With the rise of AI-driven interactions, the operationalisation of continuous, contextual identity verification is becoming increasingly relevant. Research by International Data Corporation (IDC), sponsored by Ping Identity, examines how this approach relates to enterprise performance metrics.
According to the study, organisations implementing verified trust report differences in several areas:
- Customer registration conversion: 51% higher compared to peers
- Compliance readiness: 44% higher
- Fraud losses: 43% lower
- Workforce onboarding time: 47% faster
IDC defines verified trust as an approach in which every digital interaction, whether human or AI, is linked to a validated identity. This model involves continuous, contextual verification of authorisation decisions, moving beyond static authentication methods.
The research also identifies a gap between perception and implementation. While 51% of organisations consider themselves leaders in trusted digital identity, only 9% meet IDC’s criteria for verified trust leaders. Differences are evident in several areas:
- Verification coverage: Leaders verify a higher proportion of trust flows, while early-stage adopters verify fewer
- Adoption at scale: Leaders operate verified trust across enterprise environments, while others remain at pilot stages
- Passwordless maturity: Leaders show higher adoption of biometrics and digital wallets, while early adopters remain below 30%
As AI adoption increases, identity verification frameworks are being extended across workforce, customer, partner, and AI identities to support control and accountability across digital interactions.
With regulatory requirements and AI use continuing to evolve, organisations are assessing identity verification models as part of broader approaches to risk management, operational control, and performance.
