AI that doesn't go wrong — or tells you early when it might.
Red-teaming, guardrails, AI governance frameworks, and EU AI Act compliance — for organisations that can't afford an AI incident.
AI safety and governance is the discipline of making AI systems behave reliably, within defined boundaries, under adversarial conditions and edge cases. It covers technical safety (guardrails, red-teaming, robustness testing), process safety (audit trails, model cards, human oversight workflows), and regulatory compliance (EU AI Act, GDPR Article 22, sector-specific AI regulations). As AI becomes embedded in consequential decisions, safety is no longer optional.
80%
of high-risk AI systems under the EU AI Act will require conformity assessment by 2026
45%
of enterprise AI incidents trace back to inadequate testing before deployment
6–12 wks
typical EU AI Act readiness assessment and gap-fill timeline
What's included
Services within AI Safety, Governance & Compliance
Each is a scoped engagement. Tell us which one fits your situation — or book a call and we'll scope it together.
AI Red-Teaming & Adversarial Testing
Systematic adversarial evaluation of AI systems — prompt injection attacks on LLMs, adversarial image perturbations for vision models, and edge case enumeration — to find failures before users do.
AI Guardrails Implementation
Input validation, output filtering, content moderation, topic steering, and confidence-based fallback routing — preventing AI systems from producing harmful, off-policy, or legally risky outputs.
AI Governance & Compliance Frameworks
EU AI Act risk classification, model cards, system cards, conformity assessment documentation, incident response procedures, and ongoing compliance monitoring.
Federated Learning
Privacy-preserving model training across distributed data sources — train on patient data, financial records, or proprietary datasets without centralising them, using federated averaging and differential privacy.
Privacy-Preserving AI
Differential privacy, secure multi-party computation, and data minimisation techniques — for AI systems that must operate on sensitive data under GDPR, HIPAA, or financial privacy regulations.
The problem
Where AI safety fails in practice
These aren't edge cases — they're what we hear on almost every discovery call. If any of them sound familiar, this is likely the right place to start.
Red-teaming is skipped or superficial — adversarial inputs that break models in deployment were findable in testing
Guardrails are added as an afterthought rather than designed into the system architecture
Audit trails are incomplete — when an AI decision is challenged, there's no record of what data it was based on
Bias assessment is limited to aggregate metrics that miss subgroup harms
Regulatory requirements (EU AI Act risk tiers, sector-specific rules) are misclassified — teams discover compliance obligations late
Who it's for
This is the right fit if…
These systems work best for organisations at a specific point — where the problem is real, the data exists, and generic tools have already proved insufficient.
Regulated industries (finance, healthcare, insurance) deploying AI in consequential decisions
Any EU business deploying AI systems classified as high-risk under the EU AI Act
Enterprises with AI incident history or board-level concern about AI liability
Organisations deploying LLMs in customer-facing roles where output quality is reputationally critical
Common questions
What people ask before they book
Not sure where to start?
Talk it through on a free call.
We'll help you figure out which of these fits your situation — no pressure, no obligation.
Book a Free 30-Min Call