AI Security Governance Architect

Remote from: Spain
Annual salary: Undisclosed
Salary information is not provided for this position. Check our Salary Directory to estimate the average compensation for similar roles.
Department: Legal & Compliance
Employment type: Full Time,
Job posted: 18 May 2026
Apply before: 18 Jun 2026
Experience level: Senior
Views / Applies: 17 / 2

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AI Summary

The AI Security Governance Architect role at Nestlé focuses on defining and operationalizing a cybersecurity control framework for AI, GenAI, and agentic AI use cases. The position involves working with security, architecture, and business teams to ensure AI initiatives are securely governed across their lifecycle. Key responsibilities include developing an AI security governance model aligned with frameworks like NIST AI RMF and ISO/IEC 42001, conducting risk assessments, designing practical controls, and integrating with existing security tools. The ideal candidate has 8+ years in cybersecurity with strong experience in security governance, architecture, and AI-specific risks such as prompt injection and data leakage. The role requires a hands-on approach to make the governance program executable rather than just producing policy documents.

Job Complexity

Easy Hard

AI Insight This role requires deep expertise in both cybersecurity and AI/GenAI risks, plus the ability to build operational governance frameworks at an enterprise scale, making it highly challenging.

Salary Analysis

AI Insight The salary for this role is not explicitly provided, but based on market data for a senior AI security architect position in the US, the median is around $175,000. This reflects the high demand and specialized skill set required.

Key Skills

AI Security Governance Risk Management GenAI LLM Security NIST AI RMF ISO/IEC 42001 Cybersecurity Cloud Security Security Architecture

Cover Letter Sample

I am writing to express my strong interest in the AI Security Governance Architect position at Nestlé. With over 10 years of cybersecurity experience, including deep expertise in AI/GenAI risk management and security governance, I am confident in my ability to help operationalize your AI security program.

In my previous role at a global enterprise, I designed and implemented an AI governance framework aligned with NIST AI RMF and ISO/IEC 42001, covering risk classification, control mapping, and continuous monitoring. I have hands-on experience assessing LLM applications for prompt injection, data leakage, and supply chain vulnerabilities, and I have integrated governance processes with tools like SIEM, DLP, and GRC platforms.

I understand that Nestlé needs a practitioner who can build executable controls, not just documentation. I am skilled at translating complex risks into practical requirements, architecture patterns, and KPI-driven oversight. I am eager to bring my technical depth and strategic mindset to your team to secure AI initiatives across the organization.

Thank you for considering my application. I look forward to the opportunity to discuss how I can contribute to Nestlé's AI security governance program.

Possible Interview Questions

Can you describe your experience with building an AI security governance framework from scratch? What steps did you take and what challenges did you face?

In my previous role, I led the creation of an AI governance framework for a large financial institution. I started by conducting a risk inventory of all AI use cases, then mapped them to controls based on NIST AI RMF. Key challenges included gaining buy-in from business units and integrating with existing security tools. I overcame these by developing a clear risk classification model and demonstrating quick wins with pilot projects.

How would you assess the security risks of a custom GenAI application that uses RAG?

I would evaluate the data ingestion pipeline for sensitive data leakage, the retrieval mechanism for prompt injection risks, and the LLM output for insecure handling. I'd also review access controls, logging, and monitoring. For RAG specifically, I'd assess the vector database security and ensure that the model does not expose proprietary information.

Explain how you would integrate AI governance controls with existing tools like SIEM and DLP.

I would map control requirements to tool capabilities, such as using SIEM for logging and anomaly detection of AI model interactions, and DLP for monitoring data exfiltration via AI outputs. I'd define data fields and alerts in the SIEM for prompt injection attempts and configure DLP policies to block sensitive data from being sent to external AI services.

What is your approach to handling exceptions and risk acceptance in an AI governance program?

I would establish a formal exception process with a risk acceptance form that documents the control gap, business justification, compensating controls, and a remediation timeline. Exceptions would be reviewed by a risk committee and tracked in the AI registry. I'd also set up periodic reassessments to ensure that exceptions are not permanent.

How do you stay current with evolving AI threats and regulatory requirements like the EU AI Act?

I regularly follow industry publications like OWASP LLM Top 10, NIST updates, and regulatory guidance. I also participate in AI security forums and attend conferences. For the EU AI Act, I map requirements to our governance controls and adjust risk classifications accordingly. I also conduct quarterly reviews of our framework to incorporate new threats.

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

Requirements

Must have:

8+ years in cybersecurity, with strong experience in security governance, security architecture, risk management or AppSec/CloudSec.

Real understanding of AI/GenAI security risks, especially LLM application risks, prompt injection, data leakage, model supply chain, AI agent permissions, RAG security, model/API exposure and third-party AI usage.

Ability to build governance that works operationally, not just policy documents. This is important: Nestlé likely does not need someone to explain that AI is risky; they need someone who can help make the program executable.

Experience with enterprise control frameworks

Excellent documentation and communication skills, with the ability to produce executive-ready material and technical control definitions.

Strongly desirable:

Experience with one or more of:

AI governance programs
AISPM Experience
GenAI application security reviews
M365 Copilot / enterprise copilots
AI agent governance
ML/LLM model risk management
Data Security Posture Management
Cloud security architecture
Secure SDLC / DevSecOps
Third-party AI vendor risk
GRC tooling and control evidence automation
SOC monitoring for AI-related threats

Experience with tools such as HiddenLayer, Sentra, Zenity, Wiz, Microsoft Purview, Defender, CSPM/CWPP, DLP, SIEM/SOAR, cloud-native security tooling or GRC platforms would be valuable.

Certifications / knowledge:

Useful but not mandatory:

CISSP, CISM, CRISC or equivalent
Cloud security certifications: AWS, Azure, GCP, CCSP
AI governance / AI risk training
Privacy knowledge: GDPR, DPIA, data classification
Familiarity with EU AI Act requirements for deployers of high-risk AI systems, including governance, monitoring, human oversight and logging obligations where applicable.

Benefits

Salary determined by the market and your experience 🤑

Flexible schedule 35 Hours / Week 😎
Fully remote work (optional) 🌍
Flexible compensation (restaurant, transport, and childcare) ✌
Fully free health insurance, with a co-payment for dental services 🚑
Individual budget for training or equipment and free Microsoft certifications 📚
English lessons 🗽
Birthday day off 🌴🥳
Monthly bonus for electricity and Internet expenses at home 💻
Discount on gym plan and sports activities 🔝
Plain Camp (annual team-building event) 🎪
Extra perks: events attendance and speakers, welcome pack, baby basket, Christmas basket, discount portal for employees ➕ The pleasure of always working with the latest technological tools!

Will you let us know you better?

The selection process: Simple, just 3 steps.

Phone screen
2 interviews with the team 🤘

What is Plain Concepts?

Plain Concepts is a global company of over 500 people passionate about technology and innovation. Since our founding, we have grown through technical proficiency and confidence in ideas that others might consider risky, creating custom solutions for our clients. With offices in more than 6 countries, our mission is to continue to drive cuttingedge projects around the world.

We are highly committed to technical excellence. We are known for developing highly customized projects, offering specialized technical consultancy and training.

Thanks to the great work of our technicians, we have been recognized for our ability to lead innovative projects that generate value, from artificial intelligence to blockchain, driving solutions that help companies optimize their performance.

What we do at Plain Concepts?

We pride ourselves on being a 100% technical team, dedicated to crafting custom projects from scratch, offering expert technical consultancy, and providing top-tier training.

Our approach goes beyond traditional outsourcing; we focus on creating value together with our clients.
Our teams are diverse and multidisciplinary, operating in a flat, collaborative structure.
We live and breathe AGILE principles, ensuring flexibility and efficiency in everything we do.
Knowledge-sharing is at our core: from supporting each other internally to contributing to the broader tech community through conferences, events, and talks.
Innovation drives us — even the boldest ideas are welcome here.
Transparency underpins all our relationships, fostering trust and long-term partnerships.

Want to learn more?

Check out our website! ➡ https://www.plainconcepts.com/

At Plain Concepts, we certainly seek to provide equal opportunities. We want diverse applicants regardless of race, colour, gender, religion, national origin, citizenship, disability, age, sexual orientation, or any other characteristic protected by law.

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