Black Duck Signal

The critical difference is the fidelity and accuracy of security findings. In our tests, Signal produced significantly better, more actionable security findings than general purpose AI coding assistants, because it’s purpose-built for application security analysis.

From a user perspective, using Signal is as easy as using a coding assistant like GitHub Copilot or Claude. In fact, you can register Signal with these coding assistants via model context protocol and then simply ask your coding assistant to scan code, and the request is automatically directed to Signal.

Signal augments and refines AI analysis with Black Duck ContextAI^™. This knowledge base contains over 20 years of human expert–vetted security knowledge, including insecure coding patterns; open source components, vulnerabilities, and licenses; and secure coding standards and best practices.

By combining cutting-edge AI with two decades of human security expertise, Signal produces higher-quality, higher-fidelity results than generic AI security solutions that lack this comprehensive repository of validated security intelligence.

Signal complements rather than replaces traditional static analysis solutions like Coverity^® Static Analysis and Polaris fAST Static. AI continues to evolve and may eventually render some current technologies obsolete, but Signal enhances your existing security stack by

• Extending language coverage: Supporting languages for which traditional static analysis rulesets are not available
• Accelerating developer workflows: Providing complementary real-time analysis and automated code fixes directly in the IDE
• Improving detection rates: Identifying classes of security defects that rule-based analysis may miss, such as behavior and authentication issues

The main difference is how Signal looks at code. Signal is probabilistic, not rules-based. Traditional AppSec tools use predefined rules that flag known patterns. But Signal leverages large language models to reason through code similar to the way a human would. And because Signal reasons like a human, it can identify issues that traditional static analysis misses, including

• Business logic failures
• Insecure direct object reference problems
• Risks that may not be known about yet

Signal catches conventional issues like SQL injection and cross-site scripting as well, so there’s significant overlap with traditional tools. Where that overlap exists, Signal is especially valuable because it can cover languages that aren’t supported by a traditional static analysis engine. Building support for these languages in a traditional tool would require heavy investment; Signal supports them implicitly.

Being Mythos-ready involves combining the basics of AppSec with additional AI-driven capabilities, plus the ability to scale.

The overarching goal of Mythos readiness is to help organizations keep up with attackers who are gaining an asymmetric advantage by using AI to find and exploit vulnerabilities. Here’s what organizations need to do to be Mythos-ready.

• Maintain good AppSec hygiene: The fundamentals (shifting left, static analysis, SCA) are critically important, along with policy and automation.
• Fight fire with fire: Use AI to identify threats the same way attackers are, to stay ahead of them.
• Prioritize contextually: Use metadata like reachability, CISA’s Known Exploited Vulnerabilities lists, and risk-scoring to determine which issues matter most.
• Fix and validate at scale: Waiting for developers to manually fix every issue no longer scales. You need to use AI to fix and validate vulnerabilities automatically.
• Discover shadow AI: You need to know about the tools your developers are using. And you need to automatically discover the flood of new AI-generated code and repositories being introduced into your codebase, so nothing goes unscanned.