Security Risk & Compliance, Agent Security
Anthropic · San Francisco, CA | New York City
- Location
- San Francisco, CA | New York City, NY
- Posted
- Today
About this role
About Anthropic
Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.
About the team
Anthropic's Security Governance, Risk, and Compliance (GRC) team is the connective tissue that holds the company accountable to its security commitments. We translate regulatory, customer, and voluntary obligations into controls that teams act on, and give leadership a bird's-eye view of how well we're meeting them. We're building toward a fundamentally different kind of GRC: one that directs Claude, with the right humans in the loop, to challenge and evidence the performance of controls continuously rather than through periodic audits.
Responsibilities:
- Serve as the security policy partner for AI agent governance, authoring the policies and standards for how agents are built, deployed, operated
- Define review and approval criteria when an agent may interact with sensitive or regulated data, and which actions require human approval as it relates to compliance frameworks
- Serve as the GRC reviewer within existing engineering approval workflows
- Define data governance requirements for agents, including least-privilege access, access reviews, and handling of regulated and sensitive data
- Monitor agent behavior over time and adapt governance requirements in partnership with cross-functional security, research and engineering teams
- Map agent security and data controls to AI governance frameworks (e.g., ISO 27001, ISO 42001, EU AI Act), strengthening Anthropic's certification efforts ensuring agent security is auditable
- Assess and document agent-related security and compliance risks, driving them to resolution with engineering owners
- Own the policy for agent-related risk acceptances and exceptions, including approval authority, duration and re-review
You may be a good fit if you:
- Have 8+ years in security governance, risk, and compliance, including ownership of security policies and control standards at a technology company
- Have owned or meaningfully supported efforts to compartmentalize sensitive data, PII, or intellectual property, and understand how identity and access mechanisms implement or undermine protection boundaries
- Think about non-human identities as a distinct risk and control category, and can design least-privilege access and accountability controls for systems acting on a person's behalf
- Can reason from a threat model to a control and explain the tradeoff to both an engineer and an auditor
- Have defined risk-based review or approval criteria within engineering workflows and kept them effective under high change velocity
- Deliver clear, precise written work for both technical and non-technical audiences
- Thrive in ambiguous environments with no settled industry playbook, where you're expected to propose the standard rather than wait for one
- Are energized by being the security and risk authority who educates and influences outcomes rather than only advises
Strong candidates may also:
- Understand the security properties of LLM agents, whether from an AI, developer-platform, or agent-tooling company, or through comparable experience
- Bring familiarity with AI governance frameworks alongside traditional security frameworks
- Have adapted controls from regulated or highly sensitive environments to an open, high-trust engineering culture
- Possess relevant certifications (CISSP, CISM, CRISC, CISA, CCSP, ISO 27001 or ISO 42001 Lead Implementer/Auditor)
Candidates need not have:
Done everything on this list before. Very few people have governed AI agents at scale - we care more about how you reason about access, accountability, and blast radius than about a prior title.
Deadline to apply: None. Applications will be received on a rolling basis.
The annual compensation range for this role is listed below.
For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role.
Annual Salary:$255,000-$345,000 USD
Logistics
Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience
Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience
Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position
Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.
Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.
We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team.
Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links - visit anthropic.com/careers directly for confirmed position openings.
How we're different
We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact - advancing our long-term goals of steerable, trustworthy AI - rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills.
The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences.
Come work with us!
Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.
As published by Anthropic. Applications are handled on their site.
About Anthropic
We're an AI research company that builds reliable, interpretable, and steerable AI systems. Our first product is Claude, an AI assistant for tasks at any scale. Our research interests span multiple areas including natural language, human feedback, scaling laws, reinforcement learning, code generation, and interpretability.
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