Build It or Buy It?
An Evidence-Based Framework for AI Security Testing
If you've spent the last year wiring up LLMs with open-source tools to build your own security testing pipeline, you're in good company. The instinct to build is sound, and for a well-scoped proof of concept, it works.
The harder question is what happens next. As code volume grows — 100% of security practitioners surveyed reported increased engineering delivery in the past year — the gap between a working prototype and a production-grade security program widens fast. Most of that gap is invisible until it isn't: maintenance overhead, knowledge locked in one person's head, and findings that still require a human to validate before anyone acts.
This whitepaper draws on primary research from 200 cybersecurity practitioners to cover:
Why the visible costs of DIY (API spend, initial dev time) are the smallest part of the total
How hidden costs like maintenance, silent degradation, and compliance gaps add up over time
A practical framework for deciding when to build, when to buy, and what the warning signs look like
Read the Whitepaper
What You'll Learn
Learn why DIY AI security testing hits a ceiling, what it actually costs to maintain, and how to evaluate your organization's specific build-or-buy threshold with a framework grounded in primary research.
The validation gap
Detection is step one. Confirming exploitability, filtering false positives, and producing developer-ready evidence is everything else. It's where 66% of teams spend more than half their time.
The infrastructure requirement
A prototype runs on your laptop. Production runs continuously, across every app and every release, without babysitting. The engineering distance between those two things is months of work that isn't security work.
The maintenance trap
Prompts drift. APIs change. The engineer who built it leaves. DIY solutions degrade quietly, and usually without a visible alert until a vulnerability reaches production.
The full cost of building
Initial build labor, ongoing maintenance, manual validation, LLM API spend, key-person dependency, compliance gaps. For a 2 to 3 person team covering 10 or more apps, the total can exceed $150K to $200K annually.
Organizational threshold assessment
Six concrete indicators that show whether DIY economics are working for you or against you. If your organization matches three or more, the math has likely already flipped.
A decision framework
When building is viable, when buying makes more sense, and what DIY strain looks like in practice. Structured for security leaders who need to make the case internally, not just weigh the options.
The initial build is the smallest part of the cost. Maintenance, manual validation, and organizational risk account for the majority.
Ready to make the call?
Download the whitepaper to understand the full cost of DIY AI security testing and the framework for deciding what's right for your team.