Build vs. Buy: The Wrong Debate for Security Teams

The Question Behind the Question

Of course it is possible to build a Security Design Review (SDR) tool in house. LLMs have made prototype-level tooling cheap. Connect an API, write a prompt, feed in a design doc, and you can get something interesting back in a few hours.

That is exactly why so many teams get pulled into the wrong decision too early. The question is not whether you can build something that works in a demo, but whether you want to own what comes next.

When security teams debate build vs. buy, they usually optimize for the tool: cost, features, control, maintenance overhead. All valid concerns. But none of them get to the underlying issue.

Most AppSec teams are small relative to the engineering orgs they support. As product and platform complexity grows, design decisions get made across architecture docs, Jira tickets, sprint planning, Slack threads, and product specs. At that scale, security cannot manually review everything.

So what happens? Teams triage. Protect the crown jewels. The highest-risk systems get reviewed. Everything else ships without design-level security scrutiny.

That's a coverage problem. The build-vs-buy conversation rarely addresses it directly.

Why the First 50% Is Deceptive

That first 50%, the working prototype, takes days. The second 50%? Turning that prototype into something you'd trust to review a payment system takes months. 

Production-grade design review requires:

Structured rule frameworks. Not a single prompt, but potentially hundreds of specialized prompts organized in decision-tree architectures that constrain the model's behavior and improve consistency. Without this, findings drift. The same document produces different results on Tuesday than it did on Monday.

Consistency engineering. LLM outputs are probabilistic. Achieving reliable, reproducible findings requires techniques like self-consistency checking, granular prompt decomposition, and continuous benchmarking. Skip this and the system becomes less trustworthy without anyone noticing — until something important is missed.

Ongoing rule maintenance. Security standards evolve. PCI DSS updates. New cloud services introduce new attack surfaces. New vulnerability classes emerge. Someone must continuously update the rule set, test changes against existing documents, and validate that updates don't regress existing coverage. This is effectively a full-time security research function.

Model lifecycle management. LLM providers update and deprecate models on their own timelines. A system built on a specific model version may require significant prompt re-engineering when the provider upgrades. Evaluating whether a newer model improves or degrades output quality requires its own infrastructure.

It takes 4-6 months of dedicated engineering time from a team combining senior AppSec expertise with AI/ML engineering capability.  That means owning an internal product that has to stay security-relevant, technically reliable, and operationally trusted over time.

What You’re Actually Trading

Here's the question that cuts through the build-vs-buy framing: What is your security engineering time worth?

For teams with dedicated security engineering capacity and genuinely unique workflows, building internally can make sense. But for most organizations, the better use of scarce security talent is not maintaining the machinery around SDR. 

The hidden cost of building is what you do not build instead: evaluating real attack paths, shaping architecture decisions early, making risk calls, coaching product teams, and acting on the issues review systems actually uncover.

That is the actual trade-off.

The Questions Worth Asking Instead

The next time this conversation comes up, two questions are usually more useful than any feature comparison.

How does this change the ratio of secure design decisions to total design decisions being made across the organization? 

A tool that gives developers security feedback at decision time changes coverage. One that requires a security engineer to interpret and relay every output doesn't.

Does this make engineering teams more autonomous or more dependent on the security team? 

The goal is not to make security engineers review more tickets. It is to move more security-aware decisions earlier, without requiring a security engineer in every room.

BSIMM data shows design review is now present in more than 80% of mature security programs. The EU's Cyber Resilience Act and FDA guidance on software in medical devices now require documented evidence that security was considered at the design stage. The expectation has shifted.

The question was never build vs. buy. It's whether security can scale with engineering. Every decision, including this one, should be measured against that.