AI Emerging Risks Analyst

OpenAI•Washington, DC

About The Position

We are looking for an AI Emerging Risks Analyst to help us understand potential harms and misuse of AI in a time of rapid, sustained change. From known threat actors misusing new technologies to new threats enabled by new technologies, we seek to scan available signals and use strategic foresight methodologies to enable proactive detection and mitigation. In this role, you will help to provide strategic-level perspective on a range of evolving risk areas, helping to produce actionable risk taxonomies relevant to OpenAI’s platforms, surfaces, and broader business interests. Utilizing mixed quantitative and qualitative methodologies, you will spot early warning signs, pull threads on potentially concerning behavior, and turn weak signals into clear, prioritized risk calls. You will focus on upstream ecosystem scanning, competitive benchmarking, and external narrative/risk sense-making. Your work will help to inform cross-functional partners in the protection and safety stacks to guide mitigations that keep users, brands, and communities safe while allowing productive, creative uses of these tools to thrive.

Requirements

Significant experience (typically 5+ years) in trust and safety, integrity, security, policy analysis, or intelligence work focused on a range of emerging risks situated in strategic context and translated into actionable intelligence.
Demonstrated ability to analyze complex online harms (e.g., harassment, coordinated abuse, scams, influence operations, brand safety issues) and convert all-source analysis into concrete, prioritized recommendations.
Strong analytical skills and comfort working with both qualitative and quantitative inputs, including: (1) Casework, incident reports, OSINT, product context, and policy frameworks. (2) Basic metrics and trends in partnership with data science (e.g., harm prevalence, severity profiles, exposure, escalation rates).
Strong adversarial and product intuition, able to foresee how actors might adapt AI tools for misuse and evaluate how product mechanics, incentives, and UX decisions influence risk.
Experience designing and using risk frameworks and taxonomies (e.g., harm classification schemes, severity/likelihood matrices, prioritization models) to structure ambiguous spaces and support decision-making.
Understanding of the application of foresight methodologies including horizon scanning, scenario planning, tabletop exercises, or simulations.
Proven ability to work cross-functionally with product, engineering, data science, operations, legal, and policy teams, including pushing for clarity on tradeoffs and following through on mitigation work.
Excellent written and verbal communication skills, including experience producing concise, executive-ready briefs and explaining sensitive, complex issues in grounded, concrete terms.
Comfort operating in fast-changing, ambiguous environments: you can identify weak signals, form hypotheses, test them quickly, and adjust as the product and threat landscape evolves.

Responsibilities

Map and prioritize emerging risks
Build and continuously refine a clear picture of emerging signals and trends that could affect the AI ecosystem through upstream and external scanning.
Design and maintain harm taxonomies that provide foresight and warning about how AI harms and misuse may manifest over the next 0-24 months and beyond.
Contribute to an evergreen risk register and prioritization framework that surfaces the top issues by severity, prevalence, exposure, and trajectory.
Detect and deep dive into emerging abuse patterns
Create comprehensive approaches to horizon scanning, competitive benchmarking, and external narrative/risk sense-making.
Stay current on abuse trends ranging from state actor misuse to criminal activity, drawing from the work of internal organizational and cross-functional partners.
Connect individual incidents into system-level stories about actors, incentives, product design weaknesses, and cross-product spillover–whenever possible spotting these incidents or even hypothesizing them before they hit our surfaces.
Turn analysis into actionable risk intelligence
Translate findings into clear, ranked risk lists and concrete proposals for mitigations that product, safety, and policy teams can execute on.
Work with Global Affairs and Communications teams to share findings in ways that reinforce OpenAI’s role as a leader in the online safety ecosystem.
Track whether mitigation work is landing: follow key indicators, pressure-test assumptions, and push for course corrections when the data demands it.
Build early warning and measurement capabilities
Help define the core metrics and signals that indicate whether AI environments are safe (e.g., key harm prevalence, severity distributions, escalation rates, brand safety issues).
Work with data science and visualization colleagues to shape monitoring views and dashboards that highlight leading indicators and unusual changes from signals spotted off platform to determine whether these are manifesting in user behavior or abuse patterns.
Pioneer new uses of our own technologies to scale detection and transform workflows.
Provide strategic analysis and future-looking perspectives
Produce concise but comprehensive strategic intelligence estimates that provide full context about a given interest area that includes confidence levels based on observed data to inform judgments and recommendations.
Run scenario analyses that explore how AI harms might evolve over the next 6–24 months (e.g., how scam networks may use agentic AI; how state actors may seek to misuse new scientific capabilities of frontier models).
Help design and run tabletop exercises for internal and partner audiences that distill manifest and latent risks and identify mitigations.
Benchmark OpenAI’s risk profile and mitigations against external incidents and other platforms, highlighting gaps, strengths, and opportunities.
Shape safety readiness for new products
Contribute to product readiness and launch reviews by laying out expected abuse modes based on broad, upstream understanding.
Turn risk insights into practical guidance for internal teams (product, marketing, partnerships, comms) and, where appropriate, external partners using OpenAI technologies in social and brand contexts.
Develop reusable frameworks, playbooks, FAQs, and briefing materials that make it easier for the broader organization to understand AI risks and respond consistently.