Senior Product Manager, Secret Detection and Vulnerability Research

GitLab is the intelligent orchestration platform for DevSecOps. GitLab enables organizations to increase developer productivity, improve operational efficiency, reduce security and compliance risk, and accelerate digital transformation. More than 50 million registered users and more than 50% of the Fortune 100* trust GitLab to ship better, more secure software faster. The same principles built into our products are reflected in how our team works: we embrace AI as a core productivity multiplier, with all team members expected to incorporate AI into their daily workflows to drive efficiency, innovation, and impact. GitLab is where careers accelerate, innovation flourishes, and every voice is valued. Our high-performance culture is driven by our values and continuous knowledge exchange, enabling our team members to reach their full potential while collaborating with industry leaders to solve complex problems. Co-create the future with us as we build technology that transforms how the world develops software. *Fortune 500® is a registered trademark of Fortune Media IP Limited, used under license. Claim based on GitLab data. Fortune 100 refers to the top 20% ranked companies in the 2025 Fortune 500 list, published in June 2025. Fortune and Fortune Media IP Limited are not affiliated with, and do not endorse products or services of GitLab. An overview of this role As a Senior Product Manager, you will own GitLab's Secret Detection offering and the Vulnerability Research function that produces the detection content across security products. Secret Detection is one of the highest-signal, highest-volume security capabilities on the platform. Leaked credentials are the most common initial access vector in real breaches, and as AI agents write, commit, and configure more of the software, the surface area for exposed secrets grows faster than the number of humans watching it. You will own the full loop: detect a secret with high precision, tell the customer whether it is still live, get it revoked, and prevent the next one from ever landing. In parallel, you will own vulnerability research as a product asset rather than a back-office function. The detection rules, advisory data, and malicious package and reference intelligence your team produces are what make GitLab's security scanners worth paying for, and they need to ship on a cadence with measurable quality. This is an outcome-owning role - you will carry adoption, retention, and revenue targets for your area and be expected to explain how your roadmap moves them. Some examples of our projects: - Push protection and pre-receive blocking that stops a credential before it reaches a repository, without wrecking developer flow - Secret validity checking and automated revocation partnerships with major cloud and SaaS token issuers - Detection content pipelines that turn threat research into shipped rules, with precision and recall tracked per rule - Intelligence-driven detection of malicious packages, dependencies, and references entering the software supply chain What you'll do - Own the business outcomes for Secret Detection and Vulnerability Research, including adoption, expansion, competitive win rate, and revenue contribution. Bring a point of view on packaging and pricing, not just features. - Set the strategy for the full secret lifecycle: prevention, detection, validation, revocation, and reporting across GitLab.com, Dedicated, and Self-Managed. - Treat detection content as a product. Define how rules, advisories, and intelligence feeds are sourced, validated, versioned, and measured, and make quality visible to customers. - Hold the line on detection quality. False positives are a product defect and you will own the metrics that prove precision is improving. - Work at the level of the technology. Read the rule syntax, question the entropy heuristics, understand why a scanner missed something, and challenge engineering with informed alternatives. - Use AI to compress the distance between question and answer. Pull your own data, prototype your own flows, synthesize research and competitive input yourself, and bring conclusions rather than requests for someone else to investigate. - Build the case for where AI belongs in the product: triage, rule generation, remediation guidance, and reducing the human review burden per finding. - Partner with engineering, security research, threat intelligence, Field, and GitLab's own Security team, who are one of your most demanding users. - Communicate in writing, asynchronously, with enough precision that a distributed team can act without a meeting. What you'll bring - Domain depth in application security, vulnerability management, or security research. You have worked on or adjacent to scanners, detection content, threat intelligence, or SDLC security tooling and you know how these products actually get evaluated in a bake-off. - Technical credibility sufficient to earn the respect of a security

Domain depth in application security, vulnerability management, or security research. You have worked on or adjacent to scanners, detection content, threat intelligence, or SDLC security tooling and you know how these products actually get evaluated in a bake-off.

Own the business outcomes for Secret Detection and Vulnerability Research, including adoption, expansion, competitive win rate, and revenue contribution. Bring a point of view on packaging and pricing, not just features. Set the strategy for the full secret lifecycle: prevention, detection, validation, revocation, and reporting across GitLab.com, Dedicated, and Self-Managed. Treat detection content as a product. Define how rules, advisories, and intelligence feeds are sourced, validated, versioned, and measured, and make quality visible to customers. Hold the line on detection quality. False positives are a product defect and you will own the metrics that prove precision is improving. Work at the level of the technology. Read the rule syntax, question the entropy heuristics, understand why a scanner missed something, and challenge engineering with informed alternatives. Use AI to compress the distance between question and answer. Pull your own data, prototype your own flows, synthesize research and competitive input yourself, and bring conclusions rather than requests for someone else to investigate. Build the case for where AI belongs in the product: triage, rule generation, remediation guidance, and reducing the human review burden per finding. Partner with engineering, security research, threat intelligence, Field, and GitLab's Security team. Communicate in writing, asynchronously, with enough precision that a distributed team can act without a meeting.