AI Pentesting · Build vs. Buy
Security teams are increasingly asked to justify buying AI pentesting tools from vendors instead of building one in-house. Review the research, uncover the real cost of building it yourself, and watch how one team made the call.
Considerations
As security leaders consider building AI pentesting tools in-house, they're encountering tough questions. It's not just a matter of selecting a model to use. Teams have to be prepared after a model finds something, and determine whether that finding holds up.
A frontier model alone is not a pentesting platform.
Most teams can stand up a proof of concept that finds real vulnerabilities within weeks. Turning it into a program you can trust at production scale is a different order of complexity, and that's where the real cost starts to climb.
Get the answers to the questionsDecision in practice
Dow's Cybersecurity Engineering Team evaluated several AI pentesting tools and considered building the capability internally. Ultimately, they chose Synack because replicating a dedicated vendor's platform would require high token costs and engineering overhead. Watch the conversation to hear how Dow decided on Synack.
Sara, up close
Real environments don't behave like a lab. Logins break in unexpected ways, workflows branch across services, and the same vulnerability class shows up differently across different assets. Sara is built to work inside that complexity, running as a coordinated system of specialized agents shaped by the way Synack's own researchers test targets.
Agentic frameworks built on open source models are often tuned for controlled benchmark environments. Sara's approach is modeled on how Synack's top researchers work a target, instead of a known test case.
This isn't one free-roaming model. Each agent is purpose-built for a narrow task, which is what keeps a run focused across a real, messy attack surface instead of losing coherence after a few steps.
Sara's discovery phase is intentionally high recall. A separate system then challenges its own findings, and Synack Red Team reviews what remains for exploitability. What reaches you is a validated risk, not a hypothesis.
Coverage & Timing
New assets, new cloud services and new code ship to production faster than any annual or point-in-time test can follow. By the time a report lands, the environment it describes has already changed.
Continuous pentesting closes that gap by testing as assets appear instead of waiting for the next scheduled engagement, pairing always-on AI testing with periodic, deeper human-led engagements.
Read why Synack built for continuous coverageCost
Most estimates for building an AI pentesting solution in-house are built around the initial engineering sprint. The costs that decide whether the project survives tend to show up later.
The hidden bill usually arrives in year two.
Talk to us about your own build vs. buy evaluation, or see Sara AI Pentesting in action.
