DevOps

Mend.ai Product Analysis Report

Last updated on Jul 7, 2025
Joel Witts
Laura Iannini
Written by Joel Witts Technical Review by Laura Iannini
Schedule a Demo Learn More

Fast Facts

  • Company HQ: Givatayim, Israel (Corporate); Boston, Massachusetts, USA (North American)
  • Number of Employees: 201–500 (estimated as of April 2025)
  • Ownership: Private
  • Investment: Raised $123.6 million across four funding rounds, including $75 million Series D in April 2021
  • Founded: 2011 (rebranded from WhiteSource to Mend.io in May 2022)

Our Analysis

The Application Security (AppSec) market, projected to reach $47.3 billion by 2033 with a 19.4% CAGR, is driven by the rise of open-source software, containerized deployments, and AI-driven development. Organizations adopting DevSecOps face challenges securing complex software supply chains, proprietary code, and AI components, without slowing development cycles.

As DevSecOps adoption accelerates, organizations face challenges securing open-source dependencies, containers, proprietary code, and AI-driven applications across the Software Development Lifecycle (SDLC)

Mend’s Approach

Mend.io’s AI-Native Application Security Platform is designed for security teams and developers, across mid-to-large enterprises, particularly in the technology and finance sectors. Mend.io offers a suite of tools—Mend AI, Mend SAST, Mend SCA, Mend Containers and Mend Renovate —to integrate application security with development workflows.

Mend AI, a standalone component of the Mend AppSec Platform, is purpose-built to secure AI-powered applications for mid-to-large enterprises, particularly in technology and finance sectors.

Unlike legacy tools focused on static code analysis, Mend AI targets AI models and frameworks, addressing unique risks such as data exposure, malicious models, and compliance challenges through comprehensive inventory, risk insights, red-teaming, system prompt hardening, and governance. 

Integrated with CI/CD pipelines and IDEs (e.g., Azure DevOps, VS Code), Mend AI provides a unified dashboard with risk and compliance metrics (e.g., 57 high-risk issues detected on 01/05/2025) and flat-rate pricing per developer for unlimited scans. While scalable, AI-driven features may require human oversight for accuracy, targeting DevSecOps teams needing robust AI security. Let’s dive into some of the key features:

  • AI Component Inventory Management: Mend AI discovers all AI models and frameworks, including Shadow AI, creating a continuously updated AI Bill of Materials (AI BoM). This enables developers to identify unauthorized or outdated AI components early in the SDLC, reducing integration risks, while security teams gain visibility to enforce policies, critical for mitigating attack surface expansion in AI-driven applications. For example, detecting Shadow AI prevents unapproved models from introducing vulnerabilities, ensuring compliance with standards like OWASP, vital for enterprise trust.
  • AI Component Risk Insights: Mend AI enriches components with license checks, public security vulnerabilities, and malicious package detection using a proprietary database. Developers benefit from actionable insights to select secure, compliant AI libraries during coding, minimizing rework, while security teams use these insights to prioritize vulnerabilities and ensure regulatory compliance (e.g., GDPR, HIPAA). This is essential for enterprise AI deployments, where non-compliant or malicious models could lead to legal or security breaches.
  • AI Red-Teaming(Behavioral Risks ): Mend AI simulates adversarial conversations with prebuilt, customizable playbooks, testing for prompt injection, context leakage, biases, and data exfiltration. Developers can refine AI models early by addressing behavioral vulnerabilities, enhancing application reliability, while security teams proactively mitigate risks like data leaks, crucial for conversational AI in sensitive sectors like finance. Recent tests identified 57 high-risk issues, demonstrating Mend AI’s ability to uncover threats traditional tools miss, per provided information.
  • System Prompt Hardening: Mend AI scans system prompts for security best practices, recommending secure rewrites to prevent misuse and data leakage. Developers integrate these rewrites directly in IDEs, streamlining secure coding practices and reducing vulnerabilities in AI interactions, while security teams ensure prompts align with organizational policies, protecting sensitive data in AI-powered applications. This is vital for preventing exploits like prompt injection, which could compromise customer data in enterprise settings.
  • Proactive Policies and Governance: Mend AI’s policy engine enforces rules, blocks unapproved AI components, and automates governance workflows across the SDLC. Developers work within approved AI model boundaries, reducing compliance violations, while security teams automate policy enforcement, ensuring consistent risk management across thousands of repositories. This supports enterprise AI adoption by aligning with regulatory requirements and minimizing manual oversight, as highlighted by client quotes from Vonage on compliance and speed.

Mend.io’s platform aims to shift security left, enabling developers to address vulnerabilities early while supporting security teams with governance and compliance for complex DevSecOps environments.

Market Position

Mend AI is a challenger solution in the application security market, entirely focused on AI risks, offering AI-native features for enterprises like Google, Microsoft, and Comcast. Founded in 2011, this Israeli vendor is a credible player to watch, recognized for innovative AI security and partnerships with AWS, Microsoft, and Red Hat.

Use Cases

  • Identifying AI components: Mend AI discovers all AI models and frameworks, including Shadow AI, enabling DevSecOps teams to gain visibility into AI usage, critical for mitigating risks in AI-powered applications.
  • Assessing AI compliance and risks: Mend AI provides license, security, and malicious package insights, helping security teams ensure compliance and address vulnerabilities, essential for AI-driven enterprise development.
  • Conducting AI red-teaming: Mend AI runs tests for prompt injection, context leaks, and exfiltration, allowing DevSecOps teams to identify behavioral AI risks, vital for securing enterprise applications.
  • Hardening system prompts: Mend AI detects weak prompts and suggests secure rewrites, enabling security teams to prevent AI misuse, crucial for protecting sensitive data in AI applications.
  • Governing AI usage: Mend AI enforces policies and automates governance workflows, helping DevSecOps teams manage AI risks and ensure compliance, key for enterprise AI deployments.

Strengths

  • Comprehensive AI coverage: Full AI BoM, risk data, policy engine, system prompt hardening, and red-teaming in one workflow.
  • Detailed application analysis: Clicking a library reveals vulnerabilities, impact analysis, and recommended fixes, with filters for severity, reachability, and exploits, aiding prioritization for security teams.
  • Extensive integrations: Mend.io’s partner network, including AWS, Microsoft, and Red Hat, enables seamless integration with CI/CD pipelines, IDEs, and repositories, enhancing DevSecOps workflows.
  • Flexible pricing model: Flat-rate pricing per developer, with no restrictions on code size or scan frequency, supports scalability for enterprises with diverse application portfolios.
  • Unified dashboard: Displays metrics from all Mend solutions (containers, code, dependencies), with toggleable views, labels for specific applications, and insights into findings, remediations, and high-risk projects.
  • Comprehensive reporting: Generates reports from static analysis, SCA, or container scans, providing actionable data for compliance and risk management in enterprise environments.

Cautions

  • AI-focused applicability: Mend is best suited for enterprise teams actively using AI models in their applications, seeking to reduce AI-powered application risks, potentially limiting relevance for non-AI-focused teams.

Summary

Mend AI fills critical gaps in application security by addressing AI-specific risks that traditional tools, focused on static code, fail to detect. Legacy solutions often overlook Shadow AI, malicious models, and behavioral vulnerabilities like prompt injection, leaving enterprises vulnerable to data exposure and compliance issues. Mend AI, a standalone component of the Mend AppSec Platform, targets these challenges with AI-native tools for inventory, risk insights, red-teaming, system prompt hardening, and governance, ensuring secure AI-powered applications for technology and finance sectors.

Mend.io is trusted by enterprise clients, includingGoogle and Siemens. Its specialized focus on AI security positions it to disrupt the AppSec market, particularly for DevSecOps teams needing compliance and risk management without sacrificing development speed.

We were impressed by Mend AI’s comprehensive workflow, integrating AI Bill of Materials, risk data, policy enforcement, red-teaming (e.g., 57 high-risk issues detected on 01/05/2025), and prompt hardening into a user-friendly dashboard. Seamless CI/CD and IDE integrations (e.g., Azure DevOps, VS Code) and flat-rate pricing per developer enhance usability, making Mend AI a compelling choice for enterprises prioritizing AI security.

Read Further

Schedule a Demo Learn More
Written By Written By
Joel Witts
Joel Witts

Joel is the Director of Content and a co-founder at Expert Insights; a rapidly growing media company focussed on covering cybersecurity solutions. He’s an experienced journalist and editor with 8 years’ experience covering the cybersecurity space. He’s reviewed hundreds of cybersecurity solutions, interviewed hundreds of industry experts and produced dozens of industry reports read by thousands of CISOs and security professionals in topics like IAM, MFA, zero trust, email security, DevSecOps and more. He also hosts the Expert Insights Podcast and co-writes the weekly newsletter, Decrypted. Joel is driven to share his team’s expertise with cybersecurity leaders to help them create more secure business foundations.

Tested by Tested by
Laura Iannini
Laura Iannini Cybersecurity Analyst

Laura Iannini is a Cybersecurity Analyst at Expert Insights. With deep cybersecurity knowledge and strong research skills, she leads Expert Insights’ product testing team, conducting thorough tests of product features and in-depth industry analysis to ensure that Expert Insights’ product reviews are definitive and insightful. Laura also carries out wider analysis of vendor landscapes and industry trends to inform Expert Insights’ enterprise cybersecurity buyers’ guides, covering topics such as security awareness training, cloud backup and recovery, email security, and network monitoring. Prior to working at Expert Insights, Laura worked as a Senior Information Security Engineer at Constant Edge, where she tested cybersecurity solutions, carried out product demos, and provided high-quality ongoing technical support. She holds a Bachelor’s degree in Cybersecurity from the University of West Florida.