Trust, Safety & Governance
How we build AI systems that are safe, compliant, and auditable — without slowing down delivery.
Our principles
Three commitments that guide every system we design, build, and deploy.
Safety by default
Every AI system we build starts with guardrails, not adds them later. We define failure modes before writing the first prompt. Safety constraints are architectural decisions, not afterthoughts.
Transparency over black boxes
Our clients see how their AI systems make decisions. We build explainability into the product: confidence scores, source citations, decision logs. If the AI can't explain itself, it shouldn't be making decisions.
Compliance as enablement
Regulation isn't a blocker — it's a design constraint that makes products better. We build to EU AI Act, GDPR, SOC 2, and HIPAA requirements from day one, so compliance is a feature, not a retrofit.
What we do in practice
Principles are only useful if they translate into concrete actions. Here is how we operate across every engagement.
Data Handling & Privacy
- PII minimisation — only collect and process what's needed
- Encryption at rest and in transit (AES-256, TLS 1.3)
- Access controls with audit logging
- Data retention policies aligned to client requirements
- GDPR-compliant processing with Data Processing Agreements
- No training on client data without explicit consent
Model Selection & Governance
- Provider evaluation rubric: reliability, privacy, cost, and lock-in assessment
- Model access through enterprise APIs only — no public-facing consumer endpoints
- Version pinning to prevent model drift
- Regular benchmarking against baseline metrics
- Multi-provider strategy to prevent vendor lock-in
Testing & Red-Teaming
- Automated eval suites running on every deployment
- Red-team exercises for prompt injection, jailbreaking, and data extraction
- Bias testing across protected characteristics
- Regression testing to catch model degradation
- Adversarial testing for edge cases and failure modes
Monitoring & Observability
- Real-time monitoring of output quality, latency, and error rates
- Automated alerts for quality degradation
- Comprehensive logging of all AI interactions (inputs, outputs, model versions)
- Cost tracking per request and per user
- Drift detection for model performance over time
Incident Response
- Defined escalation paths for AI-related incidents
- Kill switches and graceful degradation — any AI feature can be disabled without taking down the product
- Post-incident review process with concrete remediation actions
- Communication templates for client notification
Compliance & standards
We build to these frameworks from day one — not as a checkbox exercise after the fact.
EU AI Act
We classify systems by risk tier and apply the required obligations. For high-risk systems, we maintain conformity assessments, technical documentation, and human oversight mechanisms ahead of the August 2, 2026 deadline.
GDPR
Data Processing Agreements, lawful basis documentation, and Data Protection Impact Assessments are standard on every engagement. We implement privacy by design and data minimisation at the architecture level.
SOC 2 Type II
Our infrastructure and processes are designed around the Trust Services Criteria: security, availability, processing integrity, confidentiality, and privacy. We support client SOC 2 audits with evidence packages.
OWASP LLM Top 10
Every AI system is tested against the OWASP Top 10 for Large Language Model Applications — including prompt injection, insecure output handling, training data poisoning, and model denial of service.
NIST AI RMF
We follow the NIST AI Risk Management Framework to govern, map, measure, and manage AI risk throughout the system lifecycle. This includes continuous monitoring and stakeholder engagement.
ISO 27001
Information security management aligned to ISO 27001 controls, covering access management, incident response, asset management, and supplier security across all client engagements.
Common questions
Do you train AI models on our data?
No. We never use client data to train or fine-tune models without explicit, documented consent. When we use third-party AI providers, we select enterprise tiers that contractually prohibit the provider from training on your data. Your data stays yours.
How do you handle PII in AI pipelines?
We apply PII minimisation at every stage. Where possible, we strip or pseudonymise personal data before it reaches any AI model. When PII must be processed, we enforce encryption in transit and at rest, strict access controls, and full audit logging. All processing is documented in a Data Processing Agreement.
What happens if the AI makes a mistake in production?
Every AI system we build includes kill switches that let you disable any AI feature without affecting the rest of the product. We set up automated monitoring that detects quality degradation in real time. When incidents occur, we follow a defined escalation path: contain, investigate, remediate, and communicate — with a post-incident review to prevent recurrence.
How do you stay current with AI regulation?
We actively track regulatory developments across the EU AI Act, GDPR enforcement actions, NIST guidelines, and sector-specific requirements. We participate in industry working groups and update our governance frameworks quarterly. When regulations change, we proactively assess impact and adjust our clients' systems before deadlines hit.
Ready to build AI you can trust?
Book a call to discuss your security and compliance requirements.