Compare Backslash Security vs. Scanner

Backslash Security is the governance and visibility platform built for organizations where AI coding tools are already part of how software gets built. GitHub Copilot, Cursor, Windsurf, Claude Code, and Gemini CLI have fundamentally changed the development lifecycle — and the security controls most organizations rely on were not designed for this environment. Backslash provides a comprehensive AI coding tool inventory and policy enforcement across the full AI coding spectrum, giving security teams visibility into every active tool and the risk introduced before it reaches production. This includes vibe coding security — risk detection purpose-built for vulnerability patterns in AI-generated code that traditional scanners are not equipped to catch. As AI coding agents grow more capable, they increasingly operate with access to external services, internal data, and organizational infrastructure through MCP servers. Over-permissioned agents and misconfigured MCP connections create data leakage pathways — exposing sensitive organizational data to AI models without security team awareness or enforcement controls. These are active exposure points, not theoretical risks. Backslash addresses this directly. The platform maps every MCP server connection, identifies over-permissioned AI agent configurations, and enforces least-privilege access before data leakage occurs. Security teams gain full visibility into what AI agents can access and where permissions exceed what the task requires. For security leaders governing an environment that moved faster than their controls, Backslash is the missing layer — built from the ground up for AI-native development, not retrofitted from a previous generation of tooling.

Scanner.dev is a cloud-based security data lake and a streamlined security information and event management (SIEM) platform that allows users to index logs directly into their Amazon S3 storage, thereby enabling the retention of unlimited logs and facilitating full-text searches across vast amounts of data in mere seconds, all without the need for additional ETL processes or schema setups. With its lightweight indexing system, any log format can be made immediately searchable, and it offers rapid search capabilities, ongoing threat detection through customizable detection rules that can be managed as code via GitHub, and seamless alerting with APIs for automation and existing security workflow integration. The platform's streaming detection engine constantly assesses rule queries in nearly real time and is equipped to backtest detection logic using historical data. Furthermore, its API and Model Context Protocol (MCP) not only provide programmatic access but also allow for AI-assisted evaluation of security data, enhancing the overall security analysis process. This robust architecture ensures that organizations can effectively manage and respond to security threats with agility and precision.