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Legit Security, a leader in AI-native application security posture management (ASPM), has introduced advanced Software Composition Analysis (SCA) and Static Application Security Testing (SAST) capabilities to its platform. These enhancements streamline application security scanning, reduce noise, and accelerate remediation for AI-first development environments, ensuring robust protection against modern vulnerabilities, including those specific to AI-driven coding practices.
Legit Security launches AI-native SCA and SAST for enhanced AppSec scanning.
Supports OWASP AI Top 10, addressing AI-specific vulnerabilities like prompt injection.
Advanced reachability and license detection reduce noise and focus on exploitable risks.
Enables secure use of AI code assistants like GitHub Copilot and vibe coding tools.
Connects code-to-cloud context with business criticality for precise remediation.
Empowers developers to adopt AI-first development with minimal friction.
Legit Security’s upgraded SCA and SAST capabilities are tailored to meet the demands of AI-driven development, including the use of vibe coding tools like Cursor and Windsurf, and AI code assistants such as GitHub Copilot. By integrating advanced reachability analysis and license detection, the platform filters out irrelevant alerts, enabling security and development teams to focus on actionable risks. This results in faster, more accurate remediation, reducing the burden on developers and enhancing overall application security.
The new features expand coverage to include AI and large language model (LLM)-specific vulnerabilities, such as prompt injection, insecure model usage, and risks from third-party AI integrations. Unlike traditional SAST tools, Legit’s platform is designed to detect these modern threats, ensuring comprehensive protection for organizations embracing AI-first development. This aligns with the OWASP AI Top 10, providing a framework to address critical AI-related security concerns.
Security teams often face overwhelming alert volumes, while developers are pressured to accelerate coding with AI tools. Legit’s approach connects code-to-cloud context with business criticality, ensuring that only high-impact issues are prioritized. “SCA and SAST are critical parts of effective AppSec, especially with AI code generation, because they help identify vulnerable code anywhere,” says Liav Caspi, co-founder and CTO at Legit. “However, many traditional code scanning tools lack context, leading to too much noise, which ultimately blocks adoption by developers. Our advancements resolve the common pain points of existing tools and provide intelligent context that reduces false positives and the friction they create, positioning us to secure vibe coding.”
By consolidating AppSec tools and providing a precise development ownership model, Legit’s platform ensures that vulnerabilities are assigned to the right developers at the optimal time. This minimizes friction and supports compliance with industry standards, making it easier for organizations to manage risks in AI-led development environments. The platform’s intelligent context reduces false positives, enabling teams to focus on what matters most—securing applications without slowing down innovation.
Legit Security’s enhanced SCA and SAST capabilities mark a significant step forward in application security, offering a smarter, more efficient approach to managing risks in AI-first development. By addressing modern vulnerabilities and empowering developers, Legit is setting a new standard for secure, scalable, and compliant software delivery.
The Legit Security AI-native ASPM platform is a new way to manage application security in a world of AI-first development, providing a cleaner way to manage and scale AppSec and address risks. Fast to implement, easy to use, and AI-native, Legit has an unmatched ability to discover and visualize the entire software factory attack surface, including a prioritized view of AppSec data from siloed scanning tools. As a result, organizations have the visibility, context, and automation they need to quickly find, fix, and prevent the application risk that matters most. Spend less time chasing low-risk findings and more time innovating.
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