AI Security

AI Security & Governance Architect | GenAI / LLM / RAG / Agentic AI
Location: Belgium – candidates must already be based in Belgium or be happy to relocate themselves to belgium within 1-2 months.
Rate: €900 - €1200 per day (12 month contract +extension)
Working Model: Hybrid – 8 days per month onsite , remainder remote
Engagement: Contract

Role Overview
We are seeking an experienced AI Security & Governance Architect to join a CISO organisation and help define, design and embed the security capabilities required to support the organisation's rapidly evolving adoption of AI.

This is a senior role sitting at the intersection of AI, Cyber Security, Security Architecture and Governance . The successful candidate will work across IT Risk, Security Architecture, security engineering and operational security, while partnering closely with AI architects, engineers, data scientists, product owners and technology teams.

The role requires someone who understands modern AI architectures at a technical level – including Generative AI, LLMs, RAG and Agentic AI – and can translate emerging AI risks into practical security controls, reusable architecture patterns, governance frameworks and a sustainable AI Security roadmap.

This is not purely an AI Governance position . The successful candidate must be technically credible and capable of understanding AI architectures, threat modelling them and designing appropriate security controls.

Key Accountabilities
1. AI Security Governance
Define and validate the AI Security target state, principles, scope and multi-year capability roadmap , aligned with CISO and Technology priorities.
Design and embed the AI Security governance model, including:
Decision rights and accountabilities
RACI
Security approval gates
Escalation paths
Risk acceptance and exceptions
Evidence and assurance requirements
Develop and maintain policies, standards, minimum security requirements, control objectives and implementation guidance for AI systems and AI-enabled technology.

Create an AI Security control baseline , ensuring governance and control design reflects recognised frameworks and guidance including:
OWASP guidance for LLM and Agentic AI applications
MITRE ATLAS
SAFE AI framework
NIST AI Risk Management Framework
ISO/IEC 42001
ISO/IEC 23894
Applicable AI, cyber-security and regulatory requirements

2. Secure AI Architecture & Reusable Security Controls
Build and maintain AI threat scenarios, attack-surface maps and risk scenario catalogues, using OWASP and MITRE ATLAS as key threat references.
Assess current and target AI architectures including:
LLMs and other models
Datasets and data pipelines
RAG architectures
Embeddings and vector stores
Model APIs
Orchestration layers
Tools and plugins
Memory
Agent workflows
Inference and deployment environments
Design and publish reusable AI Security requirements, reference architectures and security patterns .
Define appropriate:
Trust boundaries
Identity and access models
Secrets management
Data-security rules
Network protections
API and tool security
Runtime safeguards
Logging and monitoring
Security testing
Human oversight
Translate business and technical requirements into secure AI solution architectures that are scalable, supportable and proportionate to risk.

3. AI Security Readiness & Capability Roadmap
Assess the organisation's current AI Security maturity across people, processes and technology.
Identify quick wins, structural gaps and capability dependencies and translate these findings into prioritised remediation plans and clear definitions of done.
Design target capabilities covering areas including:
AI data security
Identity and secrets
Secure AI supply chain
AI system security testing
Vulnerability management
Logging and monitoring
Threat detection
Incident response
Establish clear roadmaps, milestones, deliverables, ownership and progress reporting for the implementation of these capabilities.

4. Stakeholder Engagement & AI Security Expertise
Act as a senior CISO subject-matter expert for AI Security , providing authoritative guidance to Technology, AI delivery teams and control functions.

Partner with AI architects, engineers, data scientists, product owners and platform teams to ensure security is incorporated during solution design rather than becoming a late-stage compliance exercise.

Facilitate:
AI threat-modelling sessions
Architecture reviews
Security workshops
AI Security readiness assessments
Control-design discussions
Communicate AI Security risks, decisions and options effectively to technical teams, senior management, auditors, risk functions and regulators.
Monitor developments across AI security s