AI Security Architect

wBrain; Lisboa; Presencial; EUR 90 000 - 150 000; Tempo integral

Resumo da oferta

wBrain is seeking an AI Security Architect in Portugal to lead security architecture, risk assessment, governance, and control definition for AI/GenAI initiatives. You will translate AI security risks into actionable requirements and collaborate with Security, Product, Engineering, Data, and Architecture teams.

The role focuses on secure-by-design practices, enforceable controls, and auditable security outcomes across AI platforms and integrations.

Qualificações

• Proven background in cybersecurity, ideally in Application Security, Product Security, Cloud Security, or Security Architecture.
• Strong understanding of GenAI / LLM-based solutions from a security and risk perspective.
• Experience conducting security reviews, architecture reviews, threat modelling, and defining technical security controls.

Responsabilidades

• Review AI/GenAI use cases throughout their lifecycle from design to production.
• Define security requirements for AI platforms, APIs, AI agents, tool usage, and third-party integrations.
• Assess AI architectures and identify security risks for LLM-based and agentic solutions.
• Perform architecture reviews, security assessments, and threat modelling of AI solutions.
• Identify and mitigate AI-specific risks including prompt injection, data leakage, unsafe tool use, and unbounded consumption.
• Validate AI integrations and deployments against security, privacy, logging, monitoring, and auditability requirements.
• Define and assess controls around IAM, PAM, API security, secrets management, data protection, monitoring, and audit trails.

Conhecimentos

Cybersecurity

GenAI Security

Threat Modelling

Security Architecture

IAM & Privilege

API Security

Logging & Monitoring

Data Protection

Risk Assessment

Communication

Ferramentas

AWS

Azure

Databricks

Guardrails

MCP

Descrição da oferta de emprego

About wBrain

At wBrain, we support organizations across Europe in delivering secure, scalable, and value-driven technology transformation initiatives. We work with enterprise clients to strengthen cybersecurity, enable responsible adoption of emerging technologies, and build secure-by-design digital solutions.

We are currently looking for an AI Security Architect to support an enterprise initiative focused on AI/GenAI security, architecture governance, risk management, and secure-by-design practices.

Role Mission

Support the secure adoption of AI and GenAI solutions by translating cybersecurity and AI-specific risks into practical, enforceable, and auditable technical controls.

The role will work closely with Security, Product, Engineering, Data, and Architecture teams to review AI use cases from design through production and ensure that security, privacy, governance, and compliance requirements are effectively implemented.

Key Responsibilities

Support and review AI/GenAI use cases throughout their lifecycle, from initial design to production.

Define security requirements for AI platforms, APIs, AI agents, tool usage, and third-party integrations.

Assess AI architectures and identify security risks associated with LLM-based and agentic solutions.

Security Architecture & Risk

Perform architecture reviews, security assessments, and threat modelling of AI solutions.

Identify and mitigate AI-specific risks, including:

• Prompt injection and jailbreaks
• Sensitive information disclosure and data leakage
• Unsafe tool use and excessive agency
• Abusive or unbounded consumption
• Insecure integrations and poisoned inputs
• Weak or insufficient trust boundaries

Define appropriate security guardrails for the use of sensitive, personal, confidential, or regulated data.

Validate AI integrations and deployments against security, privacy, logging, monitoring, and auditability requirements.

Define and assess controls around IAM, PAM, API security, secrets management, data protection, monitoring, and audit trails.

Ensure that security controls are not only defined but effectively implemented and demonstrable through appropriate evidence.

Contribute to AI security governance, standards, policies, and secure-by-design patterns.

Support the definition of secure logging, monitoring, audit trails, and control effectiveness measures.

Collaborate with relevant teams to ensure AI solutions meet organizational security, privacy, and regulatory expectations.

Required Experience

Proven background in cybersecurity, ideally in Application Security, Product Security, Cloud Security, or Security Architecture.

Strong understanding of GenAI / LLM-based solutions from a security and risk perspective.

Experience conducting security reviews, architecture reviews, threat modelling, and defining technical security controls.

Solid knowledge of:

• IAM and least privilege
• API security
• Secrets management
• Logging and monitoring
• Security control validation
• Data protection and privacy
• Data minimization and secure handling of sensitive or regulated information

Ability to challenge technical implementations and validate whether security controls are effectively applied.

Preferred Experience

Familiarity with AWS and/or Azure environments, particularly in the context of AI workload security.

Exposure to platforms such as:

• AWS Bedrock

Experience reviewing these environments from a security perspective, including:

• Guardrails and content controls
• IAM and least privilege
• Logging, observability, and auditability
• Data protection and sensitive data handling

Familiarity with Databricks as a platform from a cybersecurity perspective, including:

• Access control and data permissions
• Workspaces, jobs, pipelines, and notebooks
• Secrets management
• Networking
• Data governance

AI Security Frameworks & Emerging Risks

Familiarity with:

• OWASP Top 10 for LLM Applications
• OWASP guidance for Agentic AI
• SAIF (Secure AI Framework)
• Model Context Protocol (MCP) and agent-to-tool security considerations
• Agent-to-Agent (A2A) architectures and agentic trust boundaries
• Emerging AI security risk taxonomies such as MCP-38
• DevSecOps and/or MLOps environments as a collaboration context

Core Skills

Strong ability to translate AI security risks into clear and actionable technical requirements.

Excellent risk identification, prioritization, and decision-making skills.

Ability to challenge technical implementations constructively and validate whether controls are effectively implemented.

Strong communication and collaboration skills across Security, Product, Engineering, Architecture, and Data teams.

Practical mindset focused on enforceable, measurable, and auditable security controls.

High level of autonomy, critical thinking, and structured problem-solving.

This is a cybersecurity role applied to AI/GenAI.

The position is not primarily focused on model development, data engineering, or operational ownership of ML pipelines, notebooks, or AI platforms.

The role is focused on security architecture, risk assessment, governance, control definition, and validation, supporting teams in adopting AI securely and responsibly.