Securing AI systems, safeguarding data ownership
For organizations where data makes the difference — and must stay protected.
AI creates new attack surfaces and data exposure risks that traditional security frameworks were not designed to handle. W69 AI Consultancy implements defence-in-depth security strategies and data sovereignty controls that protect your AI systems, your data, and your competitive advantage.
AI Security & Data Sovereignty encompasses protecting AI systems against misuse and ensuring control over business data within AI implementations. W69 AI Consultancy in Amsterdam designs secured AI architectures with attention to data privacy, access control and European data sovereignty.
What AI Security & Data Sovereignty delivers
We protect your AI investments across three critical security dimensions that conventional cybersecurity does not adequately address.
AI-Specific Threat Protection
We identify and mitigate AI-specific threats including prompt injection attacks, adversarial inputs, model extraction, training data poisoning, and output manipulation. Our security assessments evaluate your AI systems against established threat taxonomies such as OWASP Top 10 for LLMs, and we implement layered defences that address each attack vector systematically.
Data Sovereignty Architecture
We design data architectures that keep you in control of where your data resides, how it is processed, and who can access it. This includes data classification frameworks, jurisdiction-aware routing, encryption strategies for data at rest and in transit, and architectural patterns that prevent unintended data exposure to third-party AI services. We help you leverage cloud AI capabilities without surrendering data sovereignty.
Security Monitoring & Response
AI systems require continuous security monitoring that goes beyond traditional SIEM approaches. We implement AI-specific monitoring for anomalous model behaviour, unusual query patterns, data exfiltration attempts, and drift in model outputs. Our incident response playbooks cover AI-specific scenarios including model compromise, data breach through AI, and adversarial attack containment.
How we secure AI systems
Our security methodology follows a defence-in-depth strategy tailored to the unique characteristics of AI workloads.
1. Threat Modelling & Risk Assessment
We conduct comprehensive threat modelling specific to your AI systems, identifying attack surfaces, threat actors, and potential impact scenarios. This includes reviewing data flows, model access patterns, integration points, and the AI supply chain. The assessment produces a prioritised risk register with clear mitigation recommendations tailored to your risk tolerance and regulatory requirements.
2. Security Architecture Design
Based on the threat model, we design a security architecture that implements defence-in-depth across all layers — from network segmentation and API security to model hardening and output filtering. We ensure security controls are proportionate, avoiding excessive restrictions that would hinder AI utility while maintaining robust protection against realistic threats.
3. Implementation & Hardening
We implement security controls including input validation pipelines, output filtering, access control models, encryption configurations, and data sovereignty routing. We harden AI models against known attack vectors and establish secure development practices for AI workloads. All implementations are documented and testable, enabling your teams to maintain security posture independently.
4. Red Team Testing & Continuous Security
We conduct red-team exercises specifically targeting your AI systems — testing for prompt injection, data exfiltration, model manipulation, and privilege escalation. Findings are fed back into security improvements. We also establish continuous security monitoring, regular penetration testing schedules, and security review processes for new AI deployments to maintain protection as your AI landscape evolves.
Frequently asked questions
What are the biggest security risks with AI systems?
AI systems face unique security risks including prompt injection attacks, training data poisoning, model theft, adversarial inputs, data leakage through model outputs, and supply chain vulnerabilities in AI toolchains. Additionally, traditional cybersecurity risks apply — but the probabilistic nature of AI systems makes them harder to secure using conventional approaches alone. The attack surface expands significantly when AI systems have access to tools, databases, and external APIs.
What is data sovereignty and why does it matter for AI?
Data sovereignty refers to the principle that data is subject to the laws and governance of the country or region where it is collected or stored. For AI, this matters because model training and inference often involve sending data to cloud services that may be hosted in different jurisdictions. Ensuring data sovereignty means maintaining control over where your data resides, how it is processed, and who can access it — critical for regulatory compliance, competitive advantage, and stakeholder trust.
Can we use cloud AI services and still maintain data sovereignty?
Yes, with the right architecture. Options include using regional cloud deployments that keep data within specific jurisdictions, deploying models on-premises or in private cloud environments, using API gateways that filter sensitive data before it reaches external services, and implementing data classification systems that route different data types to appropriate processing environments based on sensitivity and regulatory requirements.
How do you protect against prompt injection attacks?
We implement multi-layered defences including input validation and sanitisation, system prompt hardening, output filtering, privilege separation between AI components, monitoring for anomalous behaviour patterns, and regular red-team testing. We also design architectures that limit the blast radius of any successful attack by isolating AI components and restricting their access to only the resources they need.
Does W69 help with GDPR compliance for AI systems?
Absolutely. We ensure AI systems comply with GDPR requirements including lawful basis for processing, data minimisation, purpose limitation, right to explanation for automated decisions, data protection impact assessments, and proper data processing agreements with AI service providers. We also implement technical measures like differential privacy, data anonymisation, and access controls that support GDPR compliance by design.
Ready to secure your AI systems?
Let us assess your AI security posture and design a defence strategy that protects your data, models, and competitive advantage.
Schedule a consultationAlso looking for data-driven insights for commercial decision-making?
Our sister company W69 AI Growth offers Data Predictive Intelligence — the commercial, growth-focused counterpart of what we build at enterprise level.
View Data Predictive Intelligence on w69.nl →Related services
AI Security works best alongside these complementary capabilities.
AI Governance & Compliance
Embed security within broader governance frameworks and regulatory compliance programmes.
Learn more →AI Enterprise Architecture
Design security into your AI architecture from the ground up with secure-by-design patterns.
Learn more →LLM Orchestration & Integration
Secure the integration layer where your LLMs connect with enterprise systems and data.
Learn more →