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Engineering teams have already adopted AI to ship faster. AI-assisted coding is now standard across development organizations, and the velocity is compounding. Code arrives faster, features land sooner, and deployment cadences that used to be weekly are now continuous. Engineering solved its scale problem by embracing new tooling and infrastructure. That shift worked. Security was built for a world where engineering shipped on a predictable cadence. That world is gone. The operational model still assumes a human is in the loop at every stage, and there are not enough humans. The result is a growing disconnect: engineering ships faster while security struggles to keep pace with the volume of code, configurations, and attack surface those releases create. Detection was never the bottleneck. The bottleneck is everything that happens after detection: triage, validation, reproduction, coordination with engineering, remediation, and verification that the fix did not break something else. That is five or six manual steps for every finding, and AI-assisted coding just multiplied the number of findings without touching any of those steps. Developers will not act on a scanner alert alone. They need reproducible evidence, clear impact, and actionable remediation steps before a finding becomes a fix. Adding more scanners or more process does not close this gap. It widens it by creating more noise to triage, more tools to maintain, and more results to correlate.

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The infrastructure shift

Engineering scaled with CI/CD, observability, and cloud platforms. These are not tools that do the work for you. They are foundations that let small teams operate at the scale of much larger ones. Security needs the same kind of shift, and LLMs are the scaling lever. But applying them to security work requires more than a model and an API key. It requires isolated execution environments, specialized agents, persistent memory, domain expertise, credential management, scheduling, verification systems, and integrations with the tools your team already uses. The alternative is building all of that yourself. Agent orchestration, toolchain packaging, sandbox provisioning, result verification, secret handling, integration wiring. Your security team ends up spending more time on scaffolding and maintenance than on actual security work. And what you build is frozen to the day you built it, while the models, techniques, and threat landscape keep moving. Neo absorbs this complexity. It is the platform layer that lets security teams apply LLMs directly to their workflows, from code review and HackerOne triage to production pentesting, on a continuous basis. Security moves from gatekeeping after the fact to operating continuously inside the development workflow, with tooling that validates, proves, and prioritizes autonomously, so human judgment is applied where it actually matters. Engineering is not going to slow down, and the volume of AI-generated code will only grow. Neo closes that gap so your team focuses on defining what to test, reviewing confirmed findings, and improving your security posture, not on scaffolding, noise, or proving exploitability by hand.