Whatyou'llDo
- Partnering with engineering, product, security, and legal teams to build, implement, and scale security controls that mitigate risks arising from AI/ML and agentic systems.
- Establishing and operationalizing an enterprise AI governance framework - including defining policies, translating them into actionable technical control requirements, and ensuring effective implementation across AI systems.
- Defining and coordinating guardrails for AI systems across inputs, outputs, and inter-agent communication (e.g., A2A, MCP etc.), ensuring safety boundaries, content governance, and misuse prevention across orchestration and integration frameworks.
- Crafting and enforcing robust access controls for tools, data sources, and enterprise systems accessible to AI agents, ensuring least-privilege access, secure invocation patterns, auditability, and clear segregation of duties across agent platforms and integration layers.
- Identifying critical control points in autonomous agentic workflows where Human-in-the-Loop (HITL) review is required to mitigate high-risk decisions or actions.
- Developing continuous surveillance methods and controls to ensure alignment with AI governance standards.
- Ensuring alignment with internal policies and external regulatory frameworks (e.g., ISO/IEC 42001, NIST AI RMF, EU AI Act).
- Evaluating threat models across the AI lifecycle to address risks including prompt injection, data poisoning, adversarial attacks, model compromise, etc.
- Developing Key Risk Indicators (KRIs) and metrics to monitor AI security posture and report trends to senior leadership and risk committees.
- Supporting internal audits and regulatory examinations related to AI governance and cybersecurity risk.
- Staying current on emerging AI technologies, agentic architectures, evolving threat landscapes, and industry guidelines.
What you need to succeed
- 3+ years of hands-on experience in AI governance / Responsible AI, including defining controls, risk assessments, compliance oversight, or assurance of AI/ML systems.
- 6+ years of experience in risk management, cybersecurity, compliance, or consulting with experience in client-facing or multi-functional delivery.
- Demonstrated experience translating AI governance requirements into technical security controls and working with engineering teams to implement and scale them.
- Experience performing AI threat modeling across traditional ML and Generative AI systems, including agent-based architectures.
- Familiarity with agentic AI technologies and protocols, including AI agents and autonomous workflows, Model Context Protocol (MCP), Agent-to-Agent (A2A) communication etc.
- Familiarity with AI governance standards and regulatory frameworks such as NIST AI RMF, ISO 42001, EU AI Act, and others.
- Experience with governance, risk, and compliance (GRC) platforms and collaboration with privacy, legal, and security teams.
- Knowledge of access control frameworks for AI systems, particularly regarding tool invocation and agent permissions.
- Bachelor's degree or equivalent experience in Computer Science, Information Systems, or related field required; advanced degree or equivalent experience preferred.
- Relevant certifications like AIGP, CISSP, CISA, CISM, Security+, ISO 42001 Implementer/Auditor, or an equivalent credential.
About Adobe
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