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What is Safer?

Safer is specifically created to address the viral spread of CSAM material on your platform, providing increased safety for your team, organization, and users. This innovative system enhances efficiency and promotes well-being among team members while fostering collaboration by breaking down barriers and leveraging community expertise. By employing sophisticated perceptual hashing and machine learning algorithms, it adeptly identifies both recognized and unrecognized CSAM. Any flagged content can be placed in a queue for review through moderation tools that prioritize employee wellness. Verified CSAM is carefully reviewed and reported, with all content securely archived in compliance with regulatory requirements. Moreover, Safer's protective measures are designed to identify both recognized and potentially new or unreported content right at the moment of upload. The Safer community works together to uncover more abuse materials, and our APIs are designed to enrich the collective knowledge surrounding child abuse content through the sharing of hashes, comparison with industry-standard hashes, and feedback regarding false positives. This collaborative initiative not only reinforces the battle against abuse but also promotes a proactive strategy to protect vulnerable individuals, ensuring that every effort counts in the ongoing fight for safety and justice. Ultimately, Safer represents a robust commitment to creating a safer online environment for everyone involved.

What is Binarly?

Identify and tackle both established and emerging vulnerabilities across the entire spectrum of the device and software supply chain. Our approach goes beyond merely matching binaries to a list of known vulnerabilities; we strive to gain a deeper understanding of code execution, enabling us to detect flaws that exist beyond the binary surface. By employing this methodology, we can identify broad categories of defects that extend beyond just known issues, doing so quickly and with an impressive accuracy rate that minimizes false positives. Our emphasis lies in uncovering both familiar and novel vulnerabilities, along with any potentially harmful activities, rather than depending solely on hash or signature evaluations. Our analysis encompasses more than just CVEs, allowing us to pinpoint vulnerabilities that can be found at the binary level. Moreover, by utilizing machine learning techniques, we significantly reduce alert fatigue, achieving a nearly flawless rate of false positives while simultaneously improving our detection prowess. This holistic strategy not only fortifies our defenses but also ensures that we stay ahead of a diverse array of security threats, enabling us to adapt and respond effectively to the ever-evolving landscape of cybersecurity challenges.

Media

Media

Integrations Supported

AWS Marketplace

Integrations Supported

API Availability

Has API

API Availability

Pricing Information

Pricing not provided

Pricing Information

Pricing not provided

Supported Platforms

SaaS

Supported Platforms

SaaS

Customer Service / Support

Web-Based Support

Customer Service / Support

Web-Based Support

Training Options

Documentation Hub

Training Options

Online Training

Company Facts

Organization Name

Safer

Date Founded

2019

Company Location

United States

Company Website

safer.io

Company Facts

Organization Name

Binarly

Date Founded

2021

Company Location

United States

Company Website

www.binarly.io

Categories and Features

Content Moderation

Not specified

Digital Governance

Not specified

Categories and Features

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