Senior Product Manager - Secret Detection and Vulnerability Research

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 engineering team. You do not need to have written the scanner, but you should be able to reason about detection logic, data pipelines, CI integration, and the tradeoffs between coverage and noise.
• Commercial reasoning. You start from revenue mechanics, buyer motion, and competitive displacement, then work inward to product decisions. Candidates who reason only from feature lists outward are not a fit.
• Evidence of using AI as a force multiplier in your own work: research, analysis, data pulls, prototyping, drafting. Consuming a chat assistant occasionally is not the same as restructuring how you work.
• Judgment under ambiguity. You bring structured options and a recommendation instead of escalating an open question.
• Bias for clarity. You can take a noisy, technical, politically contested problem and produce one page that everyone can align on.
• Bonus: hands-on background as a developer, security engineer, red teamer, or researcher; experience with credential and token ecosystems; experience commercializing a data or intelligence asset.

About the team

This role sits in GitLab's Security product management organization, which owns application security testing, vulnerability management, supply chain security, secrets management, and AI governance. The Security Section is central to GitLab's Ultimate tier and to the shift toward consumption-based product revenue, so the work is visible to senior leadership and directly tied to company results. You will work asynchronously with engineering, design, and research counterparts across multiple regions, and with the Field teams who take this to market.