Build vs. Buy AI Pentesting: The Real Cost of Going In-House
Dan Lacher, Cybersecurity Engineering Team Leader at Dow, joins Synack Co-Founder and CTO Mark Kuhr and Synack Solutions Architect Tim Nordvedt for an unscripted conversation. Hear why Dow chose Synack to help extend its red team, and how Synack built Sara, the Synack Autonomous Red Agent.
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Synack Extends Dow's Red Team
Dow uses Synack to extend its small internal red team with vetted security researchers and AI agents. This gives the team more testing capacity across high-value assets without having to build and maintain the entire capability internally.
The Agent Harness Matters as Much as the Model
Lab benchmarks often rely on simplified capture-the-flag exercises that do not reflect enterprise complexity. Real-world performance depends on the agent harness, orchestration and guardrails around the model, not only the model itself.
Continuous Pentesting Helps Close Coverage Gaps
Dow uses continuous pentesting to reduce the time between a new asset appearing and that asset being tested. Newly exposed cloud assets can enter the testing workflow in near real time.
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Explore Sara →Why Continuous Testing Beats Point-in-Time
The moment a point-in-time report is delivered, it's already out of date. Learn why Synack built for continuous coverage instead.
Read the Blog →Build vs. Buy AI Pentesting FAQ
What is the "build vs. buy" debate in AI pentesting?+
It's the decision security teams face when they want AI-powered penetration testing: build a custom agentic AI pentesting capability in-house, or buy it from a vendor that already operates one at scale. The debate covers model selection, engineering overhead, ongoing maintenance and whether an in-house team can keep pace with model and threat changes.
Why did Dow choose to partner with Synack instead of building its own AI pentesting tool?+
Dow evaluated AI pentesting vendors and also scoped what it would take to build the capability internally. Dan Lacher, Dow's Cybersecurity Engineering Team Leader, explained that an internal build would require significant token spend and engineering resources to reproduce and maintain a capability supported by a dedicated vendor team. Dow instead uses Synack as a force multiplier and directs that capability at its highest-value assets.
What role do human researchers play alongside Synack's AI agent, Sara?+
Humans stay in the loop before results reach the customer. AI agents can scale reconnaissance and vulnerability discovery around the clock, while Synack's vetted researchers validate which findings are exploitable and add business and environmental context. This human validation helps customers focus on verified risk rather than unconfirmed findings.
Why don't AI pentesting benchmarks and lab tests reflect real-world performance?+
Many public benchmarks use simplified capture-the-flag exercises that do not reflect enterprise complexity. Real environments include authentication flows, legacy infrastructure, varied technologies and unpredictable edge cases. A model or agent tuned for a lab can struggle in production, so the agent harness, orchestration and guardrails around the model are critical to real-world performance.
What hidden costs come with building an in-house AI pentesting engine?+
Beyond token spend, internal teams must reassess prompts and tooling as models change, maintain guardrails and an orchestration layer, monitor production safety and continuously benchmark agent performance. They also need the engineering capacity to keep the system current as models, targets and threats evolve.
What is Sara, the Synack Autonomous Red Agent?+
Sara AI Pentesting is Synack's agentic AI capability. It uses a specialized agent harness layered across frontier and open models to support reconnaissance and controlled exploitation at machine speed and scale. Guardrails enforce each customer's rules of engagement while Synack's vetted researchers validate findings before they reach the customer.
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