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Ratings and Reviews 0 Ratings
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Adaptive SecurityAdaptive Security was founded in 2024 by seasoned entrepreneurs Brian Long and Andrew Jones. Since inception, the company has raised over $50 million from top-tier investors including OpenAI, Andreessen Horowitz, and executives from Google Cloud, Fidelity, Plaid, Shopify, and other industry leaders. Adaptive defends organizations against sophisticated, AI-driven cyber threats such as deepfakes, vishing, smishing, and spear phishing. Its next-generation security awareness training and AI phishing simulation platform enables security teams to deliver ultra-personalized training that adapts to each employee’s role, access level, and exposure. This training leverages real-time open-source intelligence (OSINT) and features highly convincing deepfake content—including synthetic media of a company’s own executives—to mirror real-world attack vectors. Through AI-powered simulations, customers can continuously assess and improve organizational resilience. Hyper-realistic phishing tests across voice, SMS, email, and video channels evaluate risk across every major vector. These simulations are fueled by Adaptive’s AI OSINT engine, giving teams deep visibility into how attackers might exploit their digital footprint. Today, Adaptive serves global leaders like Figma, The Dallas Mavericks, BMC Software, and Stone Point Capital. With an industry-leading Net Promoter Score of 94, Adaptive is redefining excellence in cybersecurity.
What is Prism?
Prism is an innovative test-automation platform aimed at optimizing software testing across web, mobile, and API sectors. It offers a user-friendly web interface that carefully guides users through the setup process, enabling them to choose their desired testing framework while seamlessly incorporating all necessary components. Users can monitor the status of concurrent test executions across multiple platforms, browsers, devices, emulators, and simulators via a centralized dashboard. The platform also features advanced reporting powered by AI and machine learning, which includes more than 20 customizable widgets that provide detailed insights into both ongoing and historical test executions, allowing for thorough analysis of launch metrics, test coverage, failure trends, bug categorization, and recorded exceptions. To enhance the testing workflow, Prism utilizes a sophisticated machine-learning algorithm that reduces false positives and performs auto-analysis, pattern detection, and error classification, achieving an impressive 90% decrease in the workload associated with analysis. Moreover, the platform not only streamlines the testing process but also equips teams with the necessary information to make data-driven decisions, ultimately contributing to improved software quality and faster release cycles. This capability fosters a more efficient collaboration among team members, enhancing overall productivity and project outcomes.
What is Aspen Mtell?
Recognizing patterns in operational data is essential for anticipating deterioration and potential malfunctions well in advance. By implementing precise failure pattern identification, organizations can significantly reduce the occurrence of false positives that are often problematic in model-based methodologies. The use of advanced machine learning approaches enables a rapid differentiation between typical and atypical behaviors, which can lead to the activation of equipment protection measures within weeks rather than months. Additionally, the collaboration between Aspen Mtell and Aspen Cloud Connect™ allows for seamless access to devices operating under OPC UA, further enhancing analytical capabilities. This integration serves as a crucial line of defense against asset degradation by identifying early indicators of failure through operational data analysis. Moreover, incorporating AI-driven agent development improves current maintenance practices, enabling swift deployment of autonomous agents across multiple locations or even throughout an entire organization. With the focus on accurate failure pattern detection, businesses can greatly minimize the frequency of false positives often seen in conventional model-based approaches. By utilizing streamlined machine learning techniques, companies can quickly identify and respond to both standard and irregular activities, ensuring robust protection for their equipment and optimizing operational efficiency. This proactive approach ultimately fosters resilience and reliability in asset management strategies.
Integrations Supported
SAP Store
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
TO THE NEW
Date Founded
2008
Company Location
Singapore
Company Website
www.tothenew.com/solutions/Prism
Company Facts
Organization Name
Aspen Technology
Date Founded
1981
Company Location
United States
Company Website
www.aspentech.com/en/products/apm/aspen-mtell
Categories and Features
Automated Testing
Hierarchical View
Move & Copy
Parameterized Testing
Requirements-Based Testing
Security Testing
Supports Parallel Execution
Test Script Reviews
Unicode Compliance
Categories and Features
Preventive Maintenance
Condition Monitoring
Inspection Management
Maintenance Scheduling
Mobile Access
Predictive Maintenance
Purchasing
Reminders
To-Do List
Vendor Management
Work Order Management
