Can you use ChatGPT or Claude to run penetration tests?

You can, but these models won’t deliver an effective pentest on their own. These models lack the orchestration, exploit-verification layer, and offensive security methodology needed for production pentesting. Without those components, you get high false positive rates and shallow findings that don’t reflect real attack chains.

How much does it cost to build an AI pentesting tool?

It varies, but the total costs is typically higher than most teams forecast. Beyond the upfront build, total cost of ownership includes token consumption at scale, model deprecation and prompt retuning, regression testing after every upgrade, and the headcount required to maintain it as a live system. These costs compound and are difficult to forecast.

Does an internal AI pentesting tool satisfy compliance requirements?

Generally, no. PCI DSS, SOC 2, ISO 27001, FedRAMP, and most enterprise security policies require independent third-party assessments. An internal tool counts as self-assessment regardless of how sophisticated it is, and most auditors won’t accept it as a substitute for third-party validation.

Jun 26, 2026

Considering Build vs. Buy for AI Pentesting? Top 5 Questions to Ask

Boards and CIOs are pushing security teams to build internal AI pentesting tools, but is it worth it? This piece walks through the five questions security teams should ask when deciding between build vs buy for AI pentesting.

A frontier model alone isn't a pentesting platform, which includes custom orchestration and exploit verification.
False positives in AI pentesting are an architectural problem, not a tuning problem.
Autonomous offensive tooling needs human-in-the-loop oversight by design.
Total cost of ownership includes model drift, prompt retuning, and dedicated headcount.
Most compliance frameworks require independent third-party assessment, regardless of how sophisticated an internal build is.

With every new frontier model release, whether it’s Anthropic’s Mythos or ChatGPT 5.5, security and pentesting teams are under growing pressure from boards, CEOs, and CIOs to build internal AI tooling. Before you commit to a build, here are the five most common questions we help teams work through.

Can I Just Use Claude or GPT and Point It at My Environment?

A frontier model alone is not a pentesting platform. Without custom orchestration, specialized sub-agents, and an independent triage layer, you get high false positive rates and shallow findings. So you then look at open-source agentic frameworks which look promising in controlled lab environments but fail consistently against real applications with authentication flows, custom business logic, and non-standard APIs. The demo takes a weekend, but making it dependable against a real attack surface is the other 80% of the work.

That’s where the engineering complexity exists and where total cost of ownership (TCO) explodes. Synack spent years evaluating every major agentic framework before building our own. The gap between a lab result and a production result is where all the engineering complexity lives and where our entire development team stays relentlessly focused. Before going down this path, ask yourself: what specific vulnerabilities are you hoping to find, and can you map out the full workflow from initial recon to a validated finding?

My Engineering Team Is Strong. Can They Build This?

Typically, engineering capability isn’t the gap. The real issue is often the methodology behind offensive security, combined with long-term sustainment. Building an effective AI pentesting system requires deep knowledge of how real attack chains work and a maintained system to keep it running 24/7/365. It must scale dynamically, handle odd architecture challenges autonomously and gracefully pause when a challenge can’t be overcome. Simultaneously it must know how vulnerabilities combine, which OSINT signals matter, and what separates a real exploit from a false positive. It has to do this consistently, which is no small challenge with the nondeterministic nature of the agents. That knowledge comes from years of actual pentesting and system development, not AI expertise alone.

Sara AI Pentest, Synack’s Autonomous Red Agent, was built leveraging our existing infrastructure that supports tens of thousands of tests a year. Each test is modeled from the methodology of real production testing, distilling researchers’ intuition into a structured workflow encoded across hundreds of specialized agents. That 13-year knowledge base isn’t available off the shelf.

The question worth asking is whether your team has enough dedicated offensive security and AI/ML engineers to drive it.

Will I Maintain Control Over My Data and the Models We Use?

That’s a legitimate requirement, and it’s worth being precise about what “control” actually means for your program. Synack offers clear data handling policies, environment isolation, guardrails, and defined processes for how findings are stored and accessed.

The less obvious reality is that building internally transfers a different kind of risk to your team. When you own the tool, you own the full lifecycle. That includes model selection, token cost management, model deprecation, prompt returning after upgrades, risks like accidentally deleting data, and benchmarking every change. Control over the tool also means accountability for its failures. We’re happy to walk through exactly what’s non-negotiable for your team and show you how Synack addresses it.

Will It Be Cheaper to Build?

The upfront build cost is real. The total cost of ownership is where the math sneaks into the equation.

Token consumption at scale for offensive security is significant. Running hundreds of agents across a real application portfolio is expensive, and without precise orchestration, costs escalate quickly. Tuning for cost impacts efficacy. Tuning for efficacy increases costs. In our experience, engineers become cost-conscious and start running fewer tests, which defeats the purpose. On top of that, AI providers regularly sunset older models. The prompts and guardrails built against them don’t port cleanly to the next version. Every MCP tool change, guardrail change or model migration is a regression event that requires retesting and retuning at scale.

Add the headcount required to maintain the system and you’re not building a tool, you’re staffing a product team. Synack is a fixed, predictable cost so you know exactly what you’re paying. A DIY system at scale is much harder to forecast.

Can I Build Something Lightweight for My Internal Red Team?

Experimenting internally to make your own team more efficient is a reasonable starting point, but two things are worth considering before you make any major security plan changes.

First, most compliance frameworks, including PCI DSS, SOC 2, ISO 27001, FedRAMP, and most enterprise security policies, require independent third-party assessment. An internal tool, no matter how sophisticated, counts as self-assessment. Most auditors won’t accept it and most CISO’s want some shared responsibility when they say “it’s been tested”. Second, lightweight internal tools tend to stay lightweight or get abandoned. That’s because innovators typically struggle with sustainment. The maintenance burden compounds as models change, as your application environment evolves, and as the interest of the team that built it moves on. A proof of concept is not a program.

Before your team commits, it’s worth confirming whether a build option would actually satisfy your compliance requirements, or whether you’d end up maintaining an internal tool and paying for third-party assessments anyway.

Synack’s Sara platform combines the scale of AI with the methodology of the world’s top security researchers. I would love to help if you’re weighing build vs. buy. I’ve seen my fair share of build projects. They can be beneficial but they can be a massive financial and resource drain as well. We’re happy to help you run the numbers.