Technical assurance
Test your AI systems for vulnerabilities, bias, and failure modes before they reach production.
AI Governance tells you what to comply with. AI Safety tells you whether your system is actually safe. You need both. We provide the technical assurance layer — red teaming, model behavior analysis, adversarial testing, and runtime guardrails — that validates your governance framework is working in practice.
Why safety matters now
Overview
AI safety is the technical practice of testing whether AI systems behave as intended — even under adversarial conditions, edge cases, or unexpected inputs. It covers model robustness, output reliability, bias measurement, failure mode analysis, and runtime monitoring.
Where AI Governance addresses compliance frameworks, policies, and board-level oversight, AI Safety validates that the actual system is secure, fair, and reliable. A governance policy that isn't backed by safety testing is a paper shield. We provide the technical depth that makes governance real.
Detect
Find vulnerabilities before attackers or failures impact your business
Measure
Quantify model bias, output reliability, and robustness against adversarial inputs
Remediate
Actionable fixes — guardrails, monitoring, and engineering changes — not just a report
Monitor
Ongoing safety observability so you stay ahead of drift, abuse, and emerging threats
Services
Four capability areas that together provide complete technical assurance for AI systems in production.
A comprehensive evaluation of your AI system's risk posture. We review data pipelines, model behavior, output quality, integration points, and operational controls to identify vulnerabilities before they impact your business.
Adversarial testing against your AI systems using structured attack methodologies. We simulate real-world threats — prompt injection, jailbreaking, data exfiltration, and bias amplification — to find weaknesses before attackers do.
Systematic evaluation of your model's outputs across diverse inputs, populations, and edge cases. We measure fairness, consistency, and drift so you can deploy with confidence that your system treats all users equitably.
Design and deploy runtime safety controls so your AI systems stay safe in production. Content filters, input/output validation, PII redaction, rate limiting, and real-time observability that catches issues as they happen.
Map your AI systems, data flows, and architecture. Identify threat models, regulatory requirements, and business priorities. Define the scope and testing methodology.
Execute automated and manual safety testing — red teaming, bias analysis, robustness evaluation, and adversarial probing. Results are severity-scored and documented with reproducible steps.
Deliver a prioritised remediation roadmap with actionable fixes. Implement guardrails, content filters, and monitoring. Support your team through engineering changes.
Deploy runtime monitoring and alerting. Establish ongoing safety review cadence. Retest after remediation to verify fixes. Regular health checks for evolving systems.