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What is C9Phish?

C9Phish, created by C9Lab, is a robust platform designed for phishing simulation and training that helps organizations identify, evaluate, and address cybersecurity risks related to phishing attacks. By mimicking real-world phishing situations, C9Phish enables companies to assess employee awareness, uncover vulnerabilities, and strengthen their overall security posture. This platform allows security teams to craft customized phishing campaigns that utilize a variety of attack techniques, including email, SMS, and social engineering, thereby fostering a realistic training environment. In addition, its advanced analytics and reporting capabilities enable organizations to track employee responses, assess risk levels, and identify specific teams or individuals who may need further training. Moreover, C9Phish includes automated training modules that provide targeted education and constructive feedback to employees who are tricked by these simulated attacks, preparing them to detect and avoid future threats. Overall, this holistic strategy not only enhances employee awareness but also significantly bolsters the organization’s defenses against potential cyber threats, ensuring a proactive approach to cybersecurity. As a result, organizations can cultivate a culture of security that is vital in today’s digital landscape.

What is Barracuda Sentinel?

Emerging as significant security threats, business email compromise (BEC), spear phishing, and account takeover present serious challenges for organizations in today’s digital environment. These attacks are meticulously crafted using social engineering tactics to deceive employees, which can lead to substantial operational and reputational harm. Barracuda Sentinel provides an advanced cloud-based solution that integrates artificial intelligence with Microsoft Office 365, thereby offering comprehensive defense against BEC, account takeover, spear phishing, and various cyber frauds. At the heart of Barracuda Sentinel is its advanced AI engine, which not only identifies and addresses socially engineered threats in real-time but also highlights employees who may be particularly susceptible. Its unique API-driven framework allows the AI to scrutinize past email interactions, gaining insights into each user’s specific communication patterns. Utilizing a variety of classifiers, the system effectively charts the social connections of individuals within the organization, thereby bolstering the overall security infrastructure. As a result, companies can enhance their asset protection and uphold their credibility in an increasingly perilous cyber landscape. This proactive approach not only mitigates risks but also fosters a culture of security awareness among employees.

Media

Media

Integrations Supported

Additional information not provided

Integrations Supported

Additional information not provided

API Availability

Has API

API Availability

Has API

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

C9Lab

Date Founded

2022

Company Location

India

Company Website

c9lab.com/products/c9phish/

Company Facts

Organization Name

Barracuda

Date Founded

2004

Company Location

United States

Company Website

www.barracuda.com/resources/Barracuda_Sentinel_DS_US

Categories and Features

Categories and Features

Anti-Spam

Allow / Block List
Audit Log
Email Archiving
Email Recovery
Email Routing
Encryption
Fraud Detection
Quarantine
Reporting & Statistics
Spam Filter

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