TLDR: Lookout, a leader in mobile threat defense, has launched Smishing AI, an industry-first solution leveraging large language models (LLMs) to combat sophisticated SMS phishing (smishing) attacks. This AI-powered technology analyzes message intent and context, moving beyond traditional indicators to protect enterprises from social engineering campaigns increasingly fueled by generative AI. The solution emphasizes privacy by design, with strict safeguards for user data.
BOSTON – September 10, 2025 – Lookout, Inc., a prominent figure in mobile threat defense, today announced the introduction of Smishing AI, a groundbreaking AI-powered solution designed to protect enterprises from the escalating threat of SMS phishing, commonly known as ‘smishing’ attacks. This innovative offering marks a significant advancement in cybersecurity, specifically targeting the human layer of defense on mobile devices.
Smishing, a form of cyberattack where fraudsters send deceptive text messages to trick individuals into divulging personal information, has become a pervasive threat. These messages often impersonate trusted entities like banks, delivery services, or government agencies, aiming to steal sensitive data such as login credentials and credit card details, or to install malware. The rise of generative AI has further exacerbated this challenge, enabling attackers to craft increasingly convincing and large-scale social engineering campaigns, making mobile devices prime targets.
Lookout Smishing AI distinguishes itself by utilizing large language models (LLMs) to evaluate the intent and context of messages. This capability allows the solution to go beyond conventional security indicators, such as malicious URLs or spoofed senders, enabling organizations to detect and prevent threats that bypass traditional security tools. Firas Azmeh, President of Endpoint Security at Lookout, emphasized this shift, stating, “Generative AI has made it easier for attackers to execute large-scale campaigns, making smishing one of the most common social engineering threats businesses face today. With Smishing AI, we’re redefining mobile threat defense by focusing on the actual root cause—the intent of the message. This innovation showcases our AI-first security approach, enabling customers to protect their workforce against attacks that target the human layer.”
The company highlights its ‘AI-first security’ approach, which integrates artificial intelligence at the core of threat detection, analysis, and response. This strategy learns from vast datasets to identify patterns and anomalies that rule-based systems might miss, providing real-time insights and adaptive defense against an ever-evolving threat landscape. Zeus Kerravala, Principal Analyst at ZK Research, commented on the solution’s proactive nature: “As generative AI enables attackers to craft increasingly convincing social engineering campaigns, Lookout is raising the bar for mobile threat defense, with Smishing AI, they’re not just reacting to threats, they’re anticipating them.”
Privacy and user trust are central to Smishing AI’s design. Lookout has implemented strict privacy safeguards, including explicit opt-in requirements for both administrators and end-users. The system automatically excludes trusted contacts, analyzing only messages from unknown senders. Personal information is de-identified to prevent exposure, and a zero-retention policy with the LLM service ensures message content is never stored or used for training purposes.
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The urgency for such a solution is underscored by recent data. Lookout’s research indicates a 328% increase in smishing attacks, with a 70% year-over-year increase in mobile phishing and malicious web content in Q2 2024. Mobile devices, now cornerstones of enterprise productivity and identity verification, have become critical initial targets in the modern cyber kill chain. Lookout Smishing AI aims to provide a crucial layer of defense against these sophisticated, human-targeted attacks, protecting against credential theft and unauthorized access to sensitive corporate data.
