HUMAN Sightline Cyberfraud Defense vs. Devici

HUMAN Sightline Cyberfraud Defense is a cyberfraud prevention platform that helps organizations govern interactions from humans, bots, AI agents, and automated systems across web, mobile, and API channels. The platform is designed to determine which traffic and sessions are legitimate while identifying malicious automation, human-led fraud, abuse, and misuse. It addresses threats including account takeover, compromised accounts, fake account creation, content scraping, transaction abuse, data contamination, and risks associated with agentic AI. HUMAN analyzes behavior across the full session and multiple authentication stages rather than making decisions based only on individual requests or isolated events such as login and checkout. This layered approach allows the platform to correlate activity over time and build additional context around suspicious behavior. Adaptive AI models learn from detection and mitigation outcomes so defenses can adjust as attackers change tactics and infrastructure. High-fidelity decisioning helps security teams determine whether to allow, challenge, govern, or block specific interactions based on the available signals. Mitigation and governance capabilities are combined with reporting and investigation tools so teams can understand how threats were detected and respond to broader attack campaigns. HUMAN also provides AI-enhanced investigation capabilities intended to help security teams examine threat networks, study attacker behavior, and shorten response cycles. The platform is supported by HUMAN’s Satori Threat Intelligence Research Team, which contributes threat research and intelligence used across the company’s fraud and application protection capabilities.

Devici helps security and engineering teams build threat models that are consistent, reviewable, and easy to maintain as systems evolve. It focuses on capturing the architecture first, using a visual canvas where teams lay out processes, data stores, external entities, and trust boundaries. As components are described and attributes are applied, Devici identifies relevant threats and suggested mitigations from its built-in library, reducing the manual effort normally required to reason through diagrams or ad-hoc checklists. The tool is designed so that AppSec specialists, developers, and security champions can work together without specialized training. Real-time editing, in-model comments, and version history support collaborative reviews and allow teams to trace how decisions and risks change over time. Reusable templates and patterns make it simple to model common services or shared architecture pieces, helping larger organizations avoid duplicated work across teams. Devici also supports pushing mitigation tasks into existing development workflows through optional integrations, ensuring threat modeling outputs don’t remain isolated from day-to-day engineering. Its structure helps organizations move away from one-off diagrams and inconsistent documentation toward a predictable, repeatable approach that fits into modern development practices. Overall, Devici offers a practical way to raise the quality and clarity of threat modeling efforts without requiring complex onboarding or heavyweight tooling.