Vinod Kumar Tiwari on AI-Driven Cybersecurity Support, and Why Proactive Beats Reactive
Vinod Kumar Tiwari has been instrumental in redefining customer support as a data-driven, predictive, and risk-aware practice. He has designed AI-assisted support architectures that integrate telemetry, automation, and human judgment to preemptively…
Vinod Kumar Tiwari has been instrumental in redefining customer support as a data-driven, predictive, and risk-aware practice. He has designed AI-assisted support architectures that integrate telemetry, automation, and human judgment to preemptively manage incidents.
As a senior leader in global support and AI operations at the cybersecurity firm Qualys , Tiwari has been recognized with two Stevie Awards for Global Customer Service Excellence and Customer Service Automation.
Pattern-based analytics, proactive customer education, and human-in-the-loop AI systems are transforming cybersecurity support from a reactive function into a strategic capability, enhancing resilience and operational outcomes on an enterprise scale.
Instead of focusing on volume, Tiwari’s teams analyze patterns using Salesforce dashboards to identify key customers generating significant support tickets. This insight led to proactive customer education, resulting in a significant reduction in support tickets and cost savings.
Vinod Kumar Tiwari has been instrumental in redefining customer support as a data-driven, predictive, and risk-aware practice.
AI serves as an efficiency multiplier in handling calls and chats. It leverages publicly available documentation and knowledge bases but transfers cases to human engineers when necessary. This approach ensures that AI supports humans without replacing them, maintaining accountability and trust in decision-making.
Tiwari notes the increasing prevalence of phishing and emphasizes the need for AI tools that proactively analyze messages in real-time. He acknowledges the risks of uncontrolled AI, emphasizing the importance of governance, validation, and explainability to prevent systemic failures.
Tiwari advises startups to integrate AI as a decision-support layer rather than an autonomous decision engine. Effective systems combine telemetry, behavior signals, and risk-scoring models, with humans accountable for actions. This approach shifts operations from reactive response to predictive detection, enhancing reliability and customer outcomes.
Based on reporting by TechBullion.