Problem: Automation is blind

Today's security automation focuses heavily on structured logs, IOCs, and signature matches. Attackers exploit this by targeting what automated systems overlook—the user's actual visual experience: fake login portals, convincing invoices, QR codes, and documents designed to fool human eyes but evade detection.

These visual threats rarely trigger traditional alerts. They succeed because SOC tools analyze data but ignore the visual content users encounter directly. This gap is a known blind spot exploited by attackers.

In fact, the human element is involved in 68% of breaches, according to the Verizon 2024 Data Breach Investigations Report often because users visually trust what they see, whether it’s a polished login page or a fake invoice. Attackers count on that trust to slip past automated defenses.

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Solution: Visual Detection with Qevlar Eye

Qevlar Eye brings visual intelligence into SOC pipelines, shifting detection from purely data-driven checks to visual-semantic analysis. Using Visual Language Models (VLMs), Qevlar Eye inspects exactly what the user sees—rendered webpages, documents, and screenshots—to assess risk.

Instead of relying on signatures, it identifies visual deception, brand spoofing, impersonation indicators, and subtle social engineering techniques, enriched with contextual factors (recipient's role, threat campaign, source behavior).

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Qevlar Eye detects threats such as:

• Credential phishing pages visually identical to corporate login portals
• Fraudulent invoices delivered as authentic-looking PDFs
• Fake internal portals undetected by signature-based checks

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Real Examples

Qevlar Eye examines actual visual threats:

Example 1 – Okta Phishing Portal

Domain clean, visually authentic.

→ Qevlar Eye detects brand impersonation, deceptive calls-to-action, subtle visual mismatches with corporate branding.

Example 2 – Fraudulent Invoice PDF

Passes static checks, no malicious links.

→ Eye identifies reused logos, altered banking details, and visual similarity to known fraud patterns.

Real-World Impact: The Visual Blind Spot

Analysis of over 1000 real-world cases from SOC investigations revealed:

30.6% of confirmed malicious visual threats were undetected by traditional Threat Intel sources (e.g., VirusTotal).

Qevlar Eye identified these threats through visual analysis alone—highlighting the practical limitations of current detection methods.

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Capability Comparison

Bottom Line

Security must evolve beyond logs and signatures. Attackers leverage visual deception, but Qevlar Eye equips SOCs to detect these threats automatically, catching visual tricks that traditional methods miss.