Security teams have moved past the question that used to sound reckless: can AI review code and find real vulnerabilities?
The enterprise question now is sharper: should we build our own AI code scanner, or buy an AI-native SAST platform like DryRun Security?
On paper, building looks attractive. Your team already uses Claude Code, Mythos, and other frontier models. You control your own stack. Why bring in another vendor when you can add "AI review" to CI with a few prompts and some glue code?
The real question is not whether AI can review code. It is whether security teams can trust AI-generated and human-written code without an independent verification layer. As AI-assisted development accelerates, organizations need a way to consistently validate code before it reaches production regardless of who, or what, wrote it.
Claude Code /security-review and DryRun PR scanning are both aimed at the same workflow moment: reviewing a pull request for security risk. A developer can run Claude Code against their PR and get useful feedback. That is valuable. The difference is operationalization.
Claude Code security review is developer-triggered unless your organization builds rules to force it into the pipeline. DryRun is designed to run across pull requests as an org-owned control from the start — it does not depend on every developer remembering to ask the right question at the right time.
And once the scan runs, the enterprise problem is not finished. You still need policies, triage, reporting, evaluation, auditability, and governance. That is where a developer tool and an AppSec platform diverge. Because AI-native AppSec is not a model integration problem. It is a systems, evaluation, and governance problem. That is where internal builds usually fall short.
Where enterprises are on the AI AppSec maturity curve
Most enterprise security programs are moving through the same curve: from skepticism, to developer experimentation, to org-level control.
Enterprises need Phase 4. They need AI that behaves like a security control — not a loose collection of model calls. That means enforcement, auditability, reliability, and integration into existing SDLC and governance processes.
DryRun Security was built for this phase. It is an Code Security Intelligence platform that integrates into developer workflows, provides contextual analysis, and gives AppSec teams the control surface they care about: consistent review, policy enforcement, auditability, and low-noise findings.
Build vs. buy: what you are really deciding
A useful build-vs.-buy analysis has to go beyond "we already have an LLM contract."
Most organizations discover they are not building a security scanner. They are building a security product that requires ongoing evaluation, governance, policy management, reporting, and operational ownership.
The first version of a DIY approach often looks simple: take Claude Code /security-review, point it at the pull request, and make developers run it. But if you want that review to behave like a control, you have to answer harder questions. Does it run on every PR? Who owns the rule that forces it? What happens when developers bypass it? Where do findings go? How are results tracked? How do you tune false positives? How do you prove to leadership or auditors that the control is working?
When you choose to build, you are effectively choosing to build and maintain:
- Pipeline enforcement that guarantees review runs on the right pull requests
- An agentic review system that can reason over repositories, diffs, and architecture
- A continuous evaluation pipeline that keeps the system accurate as models and codebases change
- Triage and reporting workflows for findings across teams
- An observability layer for context assembly, routing, retries, latency, and output quality
- A governance layer that turns findings into controls your security program can trust
If you are not committing to those pieces, you are not really building a security control. You are running prompts and hoping the output remains accurate over time.
DryRun Security brings those components together as a platform, not a collection of scripts.
Scanning is the overlap, not the whole product
Claude Code /security-review and DryRun PR scanning do overlap: both can review a pull request and identify security concerns. But overlap at the scan layer does not mean equivalence at the platform layer.
A developer can manually run Claude Code against a PR. An organization can also wire that review into CI and enforce it through pipeline rules — but then the organization owns the enforcement logic, exception handling, tuning, reporting, and maintenance around that workflow.
DryRun starts from the opposite direction. PR scanning is not an optional local action; it is the default control surface. DryRun runs across pull requests, applies consistent contextual review and policy checks, and gives AppSec the platform layer around the scan.
The scan tells you what the model saw. The platform tells you whether the control is working.
Systems, not models: the multi-provider reality
Claude Code, Mythos, and other frontier tools are impressive. They are also only part of the system.
A production AppSec control cannot depend on one model, one tool path, or one prompt pattern. It needs:
- Multiple model providers to hedge outages and performance regressions
- Routing logic to pick the right model and mode for a given task
- Fallback paths when a provider fails or degrades
- Evaluations that measure behavior over time, not just during onboarding
DryRun Security is designed around multi-model operation, structured routing, fallback, and continuous evaluation. Doing this in-house means standing up infrastructure, experimentation pipelines, and ongoing evaluation just to maintain baseline behavior. That is why it matters how your vendor actually analyzes code — not just which models it claims to support.
AI-generated code changes the economics of AppSec
AI-assisted development does not just change how code is written. It changes how much code security teams must review. As coding agents and AI-assisted workflows increase developer output, pull request volume grows while AppSec headcount remains relatively flat.
Organizations need security controls that can scale review coverage without relying on manual validation or developer self-review. Independent verification becomes increasingly important as AI systems generate more of the software entering production
How DryRun analyzes code
DryRun does not treat "AI review" as a single model call. It runs code changes through a multi-stage analysis pipeline designed for real-time AppSec at pull request scale: Harness → Planner → Eval → Exploitable → Contextual Security Analysis (CSA).
DryRun does not treat AI review as a single model call. It combines contextual analysis, deterministic checks, policy enforcement, and AI reasoning into an independent verification system for AI-generated and human-written code. The goal is not to determine which model is best, but to provide security teams with a control they can trust across every pull request.
Developer tools are author-driven. AppSec platforms are org-driven.
Claude Code /security-review can review a pull request. The question is whether it runs because the developer chose to run it, or because the organization owns it as a control.
In the default workflow, Claude Code is author-driven. The developer chooses when to run it, what context to include, what prompt to use, and when the answer is good enough. You can move Claude Code closer to a control by wiring it into CI and enforcing pipeline rules — but then your team is building the surrounding platform: trigger logic, policy exceptions, result handling, reporting, evaluation, and governance.
DryRun is designed for that org-owned model from the beginning. It runs in the pull request path, applies consistent contextual review and Natural Language Code Policies, and gives AppSec teams a way to manage findings across repositories and teams.
Evaluation and model degradation: the part most teams skip
The bigger risk in internal builds is not initial inaccuracy. It is silent drift.
As models update, prompts evolve, and codebases change, you need disciplined evaluations to know whether the system is still safe to trust. Before a model is released into the DryRun Security production workflow, it is evaluated against existing models across four critical areas:
DryRun's public coverage matrix and SAST Accuracy Report show how the Contextual Security Analysis engine is evaluated across vulnerability categories such as injection, broken auth, and logic flaws.
Replicating that evaluation discipline inside a single company is non-trivial. It is a product line, not a side project on top of "we wired a model into CI."
Observability for agentic systems, not just microservices
Even with strong prompts and a model integrated into CI, agentic systems fail in ways traditional services do not. Common failure modes include:
- Wrong or missing context — incomplete history or the wrong files
- Misfired tool calls or incorrect routing
- Subtle provider behavior changes that turn previously good prompts into noisy or weak output
- Retries and loops that increase latency and cost without improving quality
Without observability into tool invocation, context assembly, routing decisions, latency, retries, and output quality, you cannot safely debug or improve the system. DryRun Security includes telemetry that helps its team understand how agents reason about code changes, where they spend time, and how behavior shifts over time.
Cost: pull request scale scanning is where budgets go to die
Scanning a single repository with a model is cheap. Scanning every pull request across dozens or hundreds of services all year is not. Internal builds often underestimate:
- Weak caching and redundant calls
- Oversized models used for simple assessments
- Reprocessing the same context across multiple checks
- Unbounded agent loops that quietly balloon usage
- The hidden cost of building the control layer around the scan
DryRun Security is optimized for pull request scale scanning, combining contextual analysis with caching, smart scoping, and multi-model strategies to manage cost without sacrificing accuracy.
Governance and change management: findings as controls
To function as a real security control, your AI AppSec system must plug into governance. Findings must be:
- Tracked and triaged over time
- Tied into tickets and change management
- Mapped to policies that auditors and leadership can understand
- Measured for effectiveness, not just volume
DryRun Security treats security findings as part of an enterprise control fabric — not just bot comments. Its contextual analysis and Natural Language Code Policies help security teams enforce standards across services without writing custom rules for every framework.
Where building still makes sense
Enterprises should not abandon internal experimentation. Building makes sense when you:
- Need to test frontier models or highly specific workflows
- Want to integrate AI-driven tooling tightly into custom pipelines
- Are exploring new types of security checks that are not yet standardized
The best pattern is not "never build." It is build for exploration and differentiation, buy for the core control.
Use internal builds to learn, prototype, and push the edge of what your team needs. Use a specialized platform like DryRun Security for the production control: accurate, contextual, low-noise SAST integrated into developer workflows.
DryRun Security helps AppSec teams independently verify every pull request, apply security policies consistently, and prove that security controls are operating as intended across the software development lifecycle.
As models like Claude Code and Mythos advance, the competitive advantage shifts away from "which single model do you use?" and toward "what system have you built around them?" DryRun Security is that system for AI-native AppSec.
See how DryRun helps AppSec teams verify code before it reaches production.
