SenSafe AI - Independent AI Safety Testing

Flawed Benchmarks, Hidden Gaps, and Why AI Testing Must Be Tailored

A new study has revealed that most AI safety and performance benchmarks contain major methodological flaws, raising questions about how progress in AI is being measured. Generic tests often overlook contextual risks and what’s harmful or acceptable depends on who the system serves and how it’s used. To close this gap, organisations need evaluation frameworks that are tailored, weighted, and continuously updated to reflect real-world conditions.

AI Growth Labs and the Future of Responsible Experimentation

The UK’s proposed AI Growth Labs aim to replace rigid regulation with evidence-based oversight. By allowing AI systems to be tested in supervised, real-world sandboxes, policymakers hope to accelerate innovation while safeguarding public trust.

Does the EU AI Act Apply to My Business?

The EU AI Act marks a turning point in how artificial intelligence will be governed, but many businesses remain unsure whether it applies to them. While only high-risk systems face mandatory conformity assessments, the reality is more fluid: as use cases evolve, so do obligations. The question is less “Does the law apply to me now?” and more “Am I structured to meet the inevitable demands of AI assurance tomorrow?”

Hitting a Moving Target: Testing AI Guardrails in Context

Meta’s new parental controls for teen AI interactions highlight that safety in AI is contextual, not universal. By tailoring safeguards to age and use, Meta reflects a broader shift from static definitions of harm to adaptive guardrails shaped by audience and environment. This raises a key methodological challenge: if safety standards vary by context, testing frameworks must also adapt. Static benchmarks alone cannot capture the nuances of real-world risk.

The Accountability Gap: Who’s Responsible for Responsible AI?

AI is advancing faster than the systems designed to govern it. Responsibility for safety is distributed across labs, deployers, regulators, and the public, yet accountability often belongs to no one. Independent evaluation provides a practical bridge: clear testing, transparent evidence, and a shared standard for trust in AI deployment.