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01/08/2026 9:01 pm
TL;DR: Security teams often frame Security Design Review tooling as a build-versus-buy decision, but Seezo argues the real issue is coverage: prototype-level LLM tools are quick to build, while production-grade consistency, rule maintenance, and model lifecycle management take months and dedicated expertise. The strategic question is whether scarce security engineering time should maintain review machinery or improve decision quality across the organisation.
NHIMG editorial — based on content published by Seezo: Build vs. buy: the wrong debate for security teams
By the numbers:
- BSIMM data shows design review is now present in more than 80% of mature security programs.
Questions worth separating out
Q: What fails when security design review tools are built as demos but used as controls?
A: The control fails when the organisation mistakes a plausible prototype for a reliable decision system.
Q: Why do security teams struggle to scale design review with engineering growth?
A: Because design decisions are distributed across architecture docs, tickets, chats, and specifications, while security teams remain small relative to the number of changes being made.
Q: How do teams know if AI-assisted IaC review is actually working?
A: Look for shorter pull-request cycles, fewer rollback events, less on-call noise, and a measurable drop in unmanaged drift.
Practitioner guidance
- Define review coverage metrics before tool selection Measure the percentage of architecture decisions that receive security review, the time to first security feedback, and the share of exceptions accepted without review.
- Build a regression test suite for security findings Create a fixed corpus of representative design documents and compare outputs after every prompt, rule, or model change.
- Assign ownership for rule and model lifecycle changes Separate content maintenance, model upgrades, and approval authority so that one team is not quietly changing the logic that drives review outcomes.
What's in the full article
Seezo's full blog post covers the operational detail this post intentionally leaves for the source:
- A practical breakdown of the rule frameworks needed to keep security review outputs stable across document types.
- The maintenance model for prompt updates, model changes, and continuous validation of review quality.
- How the workflow integrates with Jira, Confluence, and Slack so findings reach the teams making design decisions.
- Why the article argues that internal engineering effort is often better spent on architecture decisions than on maintaining the review machinery.
👉 Read Seezo's analysis of build versus buy for security design review →
Security design review coverage: what build vs. buy gets wrong?
Explore further
01/08/2026 9:07 pm
Build vs. buy is really a security coverage decision. The article is right to shift the question away from feature comparison and toward the proportion of design decisions that receive security scrutiny. That is the real control gap in most programmes. If security design review cannot keep pace with engineering throughput, the organisation is not choosing between tools so much as choosing which decisions remain unreviewed. Practitioners should measure coverage, not just capability.
A question worth separating out:
Q: How should security teams govern AI use in developer tooling?
A: Security teams should govern AI use as a data and access problem, not only a productivity feature. Define what information can be sent to models, require human review of generated code, and apply least privilege to connected repositories and tools. Approved use cases should be explicit, monitored, and revisited as model capabilities expand.
👉 Read our full editorial: Build vs. buy for security design review is really a coverage problem
