Staff Product Manager, AI Security

Description

The company is a leader in intelligent content management. Its platform helps organizations collaborate, manage the full content lifecycle, secure sensitive content, and redesign business workflows with enterprise AI. Founded in 2005 and listed on the NYSE, it serves large global organizations such as JLL, Morgan Stanley and Nationwide. Headquarters are in Redwood City, California, with offices across the United States, Europe and Asia.

Content moves through the business every day, from contracts and invoices to employee records, financials, product specs and marketing assets. The company wants to add intelligence to that content and help customers rebuild how their organizations work. As enterprise AI spreads, the team is focused on securing that content.

The role is a senior individual contributor position on the product team. Enterprise AI is shifting from single prompts to multi-agent systems and autonomous workflows, and CISOs worldwide now rank protecting enterprise content from these new AI threats as a top priority. The team works with AI/ML engineers, security architects, red-team researchers and corporate security staff to turn current AI threat research into durable platform guardrails and security controls.

The work covers the company's own AI agent, its agentic capabilities, and outside integrations where third-party agents and foundation models connect through Model Context Protocol (MCP) and enterprise APIs.

Responsibilities

• You will set the product strategy, technical direction and roadmap for AI security and agent guardrails across the company's AI, security and wider platform products.
• You will build guardrail frameworks and policy engines that monitor, evaluate and limit what autonomous agents do, including the company's agent, custom workflow agents and external model integrations.
• You will secure connections made through Model Context Protocol (MCP) and third-party AI APIs. This includes strict authentication, isolation of context boundaries, dynamic permissions and safe tool execution.
• You will design layered defenses against advanced AI threats such as direct and indirect prompt injection, jailbreaking, training data extraction, system prompt leakage and coordinated attacks by groups of agents.
• You will work with engineering, applied AI/ML research, security architecture and UX design to ship low-latency, enterprise-grade AI security controls that keep developers productive and keep the user experience smooth.
• You will own metrics for guardrail evaluation accuracy, prompt injection prevention rates, MCP integration adoption, and revenue growth from the security offering and the top enterprise plan.
• You will work with product marketing, sales, customer success and compliance on go-to-market strategy, positioning the company as the leader in safe, trusted enterprise agentic AI.
• You will talk directly with enterprise customers, AI security working groups and industry analysts to spot new attack patterns, test product requirements and shape industry best practices.

Requirements

• You work AI-first. You use AI tools to make faster, better-informed decisions, with a growth mindset aimed at multiplying your impact 10X.
• You have 8+ years of product management experience building and scaling enterprise B2B SaaS security products, cybersecurity platforms, cloud security solutions, or AI/ML infrastructure platforms.
• You have deep knowledge of AI/ML security, LLM application security (such as the OWASP Top 10 for LLM Applications), prompt injection defense, agent guardrail frameworks, or model alignment and safety systems.
• You have a record of setting product strategy for agentic architectures, multi-agent coordination, tool use and function-calling security, or model integration protocols such as Model Context Protocol (MCP).
• You understand the threats aimed at autonomous AI systems, including multi-turn adversarial attacks, indirect injection through ingested content, and coordinated attacks by agent groups.
• You have enough technical depth to work with AI/ML research and engineering leaders on vector embeddings, semantic evaluation models, LLM latency trade-offs, API gateway security and zero-trust data access control.
• You analyze well enough to define guardrail telemetry and measure how well threats are blocked, and you can balance strict safety limits against responsive AI. You also have the executive presence to persuade C-level security leaders and align cross-functional teams.
• Preferred skills: experience with frontier AI/LLM models, prompt evaluation frameworks, red-teaming AI systems, or building real-time guardrail runtime libraries (such as NeMo Guardrails, Llama Guard or proprietary guardrail pipelines).
• Preferred skills: hands-on work with MCP (Model Context Protocol), agent orchestration frameworks, or semantic router architectures.
• Preferred skills: familiarity with enterprise compliance standards, zero-trust architectures, data loss prevention (DLP), or AI governance frameworks such as NIST AI RMF and ISO 42001.
• Preferred skills: a Bachelor's or Master's degree in computer science, cybersecurity, artificial intelligence, engineering, or equivalent practical experience.

Conditions

You must work from your assigned office at least 3 days per week, as the company values in-person collaboration.

The recruiter will explain more about working arrangements and culture during the hiring process.

The company welcomes diverse backgrounds, and candidates who meet most but not all of the requirements are encouraged to apply.