Black Duck Signal: Security that moves at the speed of AI

The security gap in AI-driven development

As agentic AI takes the driver’s seat in software creation, developers face application risk at unprecedented speed and scale. Traditional application security testing (AST) tools weren’t designed for this reality. They were designed for code that was written sequentially, intentionally, and only by humans. They scan periodically, alert cautiously, and operate out-of-band. In an agentic world, that model collapses. Agentic AI is capable of producing hundreds of changes per hour, across multiple components, APIs, and configurations. Code reviews can’t scale that mountain of code, so changes are going into repos unseen.

Generic AI-powered security tools have emerged to address this gap, but they lack the one ingredient that enterprise security absolutely depends on: context. By context, I mean the deep understanding of an application’s components, relationships, data flows, frameworks, and runtime behavior that gives AI the grounding it needs to make accurate security decisions. Without it, AI tools face three critical limitations: hallucinations, noise, and remediation errors. They generate plausible-sounding but inaccurate findings, and they overwhelm teams with false positives and suggested theoretical fixes that fail in production environments. When you’re securing enterprise-grade software at AI scale, this is simply unacceptable.

ContextAI: Where AI meets two decades of security expertise

This is where Signal fundamentally differs from every other solution in the market. At its core, Signal is powered by ContextAI™, our purpose-built application security model containing petabytes of human-vetted security intelligence. ContextAI has something no generic AI can replicate: 20+ years of security ground truth from thousands of real-world proprietary and open source codebases.

Think about what that means. When Signal analyzes your code, it’s not applying LLM reasoning in a vacuum. It’s augmenting AI with petabytes of context from Black Duck’s living knowledge base, meticulously curated from hundreds of thousands of commercial and open source codebases. It’s applying context from coding rule sets exercised over billions of lines of code to deterministically identify quality and security issues across more than 40 programming languages. It’s drawing on tens of thousands of BSIMM assessments, Black Duck Audits, and dynamic scans of production web applications—millions of tests across trillions of lines of real-world code.

This isn’t theoretical knowledge generated by a language model. This is real-world intelligence gleaned from securing mission-critical software across every industry, every language, and every framework you can imagine. This context is what transforms AI from a promising technology into a production-ready security solution that enterprises can trust.

Agentic AI that reasons, validates, and acts

Signal operates differently than traditional AST tools or single-model AI solutions. Built on an agentic AI architecture, Signal deploys multiple specialized AI security agents that work together to analyze vulnerabilities, validate exploitability, prioritize risk, and recommend or apply fixes using human-like reasoning. Where other solutions stop at identifying potential issues, Signal reasons about them with the depth and nuance of experienced security professionals.

The practical impact is transformative. Signal actively addresses severe and complex vulnerabilities, including those based on business logic errors or in languages not supported by traditional AST tools. It goes beyond simple pattern-matching by using multiple analysis techniques to accurately match artifacts with security context in real time. By combining LLM reasoning with ContextAI’s security intelligence, Signal delivers high-fidelity analysis and automated remediation that solutions built on general AI models alone can’t deliver.

Built for modern development workflows

Signal integrates directly into the agentic software development life cycle through model context protocol and APIs that support AI coding assistants, IDEs, and automated AI pipelines. It works seamlessly with GitHub Copilot, Google Gemini, Claude Code, Cursor, and other popular development tools. Signal scans code in real time as it’s written, continuously analyzing across languages, frameworks, and architectures.

Unlocking AI’s true potential through governance

AI-driven development forces organizations to confront an uncomfortable truth: The very speed that makes AI transformative can become its greatest liability without proper governance. At machine speed, even minor security defects can multiply into major risks, threatening to erode the gains that AI promises.

Signal unlocks AI’s true potential by enabling enterprises to govern AI-generated software responsibly and at scale. It helps organizations move faster with AI while maintaining the security, compliance, and trust that enterprises and governments demand across the entire application life cycle.