For embedded system software development
Integrated Solutions
From project management to design and verification for international standards compliance!
Automated AI red-team testing with coverage for AI code/model components and prompt security
Mend AI
Identify security and license risks in source code—including AI-generated code—as well as model- and package-level vulnerabilities, and automate attack scenarios such as prompt injection to analyze weak points.
Mend AI helps security teams proactively address emerging AI security risks without having to overhaul their existing approach. It continuously discovers, inventories, and operationalizes AI models and frameworks, detecting and evaluating risk factors in the context of each application. Based on these insights, security teams can effectively measure and prioritize AI-related threats alongside broader AppSec risks, and take remediation actions within a single, unified security management environment. With Mend AI, security teams gain visibility and control to easily expand security coverage, prevent AI sprawl, and maintain compliance with confidence.
Key Features
1
AI-BOM generation and component visibility
Automatically identify and manage AI models, frameworks, and libraries as an AI-BOM, enabling at-a-glance visibility into component-level risk and change history.
2
Prompt and system prompt safety validation
Proactively assess prompt injection, unintended behaviors, and policy violation risks to strengthen the stability and trustworthiness of AI applications.
3
Automated AI red-team testing
Automatically run red-team playbooks based on real-world attack scenarios to systematically validate and respond to AI-specific security threats.
4
Sensitive data exposure and data-permission risk detection
Detect potential exposure of personal/confidential data and excessive data access privileges to support both compliance and data protection.
Key Capabilities
1
AI component inventory
AI 컴포넌트 인벤토리 관리
• Automatically identify and manage all AI models and frameworks used in applications, providing a continuously updated inventory
• Make Shadow AI visible to proactively reduce AI-specific security risks
• Make Shadow AI visible to proactively reduce AI-specific security risks
2
AI component risk insights
AI 컴포넌트 리스크 인사이트
• Provide actionable insights by analyzing known risks such as embedded license issues, public vulnerabilities, and malicious packages within AI models
• Establish response strategies for identified risks to systematically strengthen the safety of AI-based applications
• Establish response strategies for identified risks to systematically strengthen the safety of AI-based applications
3
System prompt hardening
시스템 프롬프트 하드닝
• Strengthen system prompts by identifying security risks based on prompt content, structure, and misuse potential
• Automatically identify vulnerable code and inappropriate instructions within AI prompts to quickly control prompt-based attack risk
• Automatically identify vulnerable code and inappropriate instructions within AI prompts to quickly control prompt-based attack risk
4
AI red teaming
AI 레드 티밍
• Validate conversational AI application-specific risks through predefined tests and customizable scenarios
• Assess security posture against AI-specific threats such as prompt injection, context leakage, data exfiltration, bias, and hallucinations
• Assess security posture against AI-specific threats such as prompt injection, context leakage, data exfiltration, bias, and hallucinations
5
Proactive policies and governance
정책 기반 거버넌스 및 컴플라이언스
• Continuously apply AI governance across the SDLC through a strong policy engine and automated workflows
• Define and manage rules by AI component to ensure stable compliance with organizational security policies and regulatory requirements
• Define and manage rules by AI component to ensure stable compliance with organizational security policies and regulatory requirements
Industries
Use Cases
1
Automotive
• Manage AI-BOM and prompt safety together for ECU/IVI software that includes AI capabilities.
• Scan AI-generated code in the development pipeline to proactively block license and vulnerability risks.
• Link red-team results and remediation actions to quality gates for final releases.
• Scan AI-generated code in the development pipeline to proactively block license and vulnerability risks.
• Link red-team results and remediation actions to quality gates for final releases.
2
Aerospace & Defense
• Transparently identify AI components in embedded and ground systems with an AI-BOM.
• Automate prompt injection and data leakage scenarios to verify real weak points.
• Strengthen prompts based on Mend AI’s improvement recommendations.
• Automate prompt injection and data leakage scenarios to verify real weak points.
• Strengthen prompts based on Mend AI’s improvement recommendations.
3
Financial Services
• Validate prompt safety and data governance across chatbot and analytics-model services.
• Prioritize vulnerabilities and license issues in models and packages for immediate remediation.
• Retain red-team results and inspection history as audit evidence to meet compliance requirements.
• Prioritize vulnerabilities and license issues in models and packages for immediate remediation.
• Retain red-team results and inspection history as audit evidence to meet compliance requirements.
4
Semiconductor
• Document and track changes to AI components applied to design-support tools and internal portals using an AI-BOM.
• Scan AI-generated code during builds to validate license obligations and vulnerabilities simultaneously.
• Connect test results and remediation actions to change management and release approval workflows to maintain quality.
• Scan AI-generated code during builds to validate license obligations and vulnerabilities simultaneously.
• Connect test results and remediation actions to change management and release approval workflows to maintain quality.
5
Software & IT Services
• Manage security and license risks for AI capabilities across your services in a single workflow.
• Automatically scan AI-generated code and model components in CI to ensure quality.
• Provide a complete record of red-team → remediation → re-test for customer and audit responses.
• Automatically scan AI-generated code and model components in CI to ensure quality.
• Provide a complete record of red-team → remediation → re-test for customer and audit responses.
6
Healthcare
• Organize medical software models, plugins, and packages into an AI-BOM to ensure configuration transparency.
• Automate sensitive data extraction and prompt injection scenarios to reduce real-world risk.
• Use remediation history and re-test results to support 510(k) security documentation and post-market inquiries.
• Automate sensitive data extraction and prompt injection scenarios to reduce real-world risk.
• Use remediation history and re-test results to support 510(k) security documentation and post-market inquiries.
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