Security Controls Assurance Lead

Anthropic Anthropic · AI Frontier · San Francisco, CA · Security

This role focuses on defining and validating security controls for AI systems and autonomous operators within Anthropic. It involves translating regulatory and compliance obligations into actionable requirements for engineering teams, assessing implementations, and ensuring AI-performed controls meet external audit and regulatory scrutiny. The role requires strong collaboration between security, engineering, and GRC teams, with a focus on integrating compliance into the AI development lifecycle.

What you'd actually do

  1. Define the control framework and requirements for autonomous AI operators in collaboration with Security, Internal Audit, and Engineering, including change review and approvals, human-in-the-loop, and evidence collection. Assess implementations against those requirements.
  2. Pressure-test major infrastructure, system, and agent framework changes for control impact during design, before decisions become expensive rework.
  3. Set the compliance bar for home-built systems. Collaborate with teams to define what the internal system must provide from day one, such as auditability, segregation of duties, and change control over the tool itself.
  4. Define the criteria for where and when AI can operate, supplement, or replace a manual process or control, including the human-in-the-loop thresholds and evidence documentation.
  5. Establish the validation, evidence, and governance standards that allow AI-performed and AI-assisted processes and controls to withstand external audit and regulatory scrutiny.

Skills

Required

  • Support technology control programs through SOX readiness or as a public company or with equivalent rigor (FedRAMP, large multi-framework SOC 2/ISO portfolios).
  • Genuine engineering fluency: ability to read code and Terraform, follow a CI/CD pipeline, and challenge a design on its technical merits.
  • Programming skills in Python or at least one systems language such as Go, Rust, or C/C++.
  • Deep familiarity with developer platform, release engineering, or infrastructure control domains.
  • Strong collaborator and communicator.
  • Use Claude and other LLMs as daily working tools, and have grounded, specific views on which audit and assurance workflows AI can run today and which it can't yet.
  • Translate framework and regulatory language into acceptance criteria engineers can build against, and translate engineering reality back into assurance language auditors and leadership can rely on.
  • Default to getting the requirement designed into the system rather than papering over the gap with procedure.

Nice to have

  • Combination of audit or advisory experience (Big 4 or equivalent) with in-house experience at an AI-forward tech company.
  • Defined or assessed controls for AI/ML systems or agents acting in production environments.
  • Stood up continuous controls monitoring or automated evidence programs.

What the JD emphasized

  • control framework
  • control impact
  • compliance obligations
  • control design
  • control environment
  • control requirements
  • control testing
  • controls for AI/ML systems