AI Security Governance Architect
Résumé du poste par JobGrid
AI Security Governance Architect at Plain Concepts: Remote, Espagne; Temps plein; IT; Ingénieur sécurité. This listing is part of JobGrid's Emplois IA à distance depuis des pages carrières. JobGrid adds normalized role facts, source context, and a path to the employer application page so candidates can compare the listing before applying.
- Location and workplace: Remote, Espagne
- Role classification: IT, Ingénieur sécurité, Temps plein
- Source freshness: checked by JobGrid on 2026-05-30.
- Application path: candidates continue to the employer application page with non-personal referral tags.
Mission
Support the client’s AI Security Governance Program by defining, operationalizing and continuously improving the cybersecurity control framework for AI, GenAI and agentic AI use cases. The role will work with security, architecture and business teams to ensure AI initiatives are registered, assessed, governed and secured across their lifecycle.
The profile will act as the cybersecurity subject matter expert for AI governance, complementing the project manager and helping translate AI-related risks into practical controls, processes, requirements, evidences and decision criteria.
Key Responsibilities
1. AI security governance framework
Define and mature the security governance model for AI systems, including intake, registration, risk classification, control mapping, approvals, exceptions, monitoring and periodic reassessment.
Align the governance model with recognized frameworks such as NIST AI RMF, NIST Generative AI Profile, ISO/IEC 42001, OWASP Top 10 for LLM Applications, and local relevant ruling as EU AI Act obligations where applicable. NIST’s GenAI Profile was released to help organizations manage unique generative AI risks; ISO/IEC 42001 provides a structured AI management system standard; OWASP tracks LLM-specific risks such as prompt injection, insecure output handling, data poisoning and supply-chain vulnerabilities.
2. AI use case risk assessment
Assess AI and GenAI use cases from a cybersecurity perspective, covering:
- Access control and identity context
- Agentic AI permissions and tool execution
- Logging, monitoring and incident response
- Model exposure and misuse risk
- Prompt injection and indirect prompt injection
- Sensitive data leakage
- Data classification and data residency
- Model supply chain and third-party AI services
- Human oversight and approval workflows
- Security-by-design requirements for AI applications
3. Control design and operationalization
Translate risks into practical security controls, including policies, technical requirements, architecture patterns, guardrails, evidence requirements, control owners and acceptance criteria.
The role should be able to define what “good” looks like for different AI patterns: internal copilots, M365 Copilot, custom GenAI apps, RAG systems, AI agents, vendor AI features, ML models and low-code/no-code AI automations.
4. Tooling integration and control mapping
Work with existing tools such as HiddenLayer, Sentra, Zenity and the AI registration/control tower process to ensure the governance model is not theoretical.
Expected activities include:
- Mapping tool capabilities to governance controls
- Defining required data fields in the AI registry
- Establishing dashboards and control evidence
- Identifying gaps between tooling coverage and policy expectations
- Supporting integration with GRC, CMDB, DLP, IAM, SIEM/SOC, cloud security and data governance processes
6. Deliverables
Typical deliverables should include:
- AI control framework
- AI use case classification model
- Security requirements for AI/GenAI projects
- AI security architecture patterns
- AI registry/control tower data model recommendations
- Tooling-to-control mapping
- Exception and risk acceptance process
- KPI/KRI dashboard proposal
- Security review templates
- AI security awareness material for project teams
- Roadmap for maturity improvement