AI Robustness Testing Matters
AI systems must be reliable, testable, and trusted—before they reach production. Regulation, customer expectations, and real-world risk now demand more than accuracy metrics. They demand evidence.
EU AI Act: New Testing Obligations
NIST AI RMF: The Global Benchmark
EU AI Act and NIST RMF align on one message: robustness must be measured and evidenced- not assumed.
Map AI risks
Measure robustness under stress
Manage failures and drift
Govern the AI lifecycle
The NIST AI Risk Management Framework is becoming the de-facto standard for trustworthy AI. Organisations must:
The EU AI Act transforms robustness from an optional practice into a regulatory requirements Across 2025–2027, organisations deploying AI must demonstrate:
Documented robustness testing
Resilience to foreseeable perturbations
Governance and risk controls
Traceable evidence and audit-ready reports
High-risk AI systems face strict testing expectations, with enforcement starting as early as 2025.
Small changes in brightness, blur, noise, or context can cause unexpected failures—even in high-accuracy models.
AI Models Are Fragile
Stakeholders Expect Proof
Boards, auditors, regulators, insurers, and enterprise buyers no longer accept claims of performance. They expect:
Robustness scores
Failure-mode analysis
Testing methodology
Exportable, auditable reports
Robustness evidence is becoming part of tech due diligence
Without systematic testing, these weaknesses stay hidden until they cause real incidents.
The VeriForj Solution
VeriForj makes robustness evaluation practical, repeatable, and audit-ready. With VeriForj, you can:
Run exploratory robustness tests across controlled perturbations
Connect via remote inference (REST / KServe / Triton)
Generate governance-ready artefacts aligned with EU AI Act and NIST RMF
Strengthen models via a closed loop: Verify → Generate → Re-verify