Vinod Kumar Tiwari has helped redefine customer support as a data-driven, predictive, and risk-aware discipline. He has led the design of AI-assisted support architectures that combine telemetry, automation, and human judgment to prevent incidents before they affect customers.
A senior global support and AI-operations leader at cybersecurity firm Qualys, Tiwari recently received two Stevie Awards for Global Customer Service Excellence and Customer Service Automation.
In this interview with Dataconomy, he shares how 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 that improves resilience and operational outcomes at enterprise scale.
Dataconomy: You recently won two Stevie Awards, one for Global Customer Service Excellence and another for Customer Service Automation. What do these awards represent for you?
Vinod Kumar Tiwari: These awards recognize a fundamental shift in how modern support is engineered. They validate my idea that support should be data-driven, AI-assisted, and human-governed, not reactive.
My work behind these awards was focused on combining telemetry, automation, and human judgment to prevent issues, reduce risk, and improve customer outcomes at scale.
For me, they represent recognition that support can be a strategic, innovation-led function, driving reliability, trust, and measurable business impact, not just ticket resolution.
Dataconomy: Your career started in databases and middleware. How did that background shape your approach?
Tiwari: I started as a database administrator after my bachelor’s in computer science, so I worked very closely with data. Later, I worked on middleware products. That gave me a strong foundation in distributed systems, security, and cloud. All of that helps today in cybersecurity, where everything is data-driven.
Dataconomy: You manage teams at a large, global support organization. How do you use data at that scale?
Tiwari: We don’t look at support as volume; we look at patterns. Using Salesforce dashboards, we identified around 200–300 customers who were generating almost 50% of our support tickets. That insight completely changed our strategy.
Dataconomy: What changed after that insight?
Tiwari: Instead of reacting to tickets, we started proactive customer education. We reached out to those customers directly, trained them on how to use the product more effectively, and explained how support teams think. The result was a 40–50% reduction in tickets from that group and 30–40% cost savings. More importantly, retention improved significantly.
Dataconomy: Where does AI fit into this model?
Tiwari: AI is an efficiency multiplier. We use AI to handle calls and chats by reading publicly available documentation and knowledge bases. If AI can’t confidently answer, it immediately routes the case to a human engineer. That’s critical: AI must know when to step back.
Dataconomy: So AI doesn’t replace humans?
Tiwari: No. AI accelerates analysis and execution, but humans are essential for judgment, accountability, and trust. In cybersecurity, many decisions involve context, risk trade-offs, and sensitive data that cannot be delegated to automated systems.
The right approach puts humans in control by design, using AI within clear boundaries and oversight, so speed never comes at the expense of security or accountability.
Dataconomy: You’ve spoken about phishing as a growing risk. What did you observe?
Tiwari: During a recent visit to India, I saw how common phishing has become, including texts, emails, fake OTP requests. Many people can’t distinguish real links from fake ones. Education helps, but it’s not enough. We need AI tools that proactively analyze messages and warn users in real time.
Dataconomy: Do you see risks in relying more on AI in cybersecurity?
Tiwari: Absolutely. The same techniques used to detect threats can be weaponized by attackers to evade controls, automate reconnaissance, and scale exploitation. That asymmetry is unavoidable.
The real risk is not AI itself, but uncontrolled AI. Models that lack governance, continuous validation, or explainability can create false confidence, hide blind spots, and trigger systemic failures at machine speed.
In cybersecurity, AI must be treated as a risk-aware system, not a black box. This means embedding human oversight, adversarial testing, policy-driven thresholds, and auditability into the model lifecycle.
The organizations that succeed will be those that use AI to augment human judgment, while designing defenses that assume AI-powered attackers from day one.
Dataconomy: What has been your biggest personal achievement so far?
Tiwari: Designing and operationalizing AI-driven support architectures that shifted organizations from reactive response to predictive, risk-aware operations.
I introduced system-level frameworks that integrated product telemetry, usage signals, and AI-assisted decision models into daily workflows, allowing teams to detect adoption gaps, capacity risks, and security exposure before they escalated into incidents.
What began as targeted system design evolved into repeatable operating models adopted across globally distributed teams, fundamentally changing how support, onboarding, and security engagement were engineered.
Dataconomy: What advice would you give startups building global, AI-enabled teams?
Tiwari: When building global, AI-enabled teams, startups should design AI as a decision-support layer embedded into operations, not as an autonomous decision engine.
The most effective systems combine telemetry, behavioral signals, and risk-scoring models, while humans remain accountable for what action is taken.
A common failure is attempting to standardize workflows across regions. Scalable organizations standardize data schemas, feature definitions, and model inputs instead, allowing local teams to adapt execution without fragmenting intelligence.
AI delivers the most leverage when it shifts operations from reactive response to predictive detection, surfacing risks and degradation before they become incidents. In security-sensitive environments, risk-weighted, policy-aware, and auditable models are essential.
At scale, this approach has cut reactive incidents by over 30% while improving reliability and customer outcomes. Ultimately, AI scales system design and leadership faster than it scales headcount.
