How employee uncertainty can result in successful cyberattacks 

Uncertainty in email threats, especially threats like phishing come from subtle ambiguities that exploit human psychology. These ambiguities are used by threat actors to gain access to internal systems. Phishing specifically can be found to trigger subconscious automatic responses by disguising attacks as legitimate requests. It causes victims to be filled with trust rather than scrutiny, as things like senders' identities seem familiar yet mismatched. 

The study ‘Understanding factors that influence unintentional insider threat: a framework to counteract unintentional risks’ offers another example of these uncertainties as, “There’ll be certain slight differences between the logos and stuff and the branding that they use… they might use an old, outdated one.”

That same study shows how people rely on experience-based pattern matching rather than deliberate analysis, with one participant explaining, “If it’s from an address I do know, you know, that I recognise, if it’s in a way that the person wouldn’t normally sort of write or interact with me, then that’s big red flags there… but even if it does, then you put on that, sort of, other lens of ‘Okay, does this look credible?’”

Uncertainty vs. deception

Many phishing emails succeed because they feel plausible, not because they are clearly fraudulent. The message looks just real enough to trigger trust or urgency before careful thinking kicks in. Under pressure, people default to fast decisions. Heavy workloads narrow attention, so recipients focus on emotional cues like “your account will be closed” and miss quieter warning signs. 

Research from Frontiers in Psychology on social engineering explains why this works so consistently: “Social engineering cyberattacks are a kind of psychological attack that attempts to persuade an individual (i.e., victim) to act as intended by an attacker… Most social engineering cyberattacks are crafted to trigger subconscious, automatic responses from victims while disguising these attacks as legitimate requests.”

When emails leave gaps or feel ambiguous, people naturally fill in the blanks with assumptions. Persuasion techniques push this even further, nudging users toward a “better safe than sorry” response that favors action over analysis. Email security tools struggle here, as machine learning works best on clear patterns, not gray areas where impersonation tactics constantly shift, or new payloads appear. 

Even highly effective filters let a small number of these messages through, and at scale, that is enough. Stress makes the problem worse by narrowing focus and weakening judgment, while individual traits like impulsivity or low attention to detail increase risk, even when no explicit lie is present.

Uncertainty in sender identity

Uncertainty around who actually sent an email is one of the biggest reasons phishing works. Attackers rarely rely on obvious lies, instead making small, deliberate tweaks that slip past quick judgment. The sender's name looks familiar. The domain looks close enough. Maybe the “From” address matches a real brand, while the “Reply-To” quietly points somewhere else. 

Most people never notice the mismatch because the brain fills in the gaps and assumes legitimacy based on recognition alone. A misspelled PayPal domain or a near-perfect company name feels safe at a glance, especially when the logo looks right.

Visual trust overrides careful checking. Cloned branding, copied signatures, and authority cues, like messages that appear to come from executives or public figures, often succeed even when small errors are present. A Security Journal article describes phishing as “the use of unsolicited email… purportedly from a legitimate company requesting personal, financial, and/or login credentials,” which helps explain why surface-level realism matters more than technical accuracy.

Under time pressure or heavy workloads, people focus on what feels familiar and urgent, not on technical details hidden in headers. Even experienced users can struggle with modern phishing emails that include clean grammar, realistic formatting, and authentic-looking signatures.

When SPF or DKIM checks fail, the result is not always a clear warning but uncertainty. The message does not scream “fake.” It just looks slightly off. 

Uncertainty in message intent

Uncertainty about what an email means often causes more trouble than what it actually says. When someone fails to reply, skips a greeting, or ignores a request, the intent is unclear. Was it rude on purpose, or did the person simply miss the message? 

People cycle through explanations like maybe the sender is busy, maybe they forgot, or maybe the silence is intentional. As one Journal of Occupational Health Psychology study noted, “Passive email incivility is viewed as more ambiguous” and can leave recipients unsure of whether an omission was deliberate or accidental.

There is a clear split between passive and active incivility. Passive behavior consistently scores higher on ambiguity, while active behavior is strongly linked to emotional intensity. In simple terms, people feel confused by silence, but they feel attacked by obvious rudeness. The data confirms that these are two different experiences, and the uncertainty created by passive behavior lingers longer because it never quite resolves itself.

Uncertainty in content and language

Uncertainty in emails often stems from vague wording, missing information, or unclear context, particularly in passive-aggressive behavior and phishing attempts. Passive incivility manifests in short, neutral, or incomplete messages, leaving recipients uncertain whether the sender is being intentionally rude, simply busy, or trying to be efficient. Active incivility, by contrast, is obvious: all-caps, insults, or aggressive language make the emotional intent clear. 

The Journal of Occupational Health Psychology study confirms this distinction, with analyses showing that ambiguity in messages (for example, “This email required clarification”; α = .76) is separate from emotional intensity (α = .79), and that passive emails can create stress because recipients are left guessing in the absence of face-to-face cues.

Phishing emails take advantage of this uncertainty, mixing subtle grammatical mistakes, urgent language like “immediate action required,” and inconsistent terminology to make scams look legitimate. People often read these messages and make assumptions rather than detect the deception, especially under time pressure or mental load.

Uncertainty in context and timing

Uncertainty around when and why an email arrives makes it much harder to judge whether it’s a threat. Situational pressures and timing ambiguities can cloud rational thinking and make people more vulnerable to phishing. Phishing emails take advantage of this, blending familiar logos and language into stressful moments, so recipients focus on surface details instead of checking carefully.

When emails hit at high-stress times, mistakes increase. People who act on impulse are more likely to click something malicious, while those who tend to think things through only do better if they aren’t rushed. 

The above mentioned study explains that passive email rudeness can leave recipients in limbo: “Behaviors such as ignoring a request that was made through email … represent omission of respectful treatment.” When someone fails to respond or sends a very curt reply, it’s often impossible to know whether the omission was intentional or accidental.

Why humans struggle most with uncertain emails (and the clear solution)

When cognitive load is high, recipients tend to misread the sender’s intentions, swinging between assuming malice and excusing oversight.

Research from the Cambridge University Press shows a similar dynamic in workplace communication: one participant described email tone as particularly confounding, saying, “Email is just so different, and it’s kind of hard. I mean, are they throwing in an exclamation point there because they are making their point? Was that intentional or not, you know? It’s hard to read through the lines.”

Phishing emails exploit exactly this uncertainty as small signals like urgency, slightly off-branding, or inconsistent phrasing often slip past attention when people are under time pressure, and instinctive judgment favors familiar cues over scrutiny.

Generative AI solutions like Paubox tackle these challenges by analyzing emails in real time and identifying subtle cues that humans miss. By looking at context, language patterns, and historical data, AI can flag uncertain messages before they cause confusion or stress. Machine learning models detect anomalies like spoofed domains or vague phrasing, adaptively learning from vast datasets of phishing attempts.

See also: HIPAA Compliant Email: The Definitive Guide (2025 Update)

FAQs

What is a phishing email?

A phishing email is a malicious message sent to trick employees into revealing sensitive information or downloading harmful content.

How can an organization identify phishing emails?

Organizations should look for unusual sender addresses, urgent requests, inconsistent branding, suspicious links, or poor grammar.

What actions should employees take when they receive a phishing email?

Employees should avoid clicking links or opening attachments, report the email to the IT/security team, and delete it.