threat Detection and Response platforms for real-time analysis
CallMiner or Cogito for conversation intelligence
Your existing SIEM for cross-channel correlation

Process improvements: Establish callback verification for sensitive requests. Create escalation triggers when callers resist authentication. Train staff to recognize emotional manipulation.

Authority and Urgency in Social Engineering Attacks

Authority claims plus urgency create compliance pressure. Learn to detect and resist manipulation in real-time.

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 Voice-Based Attacks Bypass Your Security Controls

What is feature engineering

What’s more?

What is data engineering

What is feature engineering

1. Functions

2. Practise

3. Machine learning

The Voice Channel Security Gap

Detecting Deception in Real-Time Conversations

Building Real-Time Voice Detection

Recommended Actions

Implementation resources:

Authority and Urgency in Social Engineering Attacks

Detecting Reconnaissance and Deceptive Pretexting Before Attacks Begin

Implementing Cross-Channel Correlation for Social Engineering Detection

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