Staff Product Manager - AI Detection & Discovery

OneTrust•Atlanta, GA

4d•$139,725 - $186,300•Hybrid

About The Position

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.

Requirements

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, with genuine enthusiasm for applying AI to product work.

Nice To Haves

Familiarity with AI governance frameworks and regulation, including the EU AI Act, NIST AI RMF, and ISO/IEC 42001.
Background in enterprise security or IT tooling, or experience selling to CISO and Chief Data/AI Officer buyers.
Experience with data-quality or ML-driven classification problems where precision and recall are negotiated with customers.
Exposure to agentic systems, MCP, or AI supply-chain risk.

Responsibilities

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.
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.
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.
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.
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.