Why retrofitting legacy scanners with language models falls short compared to building with AI at the foundation.
People ask me what it’s like starting an AppSec company in the AI era… and in short, it's amazing. I really believe this is the most exciting time to be in the industry. If you're interested in code security and software development, then every day there are incredible new possibilities for you. We are solving long-standing problems (e.g. broken auth vulns) and discovering new ways to identify and isolate risk in our codebase.
I literally wake up every day more excited than the previous day because DryRun Security is getting smarter day-by-day and solving real problems for our customers. Has it been easy? Of course not, but we are having a blast, and if you keep reading, you’ll see why!
Static Analysis Just Got Interesting
—and not just because someone slapped ‘AI’ on the cover.
We’re finally moving past brittle pattern matching and generic scan dumps. The new frontier is AI-native: systems that understand code in context, evolve with your stack, and surface risk with surgical accuracy. Let’s unpack why the difference isn’t just technical—it’s existential.
Software development is evolving rapidly with the widespread adoption of AI-assisted coding tools. Meanwhile, security tools are struggling to keep up. This article clarifies the key differences between two categories of security tooling vying for attention: AI‑Native SAST and SAST augmented with AI. Though they may appear similar, they differ fundamentally in design and capability—much like comparing handcrafted cuisine to processed snacks.
Defining the Two Categories
AI‑Native SAST solutions are built from the ground up with artificial intelligence at their core. Some of these systems analyze code changes in near real time, incorporating multiple contextual layers such as code type, developer intent, architectural design, and runtime environment. This holistic understanding enables them to detect complex logic vulnerabilities like Insecure Direct Object References (IDOR) and privilege escalation.
These tools continuously learn and improve based on developer interactions, resulting in more precise findings over time. Notable players in this space include DryRun Security with its Contextual Security Analysis, Pixee’s remediation-focused tooling, Mobb’s CI-integrated fix engine, and Corgea’s AI-first detection platform.
In contrast, “SAST + AI” tools are traditional pattern-matching scanners retrofitted with generative AI layers. Detection remains largely dependent on regular expressions or abstract syntax tree (AST) signatures. After the scan, a language model may reword findings or propose generic patches. Because these models lack visibility into broader architectural context or user flows, they often fail to detect deeper risks. Snyk Code with Autofix, Semgrep’s AI Assist, and SonarQube’s Clean Code AI exemplify this retrofit approach.
Why Architecture Matters
The architectural foundations of these tools carry practical implications:
- Signal-to-Noise Ratio: Traditional scanners often generate excessive false positives, and some are bolding on AI to reduce them while leaving the real benefits of AI behind. AI-native tools reduce alert fatigue by focusing on the intent and impact of code changes.
- Detection of Complex Vulnerabilities: Signature-based tools frequently miss high-risk logic flaws. AI-native solutions can reason across users, data, and permissions—crucial for catching vulnerabilities like IDOR and Broken Object-Level Authorization (BOLA). SAST tools that have been augmented with AI will likely face long-term challenges in dealing with these issues.
- Scalability and Performance: Today’s software environments are dynamic, involving microservices, serverless functions, and rapid deployment cycles. Legacy tools built for monolithic applications struggle to operate effectively in these modern settings.
The Limitations of AI Code Assistants
Code-generation tools like Windsurf, Cursor, OpenAI Codex, and GitHub Copilot have transformed software development. They improve developer productivity and reduce syntax errors, but they are not security reviewers. These assistants focus on functional correctness, not threat modeling or risk analysis.
Don’t get us wrong, we love vibe coding, but the fast-paced, results oriented flow is only as good as the prompts and security experience of the assistant. As a result, it can produce clean-looking code that harbors subtle and potentially dangerous security flaws.
DryRun Security and Contextual Security Analysis
DryRun Security leverages a structured and context-aware approach that begins by mapping the scope of each code change and identifying the programming languages involved. It then analyzes developer intent, looking for changes to authentication patterns, session management, and configuration files that might introduce new security issues—not to mention catching classic Top 10 risks that may have slipped into the code along the way. The result is a report that reads like the annotated notes of a seasoned AppSec reviewer.
While many teams begin with generic LLMs—asking tools like ChatGPT to review a repository—they often find those outputs superficial or impractical. DryRun offers a rigorous, scalable alternative tailored to enterprise environments.
The Compounding Benefits of AI‑Native Tools
AI-Native SAST solutions improve over time by learning from developer behavior. Whether fixes are accepted or rejected, the system adapts. Natural Language Code Policies (NLCPs) replace brittle regex-based rules, enabling tools to evolve with development practices. Security reviews become embedded in every code push, infrastructure update, and configuration change—instead of being delayed until release. Meanwhile, retrofit vendors remain trapped in a legacy model, making marginal improvements on outdated platforms.
Practical Advice for Security Leaders
When evaluating security tools, ask:
- Can it explain why a finding matters within the context of your system architecture?
- Does it detect complex business logic vulnerabilities like IDOR, BOLA, or privilege escalation?
- Can it scale across teams and automate both detection and remediation?
- How are new AI model capabilities integrated and tested?
AI-native solutions offer comprehensive support throughout the development lifecycle, while retrofitted tools typically only enhance output after scanning.
Try It Yourself
Interested in seeing how AI-native SAST performs in your environment? DryRun offers a two-week Proof of Value, including a full Code Risk Assessment. Discover the difference for yourself—and feel free to retire those regex goggles.
