● Covered by 1 source · 1 reportMedium impact1 neutral
Technology Solutions Emerge to Combat Social Engineering, Including AI Deepfakes
Key points
- Social engineering is a primary attack vector, bypassing security training.
- AI deepfakes increase the sophistication of social engineering attacks.
- Netarx uses AI models to detect social engineering in live communications.
- Recent breaches like MGM Resorts and Brinks Home involved social engineering.
Social Engineering's Persistent Threat
Despite investments in security awareness training, social engineering continues to be a highly effective attack method. Unlike system vulnerabilities that can be patched, social engineering exploits human psychology, making traditional training an insufficient defense. The emergence of AI deepfakes further complicates detection, making these attacks more difficult to identify.
High-Profile Breaches Highlight Vulnerabilities
Recent incidents underscore the impact of social engineering. The 2023 Las Vegas casino breaches, attributed to Scattered Spider, involved vishing attacks that tricked help desk staff into resetting passwords and bypassing multi-factor authentication. MGM Resorts incurred an estimated $100 million in losses, while Caesars Entertainment reportedly paid a $15 million ransom. Similarly, the Brinks Home leak in August 2026, linked to ShinyHunters, used voice phishing to gain unauthorized access through a help desk employee, resulting in the exposure of nearly five million customer records and 41 gigabytes of corporate data.
Technological Solutions for Detection
Given the limitations of human detection, technology is increasingly being employed to identify both traditional and AI-generated social engineering attempts. Netarx offers a solution that utilizes a proprietary orchestration layer, described as an AI defense meta harness. This system correlates signals from over 40 AI models that continuously scan communications in progress.
Advanced Signal Analysis
Netarx's technology analyzes more than 1,000 digital and metadata signals for each interaction. For instance, to verify the authenticity of a device delivering a feed, the system examines device fingerprints, EXIF data, compression signatures, and location mismatches. For AI-generated voice communications from GANs or voice cloners, specific characteristics are analyzed to detect artificial origins.
✨ This summary was generated by AI from the outlets' reporting listed below. It is not independently verified and may contain errors — check the original sources. How BrevFeed works →
The daily brief
One email each morning: the day's tech stories, clustered across outlets and summarized. No account needed.
One email a day. Unsubscribe in one click, any time.
Today's brief
Spend a few minutes, get the whole day. Every topic's top stories in one hands-free rundown — listen, watch, or read the transcript.
▶ Play today's brief
New every morning, and the back catalogue is archived by date.
Reporting from
Social engineering remains a significant and successful attack vector, with traditional security awareness training proving ineffective against increasingly sophisticated methods, including AI deepfakes. New technological approaches are being developed to detect social engineering in live conversations, moving beyond human detection limitations. This shift addresses the rising financial and data loss from social engineering attacks, exemplified by recent high-profile breaches.