Security for AI covers the measures and practices to protect AI systems, models, and the data they use from malicious attacks and unintended consequences. It blends cybersecurity, risk management, compliance and responsible AI.
AI with security built-in
We foster a proactive approach to ensure that AI systems are resilient from the start. With automated stress-tests against real-world attack scenarios, vulnerabilities are exposed early. For example, using aikido, any custom code is tested against security practices and automatically hardened. Furthermore, APIs are automatically mapped out and scanned for vulnerabilities.
AI data authorisation architecture
An effective AI data authorization architecture ensures that AI only uses data that users are authorised to access.
In practice, this means combining retrieval-augmented generation (RAG) with workflow orchestration to enforce granular access controls. Using for example n8n, controlling access to data can be automated across the entire pipeline: incoming queries are authenticated, retrieval nodes check against identity and policy rules, and only approved data sources are exposed to the model. This workflow-driven approach allows organisations to align AI usage with IAM and compliance policies and to ensure that every RAG response is both contextually relevant and securely governed.
Responsible AI
Responsible adoption of AI is as much about governance as it is about technology. We support organisations in managing the full AI lifecycle, validating models, and addressing issues such as bias and drift. Controls aligned to enterprise policy keep deployments consistent with business standards.
We emphasise sovereignty, accountability and predictability. Sovereignty means that European data and data owned by customers does not leave Europe. Accountability means that even with agentic AI, somebody (a person) can be named being responsible for the activities that have been carried out. Predictability means, for example, controlling RAG and introducing predictable workflows, e.g. using n8n.
What We Secure
- Models: Protection against adversarial manipulation, data injection, and unsafe behaviors that could compromise outcomes.
- Data: Safeguarding integrity, confidentiality, and provenance of RAG data against corruption, theft, or poisoning.
- Infrastructure: Hardening of compute, networks, MLOps pipelines, and supply chains, whether deployed on-premises or in the cloud.
- Dependability: Ensuring AI systems are transparent, auditable, and aligned with both regulatory expectations and organisational values.
Benefits
- Prevents manipulation of AI behavior and outcomes
- Preserves data quality and integrity
- Reduces risk and accelerates safe deployment
- Demonstrates responsible AI to customers, regulators, and investors
>> See also our Leveraging AI for Security services.
Controlled AI services 