Security Risk Analyst, Risk Engineering

Anthropic•San Francisco, NY

1d•Hybrid

About The Position

The Security Risk team is responsible for how Anthropic identifies, prioritizes, and drives treatment of its most important security risks. We are rebuilding risk management to operate as an engineering function, using automation, quantitative risk and AI-native platforms to enable decision making. The risks we assess span Anthropic's full security landscape, so the team needs breadth across domains and the judgment to go deep wherever a decision demands it. Security Risk, in deep partnership with Security Engineering, helps define the security program and shapes how investment and treatment decisions get made. The conventional GRC playbook was not built for a company shipping frontier AI. You will help define what replaces it, with a direct line to CISO-level decisions. As a Security Risk Analyst you will take risk questions from leadership and partner teams and drive them to a decision. Sometimes that is a fast, structured qualitative assessment and other times it is a deep FAIR-based quantitative analysis. Either way you bring leadership a clear position, the tradeoffs, and honest uncertainty, and you defend it under challenge. In parallel, you will help turn quantitative risk into a product partner teams can leverage, and define the standards a growing risk function will run on.

Requirements

Owned security or technology risk analysis end to end, qualitative and quantitative, and can point to a decision your output changed, whether a funding call, a launch call, a remediation sequence, or a documented acceptance
Hands-on FAIR-style quantification experience, including scenario decomposition, calibrated estimation, and Monte Carlo simulation in Python, R, or spreadsheet tooling, briefed to decision makers
Thrive in ambiguity. You frame a moving question into something analyzable, pick the depth that fits, and drive to a decision rather than a report
Put defensible severity and likelihood on ambiguous problems, state uncertainty honestly, and change your position when the evidence changes
Have enough technical security depth to decompose an attack path with security engineers and be credible in the room
Can compress a complex risk position into one paragraph for the CISO, make the tradeoff explicit, and defend it under pointed questions
Use Claude or other LLMs as daily working tools and review model output critically
Are low ego and collaborative, build credibility through the work, and care about AI safety and the role security risk plays in it

Nice To Haves

Applied FAIR-CAM or another structured approach to control effectiveness and attack path modeling
Built or operated a cyber risk quantification program, or turned quantitative analysis into tooling, templates, or training that others ran
Helped define risk appetite or tolerance thresholds an organization actually used to make decisions
Bring a background in security engineering, detection, threat intelligence, actuarial science, or decision science that grounds the numbers in real systems
Are familiar with security risks specific to AI systems, such as goal or intent modification, and how they change traditional threat models

Responsibilities

Enable leadership and partner teams to make risk-informed decisions.
Take ambiguous risk questions, drive them to a documented decision, and communicate the quantitative and qualitative tradeoffs clearly
Lead quantitative analysis of the company's top security risk scenarios using FAIR, calibrated estimation, and simulation, working with the engineers who own the systems and presenting results in terms leadership can act on
Partner with Security Engineering to assess threat scenarios and control effectiveness so security investment is right-sized to actual risk and remediation is sequenced where it buys down the most risk
Frame escalations and risk treatment decisions with a clear recommendation, an honest statement of uncertainty, and re-evaluation triggers, and pressure test those decisions with risk owners
Shape the analysis and narrative behind leadership risk reviews, and help define risk appetite and the risk metrics the organization should measure and why
Use AI and automation to scale risk analysis, and own the calibration and quality review that keep the outputs trustworthy
Turn one-off analyses into reusable methods, templates, and training so risk analysis becomes increasingly self service for partner teams, and raise the analytical quality of the risk register