Frontier AI assistants are amazing at writing code. They should never be your code security intelligence solution.
If you build or secure software at scale, you already see where this is going. Right now 90% of developers are using AI to write code as reported by the DORA report last week. LLMs have enabled vibe coding, allowing developers and non-developers alike to write better code, faster. That is a real win.
But asking the code‑generating model to also be your security authority is like asking the pitcher to call balls and strikes. Both the pitcher and umpire are skilled, but the roles are different. Both positions are essential for the game's success.
Frontier LLM vendors will keep getting better at “secure at write time” and perhaps a bit beyond. They will not become the independent solution for provable risk assurance over the whole codebase. Code security intelligence is about third‑party validation, full‑codebase context, policy‑driven governance, visibility, and auditability across the lifecycle.
Separation of duties is non‑negotiable
Modern security programs are built on separation of duties. The entity that creates code should not be the same authority that grants it a clean bill of health. Separation of duties is a foundational control in NIST Cybersecurity Framework (800‑53). OWASP makes it core to their Principles of Security.
“Secure at write time” is necessary, not sufficient
Leading models already prevent obvious syntax and security mistakes during code authoring, and will continue to improve code quality incrementally. True code security intelligence is comprehensive, reducing risk and building trust through:
- Context and understanding beyond the immediate code update or pull request (PR)
- Organization‑specific, enforceable policies
- Feedback to developer and security teams solving complex business/logic issues
- Insights and visibility built on learning and trends
- Infuse“tribal knowledge” into a substrate level of intelligence about customer security programs
- Evidence you can track, review on-demand, take to audit, and prove to the board
The DryRun Security AI-native, agentic code security intelligence solution was built for this. At commit our Code Review Agent and Custom Policy Agent use Contextual Security Analysis (CSA) to look beyond the current file or PR and reason about surrounding code, services, and history so it can surface risk and enforce policy in context. Our Codebase Insight Agent then goes beyond individual code updates to provide security insights across your organization that inform reporting, visibility, trends, audits, and more.
Why “code security” will stay a checkbox for frontier vendors
Frontier LLM companies aren’t focused on your security program. Their focus is taking market share and making trillion-dollar bets while cranking out features that are just good enough to stop attrition. Follow the roadmaps. Frontier vendors are racing to own consumer and enterprise interfaces, coding environments, app marketplaces, browsers, search, and media. OpenAI is shipping Sora, Codex, a browser, a GPT Store, driving construction of huge data centers, and more. Anthropic is expanding productivity features like Artifacts and enterprise connectors while running multi-million dollar ad campaigns to consumers. They’re both investing hundreds of millions to build the next model that will be outdated in months. While these strategies will benefit many globally with ever-improving AI tools, code security will remain a minimum viable product checkbox, rather than a core innovation.
DryRun Security’s focus is deeper. Contextual. We built the industry’s first AI-native, agentic code security intelligence solution. We understand the context, intent, and environment of your codebase and your org. We secure software built for the future by helping developer and security teams quiet noise, gain insights, and surface risks that pattern-based scanning tools and AI coding tools inherently miss.
What IDE and frontier LLM assistants do well, and where they stop
None of this takes away from where frontier LLM assistants shine at code suggestions, explanations, common practices, and more. For example, OpenAI’s Aardvark moves beyond the IDE to do some research in the repo like standing up a sandbox to model reachability. Anthropic Claude security-review improves security by scanning for dependency vulnerabilities at write time which should help reduce classic security flaws and noise historically introduced by AI.
Of course, we work with these leading AI coding tools to accelerate and future-proof your development program while greatly improving security intelligence. Here's how:
Frontier AI assistants + DryRun Security
Note on autofix: IDE assistants often propose autofixes for known scanner alerts, often with limited context. DryRun Security does not auto‑modify your code. It explains risk and links evidence so your team stays in control and integrates with your AI coding assistant.
Bottom line
Frontier LLMs will keep getting better at helping create high-velocity code at write time. They will need to be complemented by an independent, agentic code security intelligence solution that can see across your codebase, enforce your policies, build insights, and give you evidence you can defend.
Let the pitcher pitch. Let DryRun Security be your umpire.
Request a DryRun Security demo → dryrun.security/get-a-demo
Learn more about Contextual Security Analysis → https://www.dryrun.security/resources/csa-guide
