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HyperproofHyperproof is a compliance operations platform designed to help organizations replace reactive, manual audit prep with a continuous, well-managed program. Instead of running each regulatory framework as its own project, Hyperproof lets teams map a single control across 160-plus frameworks, so the evidence and testing already completed for one standard can count toward another, removing duplicate effort as new regulations come into scope. For business and compliance leaders, the value shows up in measurable operational gains. Customers point to a 70% lift in overall compliance productivity, close to $150K trimmed from yearly control-orchestration costs, and a 66% drop in the duplicative control work that piles up when frameworks aren't mapped together. On average, audit prep shrinks by roughly 350 hours annually. The platform's risk register gives risk owners across the business a shared place to document risks and treatment plans, giving leadership better visibility into where the organization stands at any given time rather than reconstructing that picture ahead of every audit. Hyperproof is also built to flex with organizational complexity, supporting companies with multiple business units or subsidiaries that need to scope compliance programs differently across entities rather than manage a single flat structure. Combined with integrations into commonly used business and IT systems, teams can keep compliance work embedded in their existing processes instead of managing it as a parallel, disconnected effort. Headquartered near Seattle and founded in 2018, Hyperproof is the compliance platform of choice for organizations such as Reddit, Fortinet, Appian, Outreach, and Thales as they professionalize compliance operations and reduce the resourcing burden of staying audit-ready. Best fit: compliance, risk, and operations leaders at mid-market and enterprise organizations managing regulatory complexity across multiple frameworks or business units.
What is Mindgard?
Mindgard stands at the forefront of cybersecurity for artificial intelligence, focusing on the protection of AI and machine learning models, including large language models and generative AI, for both proprietary and external applications. Founded in 2022 and drawing on the academic expertise of Lancaster University, Mindgard has swiftly emerged as a significant force in addressing the intricate vulnerabilities that come with AI technologies. Our primary offering, Mindgard AI Security Labs, exemplifies our commitment to innovation by automating the processes of AI security evaluation and threat identification, effectively uncovering adversarial risks that conventional approaches often overlook.
With the backing of the most extensive AI threat library available commercially, our platform empowers businesses to safeguard their AI resources throughout their entire lifecycle. Mindgard is designed to seamlessly integrate with existing security frameworks, allowing Security Operations Centers (SOCs) to efficiently implement AI and machine learning solutions while effectively managing the unique vulnerabilities and risks associated with these technologies. In this way, we ensure that organizations can not only respond to threats but also anticipate them, fostering a more secure environment for their AI initiatives.
What is Codename MDASH?
Codename MDASH refers to a sophisticated code scanning tool that is incorporated into Microsoft Defender, utilizing a multi-modal AI framework to identify, confirm, and resolve vulnerabilities with far greater depth than traditional static analysis techniques. It enhances the Defender CLI through a multistage method where dedicated agents collaborate across four distinct phases. First, the preparation phase sorts files by risk level, applying call-graph analysis and assessing code complexity to pinpoint functions most likely to harbor vulnerabilities. Next, the scanning phase delivers this prioritized code to over 100 specialized agents, each concentrating on specific types of vulnerabilities such as injection flaws, memory safety concerns, and authentication bypasses, ensuring targeted assessments. The subsequent validation phase incorporates taint analysis and type resolution via Language Server Protocol servers, in addition to a deliberation among multi-model agents, to bolster confidence levels and reduce false positives effectively. Ultimately, the deduplication step consolidates overlapping findings into a coherent set of unique, actionable results, thereby improving the overall efficacy of vulnerability management strategies. This groundbreaking methodology marks a transformative shift in how threats are detected and remediated during the software development lifecycle. As a result, organizations can better safeguard their applications against emerging security risks.
Integrations Supported
MAI-Cyber-1-Flash
API Availability
Has API
API Availability
Has API
Pricing Information
Free
Test drive the Mindgard AI Security Labs platform in our controlled, sandbox environment — Zero commitments.
Free Version
Free Trial Offered?
Pricing Information
Pricing not provided
Free Version
Free Trial Offered?
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
Mindgard
Date Founded
2022
Company Location
United Kindom
Company Website
mindgard.ai/
Company Facts
Organization Name
Microsoft
Date Founded
1975
Company Location
United States
Company Website
learn.microsoft.com/en-us/security-exposure-management/ai-code-security-overview
Categories and Features
Cybersecurity
AI / Machine Learning
Behavioral Analytics
Endpoint Management
IOC Verification
Incident Management
Tokenization
Vulnerability Scanning
Whitelisting / Blacklisting