Staff Product Manager - AI Detection & Discovery

Job description

### Strength in Trust OneTrust’s mission is to enable innovation through the responsible use of data and AI. We believe that ensuring data is trusted shouldn’t slow teams down—it should accelerate what’s possible. This led us to develop the first technology platform for responsible data use in 2016. Today, with AI representing the latest and most impactful expansion of data yet, OneTrust is once again redefining what responsible innovation looks like. OneTrust, the AI‑Ready Governance Platform™, unifies regulatory intelligence, automation, and connected governance workflows so businesses can continue to move at the speed of AI while ensuring good governance to prevent data misuse at scale. Trusted by thousands of organizations worldwide, OneTrust is shaping the future where trusted data becomes a transformative force for business and society. ### The Challenge You cannot govern what you cannot see. AI is entering the enterprise faster than any technology before it—often without a ticket: a team deploys a foundation model endpoint in an unmonitored cloud account, an engineer self-hosts an open-weight model, or a SaaS vendor quietly adds an AI feature to a widely used tool. OneTrust is looking for a Staff Product Manager to own Detection—the discovery layer that finds AI models, LLM applications, agents, and AI-enabled features wherever they run, and turns that visibility into a governable inventory. Detection is the front door to AI governance: every downstream risk assessment, policy control, and compliance obligation depends on the completeness and accuracy of what you surface. ### Your Mission **Strategy & Roadmap** - Own product strategy for the Detection area across multiple related products, balancing delivery across the full product line. - Set the Shadow AI detection strategy: discover unsanctioned models, applications, and agents; attribute owners; risk-rank assets; and route them into governance and remediation workflows. - Define and sequence the coverage roadmap across detection surfaces, weighing build vs. partner decisions, connector investment, and the risk retired by each incremental surface. - Establish the detection taxonomy and definition of a “discovered AI asset,” bringing models, hosted endpoints, fine-tunes, RAG pipelines, agents, and embedded vendor features into one coherent inventory. - Own the quality bar with measurable targets for coverage, precision, recall, time-to-discovery, and false-positive rate; make tradeoffs explicit. - Identify adjacent opportunities, including continuous monitoring, AI bill of materials, third-party and supply-chain AI risk, and usage telemetry. **Cross-Functional Leadership** - Lead geographically distributed engineering, design, data science, and detection research teams through ambiguous, discovery-heavy problems. - Align with Product Marketing, Sales, and Partners on positioning and go-to-market in a category where buyers compare coverage claims. - Partner with Security, IT, and Legal stakeholders—internally and at design-partner customers—to test detection signals against real enterprise adoption. - Work with Ecosystem and Partnerships on cloud marketplaces and integrations with adjacent tools (CSPM, SSPM, CASB, IdP, EDR, and SaaS management) where telemetry can be shared rather than rebuilt. **Technical & Domain Depth** - Serve as the Detection subject matter expert while building knowledge of adjacent AI governance and privacy products. - Bring deep understanding of AI deployment, including managed model services (Bedrock, Azure OpenAI, Vertex AI), commercial LLM APIs, self-hosted open-weight models and inference servers, fine-tuning and training pipelines, vector databases, RAG architectures, and agent frameworks. - Understand detection mechanics well enough to make hard approach decisions across API and control-plane enumeration, log and network telemetry, OAuth and identity analysis, code and dependency scanning, endpoint and browser signals, and vendor questionnaires or attestations where technical discovery cannot reach. - Translate requirements from the EU AI Act, NIST AI RMF, ISO/IEC 42001, and emerging state AI laws into concrete detection and inventory requirements. **Escalation & Communication** - Serve as the primary escalation point for the product line, de-escalating issues and communicating clearly from individual engineers to executives. - Represent Detection strategy with customers, analysts, and prospects, and bring those insights back into the roadmap. **Mentorship** - Informally mentor Product Managers and Associate Product Managers, sharing strategic and domain expertise. - Raise the broader product organization’s bar for reasoning about AI systems, detection quality, and evidence-based prioritization. ### You Are A subject matter expert who handles significant complexity and exercises independent judgment. You set direction when the problem space is still forming, are sought out by Product Managers for strategic guidance, and thrive while juggling multiple priorities under aggressive timelines. You are intellectually honest about detection quality: you would rather ship narrower coverage you can defend than a number that fails a customer audit. **Your Experience Includes** - Bachelor’s degree in Computer Science, Engineering, Business, or a related field. - 8+ years of Product Management experience. - Proven success managing a product through its full lifecycle, including end-of-life planning. - Experience shipping discovery, inventory, or detection products in security, data discovery, asset management, CSPM/SSPM/CASB, observability, or a comparable domain where coverage and signal quality are the product. - Working fluency with cloud platforms and AI services, as well as on-premises and self-hosted inference stacks; able to contribute effectively in architecture reviews. - Experience with integration and connector-heavy products, including maintaining them as upstream APIs change. - Experience contributing to technical debt and automation-backlog planning. - Recent experience using Agile/Scrum methodology. - Hands-on professional use of AI tools (e.g., Claude, ChatGPT, Notion AI, or similar) to accelerate research synthesis, PRD writing, or roadmap analysis